gistfile1.txt

{
 "metadata": {
  "name": "Triplet  Cluster-assisted triplet convergence"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Cluster-assisted triplet convergence"
     ]
    },
    {
     "cell_type": "markdown",
     "source": [
      "Idea: Avoid asking Cake-Cake-Broccoli triplets (insulting!) and\n",
      "Burrito-Cake-Broccoli triplets (confusing!) by using human-powered\n",
      "clusters to generate those constraints automatically instead of asking\n",
      "the crowd.\n",
      "\n",
      "Algorithm:\n",
      "- Consider a universe of points\n",
      "- (Optional) Use random triplets between these points to construct\n",
      "  an initial embedding\n",
      "- Use the embedding-so-far to select k-medoids of the points\n",
      "  within this cluster\n",
      "- Use human-powered clustering to pick a (human-powered) cluster label\n",
      "  for all points within our universe\n",
      "  - Note that the human-powered clusters approximate human distances\n",
      "- For each S in list of clusters:\n",
      "    - For all point pairs A,B in S:\n",
      "      - For each point C not in cluster S:\n",
      "          - Add triplet (A,B,C) to our current triplet list\n",
      "- Generate the new embedding using these recovered points\n",
      "- Either iterate with the same universe (which adjusts the clusters)\n",
      "  or recurse for each cluster being the new universe\n",
      "  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%cd ~/batchembedding\n",
      "import cities_example\n",
      "import pyximport; pyximport.install()\n",
      "import cities_example_accelerated\n",
      "import tste\n",
      "import random\n",
      "from sklearn import cluster"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/store/home/michael/batchembedding\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/python2.7/site-packages/scipy/lib/_util.py:34: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
        "  DeprecationWarning)\n",
        "/usr/lib/python2.7/site-packages/scipy/lib/_util.py:34: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
        "  DeprecationWarning)\n",
        "/usr/lib/python2.7/site-packages/scipy/lib/_util.py:34: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
        "  DeprecationWarning)\n",
        "/usr/lib/python2.7/site-packages/scipy/lib/_util.py:34: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
        "  DeprecationWarning)\n",
        "/usr/lib/python2.7/site-packages/scipy/lib/_util.py:34: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
        "  DeprecationWarning)\n",
        "/usr/lib/python2.7/site-packages/scipy/lib/_util.py:34: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
        "  DeprecationWarning)\n",
        "/usr/lib/python2.7/site-packages/scipy/lib/_util.py:34: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
        "  DeprecationWarning)\n",
        "/usr/lib/python2.7/site-packages/scipy/lib/_util.py:34: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
        "  DeprecationWarning)\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cities = cities_example.load_cities()\n",
      "K = cities_example.K\n",
      "\n",
      "\n",
      "fig,ax = subplots(figsize=(5,8))\n",
      "ax.set_xlim(-126,-112)\n",
      "ax.set_ylim(31,43)\n",
      "ax.scatter(cities[:,0],cities[:,1],s=0.5)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.collections.PathCollection at 0x5cad110>"
       ]
      },
      {
       "output_type": "display_data",
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IkZMVHg8PD6fTp0+XK/aKFT9TcHAwERGFhISUuAmCn58f1a1rQgcPHix07P79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\nEWSMaTUugowxrfZ/c01WDf8vC38AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "triplets = cities_example_accelerated.random_triplet_subset(K, 2, len(K))\n",
      "triplet_cities = tste.tste(triplets.astype('i32'), 2, 0, 1, 1,verbose=True)\n"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Iteration  10  error is  628.25369507 , number of constraints:  0.0270727580372\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20  error is  494.981134337 , number of constraints:  0.0139593908629\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30  error is  449.473241871 , number of constraints:  0.00972927241963\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40  error is  424.834009933 , number of constraints:  0.00972927241963\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50  error is  407.83835668 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 60  error is  394.55197867 , number of constraints:  0.00846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 70  error is  383.231596222 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 80  error is  373.474472784 , number of constraints:  0.00972927241963\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90  error is  364.872240964 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100  error is  357.54991865 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 110  error is  351.013906388 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 120  error is  344.559689989 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 130  error is  338.892812333 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 140  error is  331.670648386 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 150  error is  329.104169464 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 160  error is  326.697617108 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 170  error is  324.342003651 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 180  error is  322.019948125 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 190  error is  319.721112644 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200  error is  317.439335448 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 210  error is  315.174544817 , number of constraints:  0.00846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 220  error is  312.950621817 , number of constraints:  0.00761421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 230  error is  311.816497902 , number of constraints:  0.00761421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 240  error is  311.214077513 , number of constraints:  0.00761421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 250  error is  310.571909378 , number of constraints:  0.00761421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 260  error is  309.881583881 , number of constraints:  0.00761421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 270  error is  309.139078934 , number of constraints:  0.00761421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 280  error is  308.340041428 , number of constraints:  0.00761421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 290  error is  307.479619946 , number of constraints:  0.0080372250423\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 300  error is  306.553134742 , number of constraints:  0.0080372250423\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 310  error is  305.556971622 , number of constraints:  0.0080372250423\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 320  error is  304.488324778 , number of constraints:  0.0080372250423\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 330  error is  303.343232168 , number of constraints:  0.00846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 340  error is  302.113822274 , number of constraints:  0.00846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 350  error is  300.790723798 , number of constraints:  0.00846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 360  error is  299.382068923 , number of constraints:  0.00846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 370  error is  297.902216478 , number of constraints:  0.00846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 380  error is  296.375903776 , number of constraints:  0.00846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 390  error is  294.875267581 , number of constraints:  0.00846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400  error is  293.765318564 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 410  error is  293.30048879 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 420  error is  292.882393807 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 430  error is  292.436841978 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 440  error is  291.960907754 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 450  error is  291.454035571 , number of constraints:  0.00888324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 460  error is  290.916439486 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 470  error is  290.348804723 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 480  error is  289.751982831 , number of constraints:  0.00972927241963\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 490  error is  289.126606382 , number of constraints:  0.00972927241963\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 500  error is  288.472777028 , number of constraints:  0.00972927241963\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 510  error is  287.78996654 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 520  error is  287.077094583 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 530  error is  286.332630324 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 540  error is  285.554621187 , number of constraints:  0.0093062605753\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 550  error is  284.740677211 , number of constraints:  0.00972927241963\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 560  error is  283.888008043 , number of constraints:  0.00972927241963\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 570  error is  283.351517962 , number of constraints:  0.010152284264\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 580  error is  283.076859687 , number of constraints:  0.010152284264\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 590  error is  282.811580654 , number of constraints:  0.010152284264\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600  error is  282.525264957 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 610  error is  282.211315078 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 620  error is  281.864237045 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 630  error is  281.479376921 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 640  error is  281.060958977 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 650  error is  280.616887225 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 660  error is  280.146167803 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 670  error is  279.646816036 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 680  error is  279.1181691 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 690  error is  278.559030992 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 700  error is  277.967637333 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 710  error is  277.346315157 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720  error is  277.021694886 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 730  error is  276.835269413 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 740  error is  276.637544166 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 750  error is  276.422259545 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 760  error is  276.186875064 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 770  error is  275.929510275 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 780  error is  275.649399557 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 790  error is  275.348593633 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800  error is  275.031433147 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 810  error is  274.698901035 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 820  error is  274.34794569 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 830  error is  273.97507008 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 840  error is  273.57719514 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 850  error is  273.151222024 , number of constraints:  0.011421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 860  error is  272.693557295 , number of constraints:  0.011421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 870  error is  272.378667984 , number of constraints:  0.011421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 880  error is  272.244030474 , number of constraints:  0.011421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 890  error is  272.097694337 , number of constraints:  0.011421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 900  error is  271.936836697 , number of constraints:  0.011421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 910  error is  271.759870541 , number of constraints:  0.011421319797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 920  error is  271.565061927 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 930  error is  271.350430678 , number of constraints:  0.0105752961083\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 940  error is  271.113619205 , number of constraints:  0.010152284264\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 950  error is  270.851617894 , number of constraints:  0.010152284264\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 960  error is  270.560253769 , number of constraints:  0.010152284264\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 970  error is  270.233459642 , number of constraints:  0.010152284264\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 980  error is  269.863059894 , number of constraints:  0.010152284264\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 990  error is  269.44408561 , number of constraints:  0.0109983079526\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1000  error is  269.001929395 , number of constraints:  0.011421319797\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:2: DeprecationWarning: Specified size is invalid for this data type.\n",
        "Size will be ignored in NumPy 1.7 but may throw an exception in future versions.\n"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(*triplet_cities.T, lw=0, s=2)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "<matplotlib.collections.PathCollection at 0x9a12e50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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SwVhRZhqNbtJVRgYvNTB0KNGoUfxnr148I9TFhWj5cn7fjz9yN4o2gQ8Zokvo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\n"
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def find_medoids(triplet_cities, n_clusters=8):\n",
      "    k = cluster.KMeans(n_clusters=n_clusters).fit(triplet_cities)\n",
      "    # Find original points within triplet_cities that are closest to the\n",
      "    # cluster centers\n",
      "    labels = find_cluster_labels(triplet_cities, k.cluster_centers_)\n",
      "    return labels\n",
      "\n",
      "def find_cluster_labels(cluster_centers, cities):\n",
      "    results = []\n",
      "    for point in cities:\n",
      "        # Find the smallest cluster center wrt this point\n",
      "        results.append(np.argmin([np.linalg.norm(cluster_center-point)\n",
      "                              for cluster_center in cluster_centers]))\n",
      "    return np.array(results)\n"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "medoids = find_medoids(triplet_cities)\n",
      "\n",
      "human_labels = find_cluster_labels(cities[medoids], cities)\n",
      "prevembedding_labels = find_cluster_labels(triplet_cities[medoids], triplet_cities)\n",
      "\n",
      "fig, (ax1,ax2,ax3) = subplots(1,3, figsize=(12,6))\n",
      "ax2.set_ylim(31,43); ax2.set_xlim(-126,-112)\n",
      "for a in [ax1,ax2,ax3]: a.set_xticklabels([]); a.set_yticklabels([])\n",
      "remapped = cities_example.remap(triplet_cities, cities)\n",
      "ax1.scatter(*remapped.T, c=prevembedding_labels, lw=0, s=2)\n",
      "ax1.scatter(*remapped[medoids].T, c='red', lw=0, s=20)\n",
      "ax1.set_title('Medoids in current embedding')\n",
      "ax2.scatter(*cities.T, c=human_labels, lw=0, s=2)\n",
      "ax2.scatter(*cities[medoids].T, c='red', lw=0, s=20)\n",
      "ax2.set_title('Human-powered kNN')\n",
      "ax3.scatter(*remapped.T, c=human_labels, lw=0, s=2)\n",
      "ax3.set_title('Clusters to be repaired')"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 151,
       "text": [
        "<matplotlib.text.Text at 0x1291da10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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wO7heR976o6KiWLNmjdX6u3fvJj4+nri4OLy9vS3vEVi3G1uSkpL44osveO211/D09LS5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hD/LUzVYkVU8BzrKUTL70jwio2AEclQ6ILmztvfMQYyfwZFzZhp4PM068avn5RUzgUbExPBe+9d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\nKWDq3vo4XAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def create_cluster_triplets(labels, inner_count):\n",
      "    n_points = len(labels)\n",
      "    n_clusters = len(set(labels))\n",
      "    assert set(labels) == set(xrange(n_clusters)), \"Uh oh, some clusters are missing\"\n",
      "    results = []\n",
      "    indices = np.arange(n_points)\n",
      "\n",
      "    for cluster in xrange(n_points):\n",
      "        points_in_cluster = indices[labels==cluster]\n",
      "        points_out_of_cluster = indices[labels!=cluster]\n",
      "\n",
      "        for a in points_in_cluster:\n",
      "            for i in xrange(inner_count):\n",
      "                b = points_in_cluster[np.random.randint(len(points_in_cluster))]\n",
      "                c = points_out_of_cluster[np.random.randint(len(points_out_of_cluster))]\n",
      "                if a!=b and b!=c and a!=c:\n",
      "                    results.append( (a,b,c) )\n",
      "    return np.array(results)"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bonus_triplets = create_cluster_triplets(human_labels, 5) \n",
      "next_triplets = np.vstack([triplets, bonus_triplets])\n",
      "triplet_cities_next = tste.tste(next_triplets.astype('i32'), 2, 0, 1, 1,verbose=True)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Iteration  10  error is  2203.43056861 , number of constraints:  0.0543200874954\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20  error is  1317.78560368 , number of constraints:  0.0269777615749\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30  error is  1126.70153094 , number of constraints:  0.0218738607364\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40  error is  1037.36752284 , number of constraints:  0.0193219103172\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50  error is  981.672002304 , number of constraints:  0.0193219103172\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 60  error is  941.046415364 , number of constraints:  0.018592781626\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 70  error is  908.948166615 , number of constraints:  0.0178636529347\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 80  error is  882.187651087 , number of constraints:  0.0172560456921\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90  error is  858.505536051 , number of constraints:  0.0162838741038\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100  error is  836.030874558 , number of constraints:  0.0157977883096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 110  error is  817.011423118 , number of constraints:  0.0153117025155\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 120  error is  799.062254127 , number of constraints:  0.0142180094787\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 130  error is  782.991955756 , number of constraints:  0.0142180094787\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 140  error is  772.161877915 , number of constraints:  0.0140964880301\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 150  error is  759.644539341 , number of constraints:  0.013610402236\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 160  error is  750.301328492 , number of constraints:  0.0138534451331\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 170  error is  738.526363834 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 180  error is  733.404120061 , number of constraints:  0.0128812735448\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 190  error is  728.433085783 , number of constraints:  0.0128812735448\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200  error is  723.359551414 , number of constraints:  0.0128812735448\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 210  error is  718.536071277 , number of constraints:  0.0126382306477\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 220  error is  714.325436884 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 230  error is  709.365161835 , number of constraints:  0.0132458378904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 240  error is  704.84630215 , number of constraints:  0.0132458378904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 250  error is  699.583276999 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 260  error is  697.096027427 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 270  error is  694.604979223 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 280  error is  692.069245542 , number of constraints:  0.0128812735448\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 290  error is  689.485806176 , number of constraints:  0.0128812735448\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 300  error is  686.862905087 , number of constraints:  0.0128812735448\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 310  error is  685.12947858 , number of constraints:  0.0132458378904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 320  error is  684.407119852 , number of constraints:  0.0132458378904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 330  error is  683.640393415 , number of constraints:  0.0132458378904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 340  error is  682.814500544 , number of constraints:  0.0132458378904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 350  error is  681.926175058 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 360  error is  680.972751142 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 370  error is  679.951887162 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 380  error is  678.861544497 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 390  error is  677.699989898 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400  error is  676.465817109 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 410  error is  675.157996636 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 420  error is  673.775947891 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 430  error is  672.31961547 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 440  error is  670.789532215 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 450  error is  669.186861137 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 460  error is  667.513417144 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 470  error is  665.771673574 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 480  error is  664.522730008 , number of constraints:  0.0132458378904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 490  error is  663.318691406 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 500  error is  662.805541283 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 510  error is  662.256594246 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 520  error is  661.663412502 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 530  error is  661.023032475 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 540  error is  660.332899243 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 550  error is  659.590638876 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 560  error is  658.794052948 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 570  error is  657.941153602 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 580  error is  657.030210862 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 590  error is  656.059804607 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600  error is  655.028877831 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 610  error is  653.936788702 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 620  error is  652.783359161 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 630  error is  651.682948458 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 640  error is  651.04803471 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 650  error is  650.688607254 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 660  error is  650.312968623 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 670  error is  649.905969311 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 680  error is  649.464649252 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 690  error is  648.986639815 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 700  error is  648.469701494 , number of constraints:  0.0130027949933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 710  error is  647.911665759 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720  error is  647.310444039 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 730  error is  646.66405638 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 740  error is  645.970668612 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 750  error is  645.228634185 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 760  error is  644.436538579 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 770  error is  643.59324453 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 780  error is  643.505512144 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 790  error is  642.43917606 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800  error is  642.181264426 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 810  error is  641.900373649 , number of constraints:  0.0132458378904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 820  error is  641.594366237 , number of constraints:  0.0133673593389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 830  error is  641.261380628 , number of constraints:  0.0133673593389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 840  error is  640.899527603 , number of constraints:  0.0133673593389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 850  error is  640.506886231 , number of constraints:  0.0133673593389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 860  error is  640.081517994 , number of constraints:  0.0133673593389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 870  error is  639.621486701 , number of constraints:  0.0133673593389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 880  error is  639.124882102 , number of constraints:  0.0133673593389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 890  error is  638.589846241 , number of constraints:  0.0133673593389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 900  error is  638.014601298 , number of constraints:  0.0132458378904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 910  error is  637.397476716 , number of constraints:  0.0132458378904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 920  error is  636.736931277 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 930  error is  636.960450476 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 940  error is  635.827082157 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 950  error is  635.62241712 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 960  error is  635.399342292 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 970  error is  635.155831274 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 980  error is  634.890224741 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 990  error is  634.600811695 , number of constraints:  0.0131243164419\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1000  error is  634.285800395 , number of constraints:  0.0131243164419\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:3: DeprecationWarning: Specified size is invalid for this data type.\n",
        "Size will be ignored in NumPy 1.7 but may throw an exception in future versions.\n"
       ]
      }
     ],
     "prompt_number": 226
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "fig, (ax1,ax2,ax3,ax4) = subplots(1,4, figsize=(16,6))\n",
      "ax2.set_ylim(31,43); ax2.set_xlim(-126,-112)\n",
      "for a in [ax1,ax2,ax3,ax4]: a.set_xticklabels([]); a.set_yticklabels([])\n",
      "remapped = cities_example.remap(triplet_cities, cities)\n",
      "ax1.scatter(*remapped.T, lw=0, s=2) #, c=prevembedding_labels)\n",
      "ax1.scatter(*remapped[medoids].T, c='red', lw=0, s=20)\n",
      "ax1.set_title('Medoids in current embedding')\n",
      "ax2.scatter(*cities.T, c=human_labels, lw=0, s=2, cmap=pylab.cm.Dark2)\n",
      "ax2.scatter(*cities[medoids].T, c='red', lw=0, s=20)\n",
      "ax2.text(0.5,0.05,'(Human Perception)', horizontalalignment='center', fontsize=18, transform=ax2.transAxes)\n",
      "ax2.set_title('Human-powered kNN')\n",
      "ax2.set_axis_bgcolor('#ffeeee')\n",
      "ax3.scatter(*remapped.T, c=human_labels, lw=0, s=1, cmap=pylab.cm.Dark2)\n",
      "ax3.set_title('Clusters to be repaired')\n",
      "\n",
      "remapped_next = cities_example.remap(triplet_cities_next, cities)\n",
      "ax4.scatter(*remapped_next.T, lw=0, s=2, c=human_labels, cmap=pylab.cm.Dark2)\n",
      "ax4.set_title('New embedding with cluster-assisted triplets')\n",
      "fig.tight_layout()\n",
      "fig.savefig(\"/tmp/embedding.png\", dpi=300)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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LY8tmz54NlUqF8+fPY82aNUaT8WXFamzfKvMdB9hwMiY8PBzt27fX/794A/z0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\nfgJVNasatm6FgQNBp6u6jXD+eMr72pKXKdkyjuMwpqEJiCb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VB2IKC2HVqvo949ZbRSBG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\nREREREREgkjJGBERERERERGRIPp/LEAC3MnjpkMAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 227
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "recursed_triplets = []\n",
      "for cluster in xrange(len(set(human_labels))):\n",
      "    next_points = np.arange(len(cities))[human_labels==cluster]\n",
      "    subsetK = K[next_points,:][:,next_points]\n",
      "    subsetTriplets = cities_example_accelerated.random_triplet_subset(subsetK, 2, len(subsetK))\n",
      "    recursed_triplets.append(next_points[subsetTriplets])\n",
      "\n",
      "nextrecursed_triplets = np.vstack([triplets, bonus_triplets]+recursed_triplets)\n",
      "triplet_cities_nextrecursed = tste.tste(nextrecursed_triplets.astype('i32'), 2, 0, 1, 1,verbose=True)\n"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Iteration  10  error is  4613.74043473 , number of constraints:  0.150948739734\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20  error is  3059.58452834 , number of constraints:  0.0823185122251\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30  error is  2614.88471313 , number of constraints:  0.0623052959502\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40  error is  2357.86033788 , number of constraints:  0.0484282073067\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50  error is  2185.58298678 , number of constraints:  0.0430472953837\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 60  error is  2064.8319992 , number of constraints:  0.0376663834608\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 70  error is  1979.36047452 , number of constraints:  0.0350231284811\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 80  error is  1922.34995123 , number of constraints:  0.0347399225904\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90  error is  1881.7842506 , number of constraints:  0.0357783441896\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100  error is  1850.56177266 , number of constraints:  0.034173510809\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 110  error is  1824.22502178 , number of constraints:  0.0346455206268\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 120  error is  1804.28865373 , number of constraints:  0.0351175304446\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 130  error is  1789.6308236 , number of constraints:  0.0364391579345\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 140  error is  1679.93761807 , number of constraints:  0.0283205890683\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 150  error is  1664.2835413 , number of constraints:  0.0271877655055\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 160  error is  1649.6117747 , number of constraints:  0.0269045596148\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 170  error is  1635.58210027 , number of constraints:  0.026432549797\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 180  error is  1621.38985946 , number of constraints:  0.0257717360521\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 190  error is  1609.96489643 , number of constraints:  0.025582932125\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200  error is  1606.20989749 , number of constraints:  0.0253941281979\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 210  error is  1602.21114273 , number of constraints:  0.0252997262343\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 220  error is  1597.92107392 , number of constraints:  0.025582932125\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 230  error is  1593.3170281 , number of constraints:  0.025582932125\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 240  error is  1588.3715888 , number of constraints:  0.0251109223072\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 250  error is  1583.05104802 , number of constraints:  0.0249221183801\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 260  error is  1577.31088325 , number of constraints:  0.0245445105258\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 270  error is  1571.08284851 , number of constraints:  0.0248277164165\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 280  error is  1564.27506054 , number of constraints:  0.0244501085623\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 290  error is  1556.94609519 , number of constraints:  0.0246389124894\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 300  error is  1549.25830901 , number of constraints:  0.0243557065987\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 310  error is  1541.24668882 , number of constraints:  0.0239780987445\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 320  error is  1532.81525133 , number of constraints:  0.0239780987445\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 330  error is  1523.82153679 , number of constraints:  0.0236004908902\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 340  error is  1514.39262046 , number of constraints:  0.0228452751817\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 350  error is  1505.20043208 , number of constraints:  0.0227508732182\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 360  error is  1496.15101 , number of constraints:  0.022562069291\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 370  error is  1489.40725321 , number of constraints:  0.0223732653639\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 380  error is  1486.83363536 , number of constraints:  0.0222788634004\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 390  error is  1484.09650833 , number of constraints:  0.0223732653639\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400  error is  1481.130239 , number of constraints:  0.0223732653639\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 410  error is  1477.97334043 , number of constraints:  0.0224676673275\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 420  error is  1474.62742998 , number of constraints:  0.022562069291\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 430  error is  1471.08426094 , number of constraints:  0.022562069291\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 440  error is  1467.33456404 , number of constraints:  0.0223732653639\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 450  error is  1463.3562311 , number of constraints:  0.0223732653639\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 460  error is  1459.03501665 , number of constraints:  0.0223732653639\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 470  error is  1454.39835226 , number of constraints:  0.0220900594732\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 480  error is  1449.614795 , number of constraints:  0.0219956575097\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 490  error is  1444.55073859 , number of constraints:  0.0215236476919\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 500  error is  1441.44441629 , number of constraints:  0.0215236476919\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 510  error is  1437.20626098 , number of constraints:  0.0210516378741\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 520  error is  1435.62998413 , number of constraints:  0.0209572359105\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 530  error is  1433.91428734 , number of constraints:  0.0209572359105\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 540  error is  1432.03168389 , number of constraints:  0.0210516378741\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 550  error is  1429.97522241 , number of constraints:  0.0208628339469\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 560  error is  1427.7426941 , number of constraints:  0.0207684319834\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 570  error is  1425.32194974 , number of constraints:  0.0205796280563\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 580  error is  1422.69022153 , number of constraints:  0.0206740300198\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 590  error is  1419.82194946 , number of constraints:  0.0206740300198\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600  error is  1416.69416514 , number of constraints:  0.0210516378741\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 610  error is  1413.26465519 , number of constraints:  0.021712451619\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 620  error is  1409.17897437 , number of constraints:  0.0219012555461\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 630  error is  1404.90507799 , number of constraints:  0.0218068535826\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 640  error is  1400.79810743 , number of constraints:  0.0219012555461\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 650  error is  1396.58436165 , number of constraints:  0.0219956575097\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 660  error is  1394.36434825 , number of constraints:  0.0220900594732\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 670  error is  1393.16643255 , number of constraints:  0.0218068535826\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 680  error is  1391.89008752 , number of constraints:  0.0218068535826\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 690  error is  1390.52489255 , number of constraints:  0.0218068535826\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 700  error is  1389.06075794 , number of constraints:  0.021712451619\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 710  error is  1387.48672347 , number of constraints:  0.0215236476919\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720  error is  1385.79214021 , number of constraints:  0.0214292457283\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 730  error is  1383.96619894 , number of constraints:  0.0214292457283\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 740  error is  1381.99749858 , number of constraints:  0.0213348437648\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 750  error is  1379.87455455 , number of constraints:  0.0213348437648\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 760  error is  1377.58856055 , number of constraints:  0.0214292457283\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 770  error is  1375.13745839 , number of constraints:  0.0211460398376\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 780  error is  1372.52132703 , number of constraints:  0.0211460398376\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 790  error is  1369.73382428 , number of constraints:  0.0209572359105\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800  error is  1367.44830432 , number of constraints:  0.0208628339469\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 810  error is  1366.13530483 , number of constraints:  0.0208628339469\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 820  error is  1365.27451875 , number of constraints:  0.0207684319834\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 830  error is  1364.33564029 , number of constraints:  0.0207684319834\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 840  error is  1363.31117102 , number of constraints:  0.0207684319834\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 850  error is  1362.19425644 , number of constraints:  0.0207684319834\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 860  error is  1360.97785072 , number of constraints:  0.0206740300198\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 870  error is  1359.65461577 , number of constraints:  0.0206740300198\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 880  error is  1358.21687211 , number of constraints:  0.0206740300198\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 890  error is  1356.65649331 , number of constraints:  0.0207684319834\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 900  error is  1354.96466365 , number of constraints:  0.0207684319834\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 910  error is  1353.13132962 , number of constraints:  0.0207684319834\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 920  error is  1351.14385611 , number of constraints:  0.0208628339469\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 930  error is  1348.98345543 , number of constraints:  0.0206740300198\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 940  error is  1346.61708939 , number of constraints:  0.0206740300198\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 950  error is  1344.43369171 , number of constraints:  0.0204852260927\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 960  error is  1343.64546449 , number of constraints:  0.0204852260927\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 970  error is  1342.86971245 , number of constraints:  0.0203908241291\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 980  error is  1342.02742465 , number of constraints:  0.0204852260927\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 990  error is  1341.11740502 , number of constraints:  0.020202020202\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1000  error is  1340.14019868 , number of constraints:  0.0201076182385\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:9: DeprecationWarning: Specified size is invalid for this data type.\n",
        "Size will be ignored in NumPy 1.7 but may throw an exception in future versions.\n"
       ]
      }
     ],
     "prompt_number": 278
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, (ax1,ax2,ax3,ax4,ax5) = subplots(1,5, figsize=(20,6))\n",
      "ax2.set_ylim(31,43); ax2.set_xlim(-126,-112)\n",
      "for a in [ax1,ax2,ax3,ax4,ax5]: a.set_xticklabels([]); a.set_yticklabels([])\n",
      "remapped = cities_example.remap(triplet_cities, cities)\n",
      "ax1.scatter(*remapped.T, lw=0, s=2) #, c=prevembedding_labels)\n",
      "ax1.scatter(*remapped[medoids].T, c='red', lw=0, s=20)\n",
      "ax1.set_title('Medoids in current embedding')\n",
      "ax2.scatter(*cities.T, c=human_labels, lw=0, s=2, cmap=pylab.cm.Dark2)\n",
      "ax2.scatter(*cities[medoids].T, c='red', lw=0, s=20)\n",
      "ax2.text(0.5,0.05,'(Human Perception)', horizontalalignment='center', fontsize=18, transform=ax2.transAxes)\n",
      "ax2.set_title('Human-powered kNN')\n",
      "ax2.set_axis_bgcolor('#ffeeee')\n",
      "ax3.scatter(*remapped.T, c=human_labels, lw=0, s=1, cmap=pylab.cm.Dark2)\n",
      "ax3.set_title('Clusters to be repaired')\n",
      "\n",
      "remapped_next = cities_example.remap(triplet_cities_next, cities)\n",
      "ax4.scatter(*remapped_next.T, lw=0, s=2, c=human_labels, cmap=pylab.cm.Dark2)\n",
      "ax4.set_title('New embedding with cluster-assisted triplets')\n",
      "\n",
      "remapped_nextrecursed = cities_example.remap(triplet_cities_nextrecursed, cities)\n",
      "ax5.scatter(*remapped_nextrecursed.T, lw=0, s=2, c=human_labels, cmap=pylab.cm.Dark2)\n",
      "ax5.set_title('Recurse into each cluster')\n",
      "fig.tight_layout()\n",
      "fig.savefig(\"/tmp/embedding.png\", dpi=300)\n"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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MnToVd+/eRVpaGvr27WsYHywd78pjaTkeHh5lvh99+vTBjh07kJSUhGbNmmH8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D/GJ06Ip1H/vC5gd1Vj374RbXCLyi720kJMB//Es+vNAJSd2EHOomkE3fg+CqWSwk2zCbr+zWrD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\n85snohcsUP87f/7RYy6XWlbjkkvUfpqw29VizYsWQ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3NdnQ5wluzH63FiShmK4X8JGK9X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toHTtHwmF9v6DyFO2gMLFJ1Gz5z1KVl5Ide6j+xy3cssD1OS91tSn4IjYtelrtq/7PNxhiDQb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\nTY/BiPjfX5khb4BQIIg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AhWefTedBg3jy+efrNZhnzZzZYBmJKy66iKHjx/Pvp5/myYcf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\nMwAAAAAAAADAkP8A1AJcPQ1c6zMAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 279
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Iterative algorithm"
     ]
    },
    {
     "cell_type": "markdown",
     "source": [
      "An iterative version: Adjust clusters, don't recurse yet"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def clusterassist_iter(actual_cities, current_embedding, n_clusters=8):\n",
      "    # Returns some new triplets to add and some point labels.\n",
      "    # current_embedding = tste.tste(current_triplets.astype('i32'), 2, 0, 1, 1,verbose=True)\n",
      "    medoids = find_medoids(current_embedding, n_clusters=n_clusters)\n",
      "    human_labels = find_cluster_labels(actual_cities[medoids], actual_cities)\n",
      "    bonus_triplets = create_cluster_triplets(human_labels, 5) \n",
      "    return bonus_triplets, human_labels, medoids"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "humantriplets = [cities_example_accelerated.random_triplet_subset(K, 2, len(K))]\n",
      "embeddinglabels = [ np.zeros(len(K)) ]\n",
      "embedding1 = tste.tste(humantriplets[0].astype('i4'), 2, 0, 1, 1, verbose=False)\n",
      "embeddings = [embedding1]\n",
      "bonustriplets = []\n",
      "\n",
      "for i in xrange(10):\n",
      "    print \"Embedding\", i\n",
      "    btrips,labels, medoids = clusterassist_iter(cities, embeddings[-1], i+ 20)\n",
      "    embeddinglabels.append(labels)\n",
      "    # Bonus round\n",
      "    bonustriplets.append(btrips)\n",
      "    recurse_triplets = []\n",
      "\n",
      "    # # Ask the humans for better triplets involving *only* the cluster centers\n",
      "    # subsetK = K[medoids,:][:,medoids]\n",
      "    # subsetTriplets = cities_example_accelerated.random_triplet_subset(subsetK, 10, len(subsetK))\n",
      "    # recurse_triplets.append( medoids[subsetTriplets] )\n",
      "\n",
      "    # Just ask for more random triplets\n",
      "    humantriplets.append(cities_example_accelerated.random_triplet_subset(K, 2, len(K)))\n",
      "\n",
      "    # Ask the oracle for more human triplets within each cluster\n",
      "    # TODO this doesn't help, not enough global structure!\n",
      "    for ccluster in xrange(len(set(labels))):\n",
      "        next_points = np.arange(len(cities))[labels==ccluster]\n",
      "        subsetK = K[next_points,:][:,next_points]\n",
      "        subsetTriplets = cities_example_accelerated.random_triplet_subset(subsetK, 1, len(subsetK))\n",
      "        recurse_triplets.append(next_points[subsetTriplets])\n",
      "\n",
      "    # Next embedding\n",
      "    humantriplets.append(np.vstack(recurse_triplets))\n",
      "    next_embedding = tste.tste(np.vstack(humantriplets+bonustriplets).astype('i4'), 2, 0, 1, 1, verbose=False)\n",
      "    # I should try the next embedding using the previous embedding as a starting point!\n",
      "    embeddings.append(next_embedding)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Embedding 0\n",
        "Embedding"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1\n",
        "Embedding"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2\n",
        "Embedding"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3\n",
        "Embedding"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-37-1d2ce57d2066>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     31\u001b[0m     \u001b[1;31m# Next embedding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m     \u001b[0mhumantriplets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecurse_triplets\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m     \u001b[0mnext_embedding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtste\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtste\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhumantriplets\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbonustriplets\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'i4'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m     \u001b[1;31m# I should try the next embedding using the previous embedding as a starting point!\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m     \u001b[0membeddings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnext_embedding\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/store/home/michael/batchembedding/tste.pyc\u001b[0m in \u001b[0;36mtste\u001b[1;34m(triplets, no_dims, lamb, alpha, use_log, verbose, max_iter, save_each_iteration, initial_X)\u001b[0m\n\u001b[0;32m     87\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mUSING_THEANO\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0muse_log\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m                 \u001b[0mC\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mG\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtste_grad_theano_log\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mno_dims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtriplets\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlamb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     90\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m                 \u001b[0mC\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mG\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtste_grad_theano\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mno_dims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtriplets\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlamb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    578\u001b[0m         \u001b[0mt0_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    579\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 580\u001b[1;33m             \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    581\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    582\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'position_of_error'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36mrval\u001b[1;34m(p, i, o, n)\u001b[0m\n\u001b[0;32m    603\u001b[0m         \u001b[1;31m# default arguments are stored in the closure of `rval`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    604\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 605\u001b[1;33m         \u001b[1;32mdef\u001b[0m \u001b[0mrval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_input_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode_output_storage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    606\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    607\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = subplots(1,1+len(embeddings), figsize=(4+4*len(embeddings),6))\n",
      "for a in axes: a.set_xticklabels([]); a.set_yticklabels([]); a.set_ylim(31,43); a.set_xlim(-126,-112)\n",
      "ntriplets=0\n",
      "for i,(a,embedding,lbls, ht) in enumerate(zip(axes, embeddings, embeddinglabels, humantriplets)):\n",
      "    a.set_xticklabels([]); a.set_yticklabels([]); a.set_ylim(31,43); a.set_xlim(-126,-112)\n",
      "    remapped = cities_example.remap_homography(embedding, cities)\n",
      "    a.scatter(*remapped.T, lw=0, s=3, cmap=pylab.cm.Dark2, c=lbls, zorder=15000)\n",
      "    # Draw correspondence lines\n",
      "    fraction_of_lines=1\n",
      "    which_lines = random.sample(xrange(len(remapped)), int(fraction_of_lines*len(remapped)))\n",
      "    for ((x1,y1),(x2,y2)) in zip(remapped[which_lines], cities[which_lines]):\n",
      "        a.plot([x1, x2], [y1, y2], 'black',alpha=1, lw=0.1, zorder=1)\n",
      "\n",
      "    ntriplets += len(ht)\n",
      "    a.set_title(\"%d human-triplets + %d bonus\"%(ntriplets,i))\n",
      "    # fig.tight_layout(); fig.savefig(\"/tmp/convergence%d.png\"%i,dpi=100)\n",
      "# fig,a = subplots(1,1, figsize=(4,6))\n",
      "a = axes[-1]\n",
      "a.set_xticklabels([]); a.set_yticklabels([]); a.set_ylim(31,43); a.set_xlim(-126,-112)\n",
      "a.scatter(*cities.T, c=embeddinglabels[-1], lw=0, s=2, cmap=pylab.cm.Dark2)\n",
      "a.set_title('Perfect')\n",
      "a.set_axis_bgcolor('#ffeeee') "
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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qlCQlhBBCCCGEEEIIIYSMapQkJYQQQgghhBBCCCGEjGqUJCWEEEIIIYQQQggh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kqlEtu3bwfg6aefZmRkhHV3/i2tRpVUUSASCmDWVCgEn0NtGcF38wRm9yiHHvsaWqGOy+Vg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\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NZDIZJEk6ayCzWCySz+flIW8LTe7PdIMYjUaJxWJ4vV4SicSSr1EoLlapeIHJ43HKRRGV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\nFZoX3qVYAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig.tight_layout(); fig.savefig(\"/tmp/convergence-onlytrip.png\", dpi=500)"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# For comparison\n",
      "triplets = cities_example_accelerated.random_triplet_subset(K, 10, len(K)) \n",
      "len(triplets)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "11820"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "triplet_cities = tste.tste(triplets.astype('i32'), 2, 0, 1, 1,verbose=True)\n",
      "fig,a = subplots(1,1, figsize=(4,6))\n",
      "a.set_xticklabels([]); a.set_yticklabels([]); a.set_ylim(31,43); a.set_xlim(-126,-112)\n",
      "remapped = cities_example.remap(triplet_cities, cities)\n",
      "a.scatter(*remapped.T, lw=0, s=2, cmap=pylab.cm.Dark2,c=embeddinglabels[-1], zorder=15000)\n",
      "which_lines = random.sample(xrange(len(remapped)), int(fraction_of_lines*len(remapped)))\n",
      "for ((x1,y1),(x2,y2)) in zip(remapped[which_lines], cities[which_lines]):\n",
      "    a.plot([x1, x2], [y1, y2], 'black',alpha=1, lw=0.1, zorder=1)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Iteration  10  error is  5239.75018789 , number of constraints:  0.113367174281\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20  error is  3650.3814402 , number of constraints:  0.0516920473773\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30  error is  3368.9783866 , number of constraints:  0.0450084602369\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40  error is  3235.17281011 , number of constraints:  0.0439932318105\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50  error is  3150.3158252 , number of constraints:  0.0434856175973\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 60  error is  3088.42025731 , number of constraints:  0.0428087986464\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 70  error is  3040.46709453 , number of constraints:  0.0421319796954\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 80  error is  3000.90318964 , number of constraints:  0.0410321489002\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90  error is  2967.60127436 , number of constraints:  0.0406091370558\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100  error is  2938.88727208 , number of constraints:  0.0404399323181\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 110  error is  2912.65765563 , number of constraints:  0.0403553299492\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 120  error is  2892.18067383 , number of constraints:  0.041116751269\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 130  error is  2876.45503995 , number of constraints:  0.0406937394247\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 140  error is  2860.63947436 , number of constraints:  0.0403553299492\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 150  error is  2848.22187995 , number of constraints:  0.0399323181049\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 160  error is  2836.50565142 , number of constraints:  0.0402707275804\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 170  error is  2827.4057131 , number of constraints:  0.0414551607445\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 180  error is  2781.77662286 , number of constraints:  0.0388324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 190  error is  2774.90001715 , number of constraints:  0.0389170896785\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200  error is  2768.49508308 , number of constraints:  0.0391708967851\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 210  error is  2762.02128978 , number of constraints:  0.0389170896785\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 220  error is  2756.00725945 , number of constraints:  0.0388324873096\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 230  error is  2752.80424287 , number of constraints:  0.0387478849408\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 240  error is  2749.56478119 , number of constraints:  0.0389170896785\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 250  error is  2746.21851633 , number of constraints:  0.0390016920474\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 260  error is  2742.75426518 , number of constraints:  0.0390862944162\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 270  error is  2739.14998438 , number of constraints:  0.0391708967851\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 280  error is  2735.3807986 , number of constraints:  0.0393401015228\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 290  error is  2731.46922221 , number of constraints:  0.0390862944162\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 300  error is  2727.47442407 , number of constraints:  0.0393401015228\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 310  error is  2723.4257818 , number of constraints:  0.039255499154\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 320  error is  2719.33329484 , number of constraints:  0.0394247038917\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 330  error is  2715.68978984 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 340  error is  2714.34963426 , number of constraints:  0.0394247038917\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 350  error is  2713.20160205 , number of constraints:  0.0394247038917\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 360  error is  2711.96877336 , number of constraints:  0.0393401015228\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 370  error is  2710.64358293 , number of constraints:  0.0393401015228\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 380  error is  2709.22137859 , number of constraints:  0.0395093062606\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 390  error is  2707.69806261 , number of constraints:  0.0394247038917\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400  error is  2706.06994072 , number of constraints:  0.0396785109983\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 410  error is  2704.33367618 , number of constraints:  0.0396785109983\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 420  error is  2702.48604162 , number of constraints:  0.0397631133672\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 430  error is  2700.52307407 , number of constraints:  0.0397631133672\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 440  error is  2698.43801785 , number of constraints:  0.0399323181049\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 450  error is  2696.21979334 , number of constraints:  0.0399323181049\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 460  error is  2693.86599636 , number of constraints:  0.0397631133672\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 470  error is  2691.4047634 , number of constraints:  0.0396785109983\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 480  error is  2688.86340819 , number of constraints:  0.0396785109983\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 490  error is  2686.24164861 , number of constraints:  0.0395093062606\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 500  error is  2684.50921601 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 510  error is  2683.762937 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 520  error is  2682.97795056 , number of constraints:  0.0395093062606\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 530  error is  2682.12926723 , number of constraints:  0.0395093062606\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 540  error is  2681.21198259 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 550  error is  2680.22224567 , number of constraints:  0.0395093062606\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 560  error is  2679.15611373 , number of constraints:  0.0395093062606\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 570  error is  2678.00908785 , number of constraints:  0.0397631133672\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 580  error is  2676.77564938 , number of constraints:  0.0399323181049\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 590  error is  2675.44902838 , number of constraints:  0.0401015228426\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600  error is  2674.02504741 , number of constraints:  0.0402707275804\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 610  error is  2672.51113436 , number of constraints:  0.0403553299492\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 620  error is  2670.91399228 , number of constraints:  0.0402707275804\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 630  error is  2669.22969904 , number of constraints:  0.0403553299492\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 640  error is  2667.45326068 , number of constraints:  0.0403553299492\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 650  error is  2665.8995253 , number of constraints:  0.0403553299492\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 660  error is  2665.32714359 , number of constraints:  0.0404399323181\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 670  error is  2664.78659586 , number of constraints:  0.0404399323181\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 680  error is  2664.19960765 , number of constraints:  0.040524534687\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 690  error is  2663.56140787 , number of constraints:  0.0404399323181\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 700  error is  2662.86828343 , number of constraints:  0.0402707275804\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 710  error is  2662.11660597 , number of constraints:  0.0402707275804\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720  error is  2661.3028224 , number of constraints:  0.0403553299492\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 730  error is  2660.42350968 , number of constraints:  0.0402707275804\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 740  error is  2659.47530432 , number of constraints:  0.0404399323181\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 750  error is  2658.45443699 , number of constraints:  0.040524534687\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 760  error is  2657.35431881 , number of constraints:  0.0406091370558\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 770  error is  2656.1372102 , number of constraints:  0.040524534687\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 780  error is  2654.73785583 , number of constraints:  0.0406091370558\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 790  error is  2653.35796185 , number of constraints:  0.0408629441624\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800  error is  2653.10446268 , number of constraints:  0.0404399323181\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 810  error is  2651.50499091 , number of constraints:  0.0406091370558\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 820  error is  2651.090656 , number of constraints:  0.0406091370558\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 830  error is  2650.63908623 , number of constraints:  0.0406091370558\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 840  error is  2650.14637072 , number of constraints:  0.0406937394247\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 850  error is  2649.60904977 , number of constraints:  0.0407783417936\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 860  error is  2649.02349747 , number of constraints:  0.0406091370558\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 870  error is  2648.38587348 , number of constraints:  0.0406937394247\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 880  error is  2647.69217144 , number of constraints:  0.0408629441624\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 890  error is  2646.93841213 , number of constraints:  0.0407783417936\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 900  error is  2646.12110429 , number of constraints:  0.0407783417936\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 910  error is  2645.23797773 , number of constraints:  0.041116751269\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 920  error is  2644.28845459 , number of constraints:  0.041116751269\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 930  error is  2643.27284295 , number of constraints:  0.0412859560068\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 940  error is  2642.19044877 , number of constraints:  0.0413705583756\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 950  error is  2641.82309468 , number of constraints:  0.0412859560068\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 960  error is  2640.79367846 , number of constraints:  0.0413705583756\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 970  error is  2640.45959885 , number of constraints:  0.0413705583756\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 980  error is  2640.09517096 , number of constraints:  0.0413705583756\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 990  error is  2639.6974062 , number of constraints:  0.0413705583756\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1000  error is  2639.26360503 , number of constraints:  0.0413705583756\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: DeprecationWarning: Specified size is invalid for this data type.\n",
        "Size will be ignored in NumPy 1.7 but may throw an exception in future versions.\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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rlvJ4PHi9Xo5oHOgliXi7ws1KhsFgQLPZpFAoiOl5Op2OWq2GTqejUCiIVbNe\nr5NKpYS529WrV5mfnycajYp7XLlyhePHj+9pvL8fMpnMXc3kFBTuxb4S7dDC9IO4XC5arRaNRmPP\n4zqdDvf0KG1k3GoDJanH33vvuxgsZqanp/dMAigUCvT7ff70T/8Um82G2+3GbDbj8/kYDAbcuHGD\nJ598kmq1ytTUFLlcjsXFRZ566qmPZZva7/cVu1WFj8W+Eu298Pv9ZDIZ+v3+nseDfR0XvvE/8o8O\nPs3//e6PGWjVOK02HA4HqVSKeDxOPB6nWCwyGAwIBoNMT08zNzfHxsYGOzs71Ot1pqenqdfrTExM\nEIvFqNVqjI6OigaBj8P29rYSJit8ZB4Z0QKMj4+zs7Mjvo7FYthsNiwmE//H8nlW5RrTmEQ+NxgM\nEgqFhDtjv98XRf2FQoFMJoPRaBTDpod75UgkgtVqvW127UchkUggy/JtHzIKCh/GvhFtp9P50H3j\nMPUTjUbZ3t4WDoobK2v8r0deRAXcHFSYmJgglUqJfO/KygrhcFgcPt28eRO1Ws1gMBCjNEdHR2k0\nGiwuLjIzM3ObWfm9uHW6Xq1WY3V1lVqtxtjYGKVSSVltFT4S+0a0w5LC+2FlZYVoNEqtVsPpdHLi\nxAn+6ZNf4qDFQ0uS+b3MZSKRCIlEgtXVVer1OjabjfX1dUZHR7Hb7dhsNq5du8b09DQ+n49ms0k+\nn8fv94vn3w/JZFIUdWxsbLC4uEi5XOa5555Do9HQbDYV9wqFj8S+Ee2HUS6XWV5e5s033+TgwYME\nAgFGRkb29Lj++Nf+B/6Z/ziFZg21y8rIyAgGgwGVSsUf/uEfMhgMxGr+h3/4h4TDYcbHx4lGoyKd\nI8syJpPpQ8PadrvN9vY2LpdL+C4PBgPGxsYIhUJotVqxiisofBT2tWhlWSYejxOLxchms6LEcXx8\nnAMHDtBqtfZYvnS7XVbreVabRf6ql+L6zZtotVrUajVer5cvfelLu3OC3n6bWCzG/Pw8i4uLNJtN\nRkZG6HQ6GI1GkQO+lcFgsMfAPJfLMTExQTweZ2lpiWPHjqHRaPbsg+80yEtB4cPYN6K9dZxGvV4n\nFouJeuPheI8XXniBsbExYT7u9/tJp9NiANfi4iL/yHsYCVgZVIhnUuRyOTwej7BbLRaLJJNJvvzl\nLzM5OUmtVqPX67GwsEA2m0WSJJxO5559aCqVYm1tDZPJxPb2NqlUCqfTyVtvvYVer+f06dMkEok9\nvlDValWcVBP4AAAgAElEQVQRrMLHYt/4Huv1etLpNN1uF5PJhNVqpVqtIkkSk5OTYij1UAipVAq/\n38/4+DhLS0tIkoQkSThcTlSbEi12R2A6HA6SySR6vR6NRsPly5d55plniEajHDhwgFAoRDabRa/X\no9friUQi+Hw+UqkUOp0OSZIol8t0Oh3K5bJoml9aWuLZZ59FkiRisRjBYFC0W8myTLlcVqxnFD4W\n+0a0sViMkZER6vW6GOdxt196i8UinBWHzhEGgwGv10s6neZ/Dpwm6Pbyb268TqC4zL8586ssLy+z\nubnJF77wBZaWlpBlGZfLxc7ODr1ej263i9Fo5ODBg1itVlQqFSaTiaWlJVKpFIPBAL1eL3yQn3rq\nKQDhUzXcE+dyOcxms5LqUfjY7Jvw2GQyidk6o6OjH3p6Wy6XWVpaQqVScerUKcxmsyhD/PqhJwnp\nLKz2qyw1ciwXUySTScbHx1lfX2dqakqs2I1Gg1gstjuGpNHYM9JyZ2eHfD7P3NwcrVaLarXKyMgI\n3W6XeDxOqVRCkqQ9PzOcbDAM91OpFKVSiVgspjQQKNwX+2altVqte8oO70Y6nSaZTGK32/mlX/ol\n8vk8ly5dYm5uDqvVymAw2LWqsVpAgny3yT+//Jf8K+eTAKIVbzh1YHt7m3A4LA6Wer0eGo2GxcVF\n9Ho9drudd955hy996UtoNBoCgQCJRAKn08nNmzfx+XzE43EMBgNut1us+s1mk2vXrlGtVrHZbBw5\nckSE+AoK92LfiPZ+CvJrtRqlUomZmRnh0dRutzlw4ADxeJyxsTERrvYrdWYlKyuDKsV+m9HRUZrN\nJj6fj0ajQTgcZm1tjUqlglarxW7fHfI1tGGtVCqMj4+TSCR46qmnGB0dFQdgtVqNWCwmQmTYrZu+\nceMGV65cweFw4PV60Wq1zMzM3OaDPJyep9Hsm/8ehc+QfRMe3w96vZ7Z2Vn0ej07OzsYDAZCoRB2\nu11Yn5rNZra2tmg2m3i1RiQZZBm+v7FAvJAV/bSpVIof/ehHWCwWyuUy7XabV155hY2NDbrdLoFA\ngJs3b/Lcc8/tCdXT6TTXrl3jySefFI81m02i0Sibm5tYrVa+9rWviTm6arValDTCbjHGjRs3uHHj\nxmf+/insDx6pj/JhyqZWqzE+Pi4ms5vNZtrtNmNjY1y8eJGDBw9y9epVRlQGDurtrHbL/FkrwjGp\nwX+vDmGxWDh//jxOpxONRoPNZiORSGAwGDCZTJTLZdbX15mfnyedTqPX64lGo1y7dk0MoX7vvffQ\n6XTU63Xa7bYIjZ977jlSqRQmk4kjR44Auzne5eVlqtUq4XAYl8slVm0FhQ/ySIm20WjQaDTE3tdm\ns7G2tkan0xHdOuVymc3Nzd0ZtNfXqZj7aJHo9vv84/nn2S4XOBM+QL/fx+l0UiqVuHTpEl6vlyee\neIL19XVCoRDj4+M8++yzpNNp7HY7xWKRUChEpVLBbDZjtVpxOBxYLBYcDgexWEzYrS4tLXHmzBlg\nt3JqOK7EbreTy+XEgZaCwp3YlxaqH0apVKJerwO7vbYrKyv88Ic/5Dd/8zfFhIF2uw1ASyNxaXmJ\nfy/t4DdYyLRq/DPGGdfZyOVyNBoNXnrpJTqdDvV6nWAwSLfbpVAo8IUvfIHLly+j0+nQaDREo1HC\n4TCbm5ucPXsWnU4nWvcuXbokQu3Tp08jSRLxeBy1Wr1naFcikaDdbmMymRSD88eYe2nvkRTtrVy5\ncoWtrS0MBgMGgwGLxYLRaGR7e5tgMEi/32cltsO/LF/BpDfyTe8MibeuMGvxoNFosFqthMNhgsEg\n2WyWL37xi3zve9/D7XZTqVQAeOKJJ9jZ2SEajXLs2DFKpRIOhwODwSD2u++88w4Ahw4dYjAYUK/X\nCQQCe8ohm82mmMR37NgxZW7tY8y9tPdIhce30u12icViYn7O0aNHKRQK/NEf/REul4tAIECpVKJS\nqRB362iUB7Tadf509X0mpR7fmJlhY2ODer2OXq/nvffeY3Z2lp/85CfkcjkcDgdHjx7FaDSyvr7O\n3NwcgUBATBsIhULodDpsNhs3btxgYmKCer2OyWQSTQRD6vU6xWIRs9lMJpNhZmZGEazCXXkkfzOq\n1SqpVIrJyUn8fj+yLIuSxUOHDuHxeHjttddEwf7fPngSgAFQNar5N3/nn4gyRZPJRKPRYGZmhnA4\nTK/X41d/9VdRqVTs7Oyws7PDM888I2xVh433xWIRi8VCpVLh4sWLZDIZRkdHGRsbE4Idpoba7Tbh\ncFh8yCitegr34pEUbb/fFz7Iw1RPLBbj3Xffpdfrsbq6yi//8i/T7XapVCpEl9f4tY4HFbvC/c33\n/5RcLkc2m8VisYjOoatXr+LxeGi32/R6PSqVCiMjI2QyGbRaLcvLy5w4cYJSqYTJZKJarXL+/Hns\ndjsTExOiYaBSqRCNRul2u+K0GGBra4v5+fnP501T2Dc8kuHxrY6NmUyG8fFxgsEg3/ve9wiFQpjN\nZkZHRzl79ixXr14ln89zSu9hoVthS90m0WuQMFrR5So4HA62tra4evUqzWaTY8eOUSgUWFpa4sSJ\nExQKBTF7dmRkhMuXL6PX67l27Rr9fp/BYIBOp6PRaLC8vEytVsNsNmO322k2m6J1MB6PU6vV6Ha7\nRKNRstksarWaw4cPf15vo8JDyiMp2g/SaDS4fv06k5OT6HQ6Dhw4QCQSoVQqYTabyWazFItFvmh2\nk1JlaQ66/LW1StDcxLe0yKDbw+fzYbPZyGQyRCIR4vE4f/fv/l0WFhbE/J9Wq8Xk5CTvvPOO8JQq\nlUqoVCr6/T7BYPCONdOyLHPz5k3OnDlDMBhka2sLm832kYzPFR4fHnnRRqNRHA6HaGQfDAaMjIxw\n9OhRvv/974sRIEePHqVAh59uF4nSo6OS2R41c2j+FKaOLNI6LpeLbrfL3NwcqVSKfD7P9PQ0a2tr\n+P1+rl69isPhwOPxsLOzQ6VS4Vd+5VdEjfGd2NzcRKvVMjY2xubmJtVqFVmWlRGZCnfkkdzTwu7s\n2GGd74EDB+j1euRyObGPvXDhAh6Ph8OHD7O4uIjX6yWzvMmLbTvIu0ZshoGGTqbIu+++K2qOI5EI\nxWJRXO/Xf/3XxZT5SqXCU089RSaTEXXPzz33HL1e764T9GRZJpPJYDKZqNVqJJNJRkdHyWQyygmy\nwh15ZH8rdDodIyMj2Gw2lpeXhXui1+vF6/Xy1FNPYTAYRD/sj3/8Y1qtFn7JwNGiCpBo6eDfvfsj\njh8/zszMDNeuXSMcDrO9vY1WqyUcDvPDH/6QYrFIuVxmY2ODZDJJr9ejXC7zpS99iZWVlbuarMPu\n1PjZ2VkSiQTlcplisShKJxUU7sQjK1qVSkWpVCKRSLC5ucnbb79NPp/HZrNRq9VYXFxkfHxcjLR8\n8sknGQwGeDwezvbsMJABma05F+fXFvn5z3/OkSNHuHr1KuFwGL1eT7Va5emnn2YwGJDP5zl79iyZ\nTEbMwP3Zz36G0+kkmUxSKBSA3Q6eoTujLMtUq1V+/OMfCw8pg8FArVZTZtsq3JVHVrStVgu/3y+q\nlkZGRvj7f//vEwgEMBgMtNttfvKTnwiX/2QySb1ep1Qq4fF4+GJ2d7VFgrcbKS5fvszS0hKtVoty\nuYzT6WR5eZnz589jMBg4cuQIvV6PQqHA+Pg4oVAIt9vNyZMnUavVwi5HlmU6nQ4ACwsLGAwGrFYr\nkiSRy+WYnZ3d91VoCg+WR1a0hUIBl8tFo9HAaDSK8FiSJHQ6Hdvb2wwGA0ZHR1ldXWVlZYXTp0+j\n1Wp57733OKj5hRm5JLEzoqMR2v263W6TyWREA8Ly8jLRaBSLxcIrr7xCp9PB7/eTSCRwuVxEo1GS\nySRms5lut4tGoxFteZubm+KUuN/v43A42NnZuWOzv+JqoTDkka09Hu4RhznUI0eOkM1meeutt6jX\n60SjUYxGo0gBDUsbh5VSx44d4/9KXmZJ3QB234PTV/L4e1p++7d/W9Q0P/HEE6RSKTqdDpFIhJde\neonNzU1mZ2cpFotYrbv+yn6/X5QqGo1GfvCDH/DlL3+ZZDJJs9kkm81y/PhxNjY2mJycRK/Xi4aB\nTCaDw+FAp9N9ju+owmfJvbT3yK608Dc1vUajkUqlQrfb5cSJEwwGAyYmJvjqV7+K1+vFarWyvr7O\n5cuXGRsbo1arcePGDV4oGxgKFiS0Ez56vR6/93u/x5UrV8jlcrzxxhu02222traYnJwUB1t+vx+D\nwYDZbObAgQNUq1U0Gg2VSoXr168TDAZJp9NcuXKFwWDA8ePHCYfDmM1m8vn8nnRPt9u9TbDDPbLC\n48cjKdrh9LxOp4PNZsNgMJDL5VCpVMRiMXF6PDY2JozVDh8+LIS9ublJsVgkn8qgiQzFIfO2q8+J\nL57FYrFw9epVisUiv/Zrv0Y0GhVDpycnJ5mamuL1118Xh1S9Xg9JkqhWqySTSW7evInRaESv1xMM\nBjGZTMKHymq1olar96R7PliLnMvlPtIAa4VHi0dStHq9HkmSuHHjBo1GA4PBwOzsLOl0Go/Hw8TE\nBMePH+fmzZt4PB7C4TCRSARZlkU3j0qlYnt7m4nL8b+5sCyTiSXQarX81m/9FhaLhcuXLyPLMidO\nnCAWi2Eymeh0OvzSL/0S8Xicer1OMplkYWGBcrlMPB7n6NGjhMNh8vn8nr7ZSCSCxWLZY8yeTqf3\nrLqyLNNoNDCbzZ/Z+6nwcPFIirbT6Yhw0ufzcejQIc6fP086nabdbrOxscFbb71Fv99HpVJht9u5\nePEisDtZfmpqCpPJtCsgvekX6Z9d/sBW5MUXXxQHUaurqxw/fpxIJMLMzAydToeRkRGKxSJarRaT\nycTrr79OLpcT7XoejweVSkWr1UKv14smeFmWhan6kF6vt8fULh6PKybnjzmPpGiHFi4HDhyg0Wjw\n8ssvo1arOXv2LJ1Oh29961scOXKEJ554ApfLxfe//3263S5Wq5VUKsXLL7/Mzs4O29vbVMqVXdHK\nMkgSHR2c31gkGAyi1Wrp9/uUSiW2trYYHR3FaDTi8XjIZrMsLCxQqVQwmUx88YtfZGNjQ6yS2WyW\nVqslnBibzeaekPdOHlGtVgutVqtUSj3mPJKnx7Isk81mhUNEqVQSA6HX19ex2+1cv36dVqslBmcZ\njUb6/T4XL17k0KFDQviZTIaMpkf27AyM/KJKSYb/OP0i/+W7/5mRkRE2Nzc5evQoMzMzeDweJEkS\ndq2Li4tMTU0xMTHBwsICkiQRCATEzNrTp08DuyvorSusLMui1nm40kYikdvsVhUeTR6702NJkuj3\n+yLfeeTIEWFX6vf7cTgcTE5OolKpkGUZi8WC3W6nVqvx7W9/G7fbzfb2NqdOnaJQKNDdSTP9s1UY\nDECSQJJ4I7WJxWJhYWFB5GZnZ2eZmppCr9eTSCRoNpscPHiQUqnE9vY22WyW+fl53G43+Xz+jubk\nvV5PWL32+30h2GKxqFRJKQCPqGiHFAoFRkdH0el0BAIBrFYrS0tLvPXWWxSLRQwGA8FgkHq9jtPp\nRKfTsbS0xMWLF7Hb7SwuLpLL5XjmmWfo1pvoNjO7YTLwB/UNrrcKDAYDXC4XZ8+exW63i+l8x48f\nZ319ne3tbbxeLzs7Oxw8eBCtVsvS0hJOp5PZ2Vlg10J1+Mmaz+eFOfqtn7TlclmpR1YAHmHRWiwW\n/H6/qD++evUqr776KlarlTNnzqDX66nVaiwvL+N2u8Usnq2tLTqdDocOHeLKlSt4PB6uXbtGpVKh\n9fs/3c3aSiCrYPGEl+DYKI1Gg4mJCfL5PNlslpGRES5evEgul8Pn87G4uMjo6KjYy46MjGC1WsUq\nmkwmcbvdoopLpVKJWbuw27qn7GMVhjyyvwnDoVcGg4Fer8fMzAzj4+McPHiQ9fV1Dhw4wMjICC+9\n9BKHDx/GbDZjs9mYmppidnYWSZLo9XpMT09TKpWEf/FgNba72soySOCw7FY8ra2tsbCwQC6Xw2Aw\nkM/nUavVYnK8zWYjFosJS9VhWSXs9vzeuHEDg8FAq9XCZrPR7/fRaDR0Oh3S6bSyl1UQPLKihV0v\nplKphEajoVgsYrPZUKvVzM/PMzExQSgUQpZlSqWSGLjlcrlwuVxsbW3RarXIZrOYTCZxLft/uQT5\n2i/uIJHp757olkolstksWq2WSqXC+vo6k5OTpNNpQqEQJpNJ5I8nJibEKtvpdLh48SI+nw+z2Uyt\nVtvj1Pj+++/vmQl0N9rtNrVa7UOfp7D/eaRFa7PZGBsbIxgMEggEaLfb6PV6JiYmxInxzZs3KZfL\ntFotYfUyPDg6cOAAa2trNBoNms0mhw4dQq/XYy21xD3eG9fQaDQ4fPgwTqeT69evk81m6Xa7jIyM\niOtWq9XdqQa/mCw/nCDw8ssv85WvfGXP6x6Gxpubm4RCoT3eyHcjk8nsEfuwu0nh0eORFu2tRCIR\nDAaDKGRYXV0V0++G+8xSqYTb7RY1y5ubm1QqFdRqNS6Xi+eee45ut4tlLS0KLqomDRa7je3tbRKJ\nhEj1HDlyhEajgUajYXl5WZi73VrdtL29TalUYm5uDtidjGC320UJ5rDN78MYjt8cotQlP9o8FqId\n9ssOBbC0tES9Xuf69etUq1XOnDlDtVrFbDZTKpXI5XKkUini8ThTU1M4HA6mp6dFz60pXeWo2gbI\noJL4C3uNra0t1Go1yWSSkydPIkkSCwsLqFQqjh49islkIp/PMzk5CewK7ec//znHjx8Xr3MYGsuy\nzNLS0n07MaZSKWHK3u12aTabyknzI8xjIdpUKkU4HEaWZba2tshkMrzxxhsEg0HOnj0rhmjduHGD\nsbEx3n77bTKZDMFgEJ/Ph8lkQqPRsLGxIbqCvi4FMDf6AJQcev5g9T3aanC73Wg0GiKRCCaTifHx\ncVGy6HA4qFQqWK1W/vqv/xqtVitGYg6bFQaDAY1GA5/PJw6t7sUwXTQkHo8TCoUezBup8FDwWIh2\nODNnZWWFRCLB4uIiL774ImNjY8RiMUKhEJcvX6bT6fD7v//7YlqAy+XaUy1lMBjQaDTo9Xp+9v2/\n5tDSL8JQSeYteweVx8bMzAyvvvoq5XJZTHcfHR0VFjRDU7lisYjP5xOnyMlkkkAgQD6fp9fr3fdp\ncTweJxgMArsfTsrQrkefx0K0ACaTCavVyuLiIt/4xjfIZrMcOHCAQqHA8vIy6XQa2N0PDp+bzWYx\nm800m03RXZNOpymVSvh8PuZdfjTdvymAsNX7mEwmsdrNzc1hMBiE8Xi/32d5eZlkMonL5WJ+fp58\nPk+/36fZbFKpVMSYy/sZDTIsvpAkiXq9jiRJd6yyUni0+FDf4+985zvi7+fOnePcuXMP8OU8OOx2\nOxqNhm9961vCsvTKlSsYjUbefvttRkZGSCQSdLtd0YjudrsxGo187Wtf48aNG6ytrWG1WgmFQjz/\n/PNkMhm+nEjx4/Fd8fwnQ4be975HIBBgcnKSRqOB2+2mWq1SKBSw2XbHZ3q9XmH4dvr0aeEdFYvF\neOKJJ/B4PPf1b0okEiIUzmazTExM0Gw2FeHuQ86fP8/58+fv67kfSbT7HbPZTL1ex2q1Yrfb2dnZ\nod1uI0kSa2trpFIp1Go1vV6PwWCAz+djYmICs9nMzZs3abVahMNhnnnmGXZ2dlhbW2Nubo432lka\nBjUZqUuyVcVuNjMxMYHD4eC1114T+d9XX32VqakpYak67OMtl8tkMhkCgcBHGiYtyzIqlYpoNMro\n6Cj1ep1Wq6WIdh/ywQXxd3/3d+/63McmPB6SzWbpdDqioeDMmTOiekqWZfR6PYVCgYMHDzI2NobV\nauVnP/sZg8GAc+fOodfr6XQ6WCwWXnzxRer1Ov91zSkqpL4baLESNvDuu++ytLSELMuiSWFYbxyN\nRsnlcqLA4s033+To0aP0+/37bm5PJpP4fD5KpZJwuygUCnsa6BUeTR4r0cqyjNvtJhAIcPbsWZxO\nJ+12m/X1dSRJIpFIYDKZ0Ol0HDt2DIPBgF6vp1QqMT8/j8vlElVLg8GATCbDysoK9WwBXf8Xe1AJ\nSlqZqakpwuEwR44cIZlM0u126ff7/OVf/iUzMzMinztslC8UChw9evS21zy0sfkgvV4PlUpFpbI7\nJEzpAnp8eKxEK0mSKNTXaDT4/X5u3rxJvV4nn8/jcDjIZDIcP35cGJsXi0UOHjyI0+mk0Whgt9vx\n+Xzo9Xpef/313b+j4p/Ko+I+z5sC6HQ6arUakUgEnU7H2tqamCvkcrmYmZnB5/OxsrIicqy3pmo6\nnQ6xWIxqtXqbqVsmk2FkZESExbCb4x1GDAqPNo/8AK67IUkSpVKJl19+Wbj6S5KE0+nEbrfjdrvJ\nZrP0ej263S71ep3Dhw+zsLCATqdjZWUFm82G0WhErVYzYXOjr0ZpayCi7/LWwiWkRls0Hgxn5p45\ncwa73U6xWBS9vOl0GoPBgEqlotvtioHWd7OV6XQ6lMtlvF4vkiQpqZ7HjMdqpf0gq6urwp701KlT\n/IN/8A8wmUw899xzFItF1Go1TzzxBMeOHUOWZSYmJjAYDOzs7GCz2fjmN79JIBDgiSeewOv1csK4\nW6L4l5lV/nVnmXK5TKPRoNFocPDgQQKBADabjUKhQK/Xw26302g0SKVSBINBotEo+Xye0dHRu4ow\nl8thMpkYDAaYzWbh9jhcjYfljwqPLo+1aHU6HcFgEI1Gw4svvkij0WB6eppCoUC/3+eZZ57Zs6pd\nvnxZTG43mUy4XC56vR6jo6N4PB5+w3uIMckEMnTkAVf7JU6ePMmTTz5Jo9FAq9XuOmH8Iq20sbFB\nv99Hr9fTbDYJh8OiNvpONJtNVldXeeONN8TzhjnhWq1GLBajWCzecUKBwqPDYxseA6KMsNvtYjAY\n6Pf7PP/88ywsLDA7O8vm5ibHjh3ju9/9Ll/96lfJ5XI4HA6Rax0MBszNzWG322m1WmyvrvPfTR7l\nX2TfBRle9Xb5tlaFWdLh9Xpxu91cvXqVsbExzGYzr7zyCqFQSPhKJZPJOxZVDIsoVldXyWazPPHE\nE8DuIVWz2SQej2M2mxWXxseEx3qlnZycpFQqMTMzg9/vZzAY8PWvf512u0273WZmZgadTkcoFKJc\nLvP000+TSqXo9Xo4nU7GxsbY2tqi0Whw8+ZN/H4/Z4+dwqfarRnW9CX+5P3X+fPXd2f83Lhxg36/\nj1qt5s/+7M8YHR1ldnaWY8eO4fP5RAvhB/9UKhXhXnHq1CnGx8dJp9Ncu3aNYDBIOBzG6XR+zu+m\nwmfFYy1ao9HIyZMnCQQCvPfeezz77LMkEgmcTicej4fnn3+e7e1t3G43JpOJUCjEysoKPp+PZrMJ\ngNPpZH19XczeaTQa/Jcv/2O8OhM9NfyVoURnxIYsy7z++utIksT8/DyDwYBvfetbFItFPB7Pnta6\nIQsLC7z77ru0Wi0OHjyI3W4nn89TLBZRqVTMzMzs6aG9lVwuR6PReKDvn8Lnw2MrWlmWKZfLTExM\noFar0Wq1BAIBfvCDHzA3N8fBgwep1+v0ej3UajVjY2P8+Z//ObVajVOnThEI7KZ1MpkMyWSSUCgk\nps7XqlUOmXZLEQOZFl/xTrOzs8Pzzz/PL//yL/O9730PjUZDJpNBlmURbsNu/vXixYtcvHiRcDhM\nIBBgbm6Od955R+SYw+Gw6Bq6E7FYjG63e9dB1gr7m8dWtN1uF1mWhbvEsHVv2HVz5swZlpeXmZqa\nIp1Oo9fruXTpEt/85jdZXV3lueee40/+5E9otVrMzc1x4MABxsbGSKfTWK1WfsU1DRLEfXr+v/YO\nkiQxNTVFqVRienqaZ599VswMSiQSpNNpLly4wJUrVzh58iQTExNUKhVUKhXXr1/nxIkThMNhVCoV\n2Wz2jvXJg8GAzc1NPB4PvV7vs35LFT4jHlvRlstlRkdHhemb2+0mkUig0WgolUpsbGxQr9d55ZVX\nOHbsGD/60Y8IBoM4HA5isRh//Md/TCaTwWazMTIyIhrkU6kUy8vLhCUDXzGEdj2Sc9s4HA5arRYG\ngwFZlpmeniYYDGKz2VhcXGRhYYFgMEgwGOTy5cvkcjmcTic+n4/BYCAa+AeDwR2bAprNJpFIhMnJ\nSfL5vNJT+wjz2IrW6/ViMpmQJAmfz0coFCKVSuFyuTh16hSyLPPCCy/g9XrxeDwMBgOefvppkskk\n/f7/396bB8eZ3vedn7fvbnSjG+gG+gRAHLwPkCCHI2ou6piRZUmxN4pkJ9lUZe3sJqts4iO1lc1f\nkZNKbbK1641rK46d3di7my3Jp5REcnRa4kj2HBxyeIAXSFyNvu/7Pt79o/U+QuMgwRleAN9PFauG\nIIB+weG3n+f5Pb/f99vh4x//OFarldnZWWZmZvB4PELUbrcbq9XKfzvzIhok2lr44XvvEAgEiEQi\nTExMEI1G+eEPf0ij0eD111/nxIkTrKyscO3aNZxOJ4ODgxgMBpaXl4XbBSC24uvJ5/Niq6+0N6qW\nq3uX5/r/rM1mE+dCJXhraWmJM2fOYDAYyOfz5PN5JEnCZDKRTCZFAWhxcZGpqSkkSRJN+kr48/Dw\ncM861WDklUE/gzoT35rW8P/9+Z9RLpe5ffs2a2trHDt2TKTtKa4ZH//4x0WbZalUQqvViq1wsVgk\nm832Gb3F43G63a64t11vPaOyN3muRQs94RYKBeHN9NnPfpZkMokkSXS7XWRZ5mtf+xqjo6MUi0UM\nBgOFQkEMGBiNRmKxGIlEgmg0ysrKCqurq1SrVTLpNH9vdJaO3KVKl++04pTLZd544w1cLhf37t0T\nhai5uTkmJiYwmUyMjY2h1+sJh8OiSg3w5ptv8olPfALoFdJWV1ex2WyimaJarapjec8Bz3VzhUK5\nXCadTvPiiy9Sr9cJBoPMzc3xla98Ba1Wy8TEhMiznZiYwGaz0e12cTgcHDlyBIvFIhogut2u6CUu\nl3XDkwEAACAASURBVMucOHGCL+py/N6dtwhLvV7kxcVFYTeTyWSYmJjoG6lTKtsejwe3200oFGJ5\neZnx8XF0Oh2tVotwOMz4+Hjfqqu0QKrsbZ77lRYgGAzicDhwOp1IksS5c+coFApcu3aNQCCA2+2m\nXq9z7NgxyuUyfr+fpaUlPB6PuFZRBgCUrfHq6qqIvvy1uU8C0NDCn0Zvcu3aNVE4SiQSOBwOKpWK\neJ5QKCQqxeujQ1KplLCrmZyc7BOsYr/6MKh9yruT5160Ss+vTqfr81xqt9uiGHTo0CHGxsZEoLPi\nnLjeZaLRaCDLMq1Wi2q1KopFjUaDa29f5EWbDyQIDevEwIDL5WJ2dpaxsTGazSaRSISbN29SrVa5\ndu0a3W5XBIJ97GMfw+PxkEwmSafTm36OcrksbFPr9TrJZPKBP7viVaWyu3juRavRaDCbzUiSJHyh\noPcP2mq18vLLL2Oz2SiXyyIrdnl5mUajwenTp8X3UVLl79y5w8zMDE6nk3v37nH9+nUmJyf507/2\nD/nlidMUK2XuDvXeFMLhsLhPVa53BgcHOXjwIDqdjnq9zr1793C73Vy7do133nkHp9PJ+Ph4X6Le\n+nvbRqMh5m3vRyKR6DNOV9k9PPeibbVajIyM4PP5aDabIo19dHQUnU4npoDWi6BQKGCz2cS1Sjab\npV6vi5WwWq0yPz/P0aNHeeGFF0SL4if8BylKbb5dWUOv1wujt0wmA/Q6mRwOB++++y779u0T9qjR\naJSxsTHGxsY4evQoVquVSCRCNBolEomwurpKNpvl9u3bIuXgftTrdREMprL7eO5Fu13v7tWrV3nl\nlVdEFo/D4RDbz0Kh0NdCGAwGGR8f5wc/+AE6nY5EIsGnPvWpTV1LMzYXf/foK+Q6TdYaRQKBAJIk\nodfreeedd6jVajSbTWw2G1//+tcpFAosLy/z+c9/Hp/Px759+wAwGo10Oh3hdzw3N8fQ0BBLS0t9\nd7rbkUgk7jsCqPJs89yLdj3KdUm73aZUKmEymRgZGRGuEk6nk3A4TLPZZP/+/eLrVlZWiEQiGAwG\nPB7PpiLRek5bRtFqJP5T7DZer5dKpcKtW7fQ6Xpn3W984xvcuHGDubk59Ho9v/iLv4hGo2FxcZED\nBw4AvULV+Pg47XZbXE19+9vfZm5u7oE/YywWUwW7y1FFuw5lvC0YDDIwMIDRaMRkMgmHiVqtRiaT\nEaHQ0WiUe/fuYTAY0Ol0WK1WpqamNn1fZUZ2dHQUKV3CoTNziTx/fPtdLl26RCaTIZPJCE9lZTt9\n+vRpkRAvyzKSJFEsFrFarWL+dnR0lAsXLvDiiy9u8pLaSLVaRavViiOAyu5EFe0WtFot6vX6JgF2\nOh3MZjP1ep1IJCLOufF4nKmpKVFtXp/BU6vVhEjeeecdSqUS51xjdIGvJG9itVqx2+0cPnwYo9FI\npVLh1KlTVCoVzGYz2WxWVJIjkYjwX15aWiKbzfLWW2+J13I6nXS73W1/LiUdUGV3ozZXbIHNZmNq\nakrckSosLCxQq9UYGxvj2LFj5HI53n//fV577TUajQZms5lcLifOmoCwl7l79y52ux3PeIDr787j\n0BrJyw3i2g7eXI5qtcrVq1f5mZ/5GRqNBsPDwwSDQcxms6jydrtdzpw5g1arZXV1VaTFv/LKK1y9\nepVut4vL5doyuCsajfY9l8ruRRXtFng8nr4VKRqNEo1GxSif1WollUphMBiEF7Kyfd1IIpEgEAhw\n8uRJKpUKtXqdXzryEoNNmV+/+mf8mAynNeMkk0k++tGPMjg4iEajERXgdDpNNBpFo9Hg8XjQarXk\ncjnRzAE9nyin07mt3Uy5XMZoNG56E1LZnajb4y1QhuLL5TLBYBCXy8XQ0BCZTIahoSFsNhtut5vr\n169z5MgRwuEwRqORer1OpVKhVCqRSqWYn59Ho9FgMBhYWlpCo9FQKZf55ECAZjzLPuMgGW2H/zH8\nY0LUmZycJJfL9Z1NG40GJ06cIBgMYjAYkGWZq1evYrVauXXrFufOnRPNIVuh9DaryQN7B1W021Aq\nlSiVShiNRnK5HFeuXMFms1Gr1ZAkibt37wK9inOlUhEJezqdjlQqRT6fZ3JyEqvVil6vF9c0Wq0W\nk8nEy4dn+ZWDr5Ds1CjqYewnrZFKvq1SfILetvzcuXPodDq++c1vigH5l156SRSqtiMcDm/qR94q\nsUBl96Buj7fBZrP1OfYPDAzg9/sxmUxotVq8Xi+NRkOYmw8ODtJsNpEkidHRUZHJY7FYSCQSaLVa\ntFqtuCqKRqPM2tz89dEjfDV5i+t3bmMaGWdqakrc70YiERwOB6FQCIvFwuLiIsePH2dwcJBCoSDu\njVOp1JbdTcr00sYeZb1e/8BKs8qzi7rS7pBcLie6ksrlMpFIhKNHj7KwsMDMzAz5fJ54PC5S9pSv\nSafTlMtlpqam+vp8JUni9u3b/MYbv8Bp5xhNkwatVsu1a9dYWFggHA6TyWSYn58Xbwherxefz8fy\n8jLT09PiDN1utzedV2VZJp/Pb3JprFarOw75Unk2UUW7Q7rdLpIkUS6XRcujxWKh0+mIwXRlSKBe\nrxMKhQiHwyKIayNKIatRrtLotvkvUgr7qIv9+/fzzjvvkMlkSCaTjIyMsLi4iMViQavViqsmvV5/\n3+DptbW1LdsZdxJWrfJso4p2B5RKJcxmM5lMBoPBIEK4CoUClUoFq9WK3++n2+0SCoUoFouMjY3h\ndDrJ5/ObJmnq9Xpvezw7iyRJTJjs1OUO/0fkEnfv3uVzn/scb7/9Ni6XS1SN4/E4zWZTXDkp2bSp\nVGrTmVZJ0Luf8bnK7kUV7Q5IJpO43W40Gg2yLNNsNqlWq3Q6HQKBAEajkYWFBXQ6HWNjY+K6aH0x\naT3xeByn00kikaBarfLrB17GqTER7lRxOBxEo1FOnz4t4knC4bBY3ZXQLSXDp9Vq9Ymz0+mwtrZG\npVIhGo32FZ2y2axqar4HUAtROyCXy2E2mzl27JiIrHzhhRcAWF5eJhAI4PP5NvX0JhIJZmdnxfeo\n1WpEo1GRNu/xeHpXMRn41cmP8P3MCm+ZKwy8eY0TJ07w0ksv8d577+Hz+VhZWQEQhSxZlkkkEtjt\ndsrlsnjNcDjMiRMntmywqNfr6vzsHkAV7QNIpVIMDw+Ty+WIRCLUajUxLqeM7nk8nr4VtVarkcvl\nSCQSJBIJABwOBxqNhunpaSqVChqNBkmSMBqN+Hw+3pAkflyNESnleNnnxWKxEIvFkGWZRqPBxMQE\ner2ey5cvU61WRbOE4rsMPbuZw4cPbylYlb2Duj1+AM1mk263S6lUwul0Uq/XqVarwv2/2+0SjUaJ\nxWKic6pWq2EwGDh48KDwMjYYDBSLRYxG47bma3bLAH+6cpWvaKP4x8eYn59nYWEBjUbD/Pw8VquV\nM2fOMDExQbVaxWKxCIEq/dJqsPTeRxXtfeh0OiwvLyPLMoFAQKTDDw4OcuvWLS5fvszt27dFZVn5\n1Wg0uHTpEiaTiVQqJVLdx8bGyGazOJ3OTWfd4eFh/tdXvoDTYOFKNsrl6Ap2u12kEpw4cYJ0Ok2n\n0yGRSDA3N0cgECCRSIg3jPsZlK935VDZ3UjyfcqJD+q22eso50aTySR6gsPhcN8VzlaN+LIsc/ny\nZc6cOUOz2eTevXuYTCZMJhOxWAyfzydiLdefg5vNJj9OLPNPbn2XY4Yhfn3klOhJtlgsrK2tiYDq\nWq0m5nx1Oh0DAwP3vX9VBwZ2F/fTnnqmvQ8bRaUIZf3vt9rqRqNRYRiuGMCNjIwQiUT6fKUAvF5v\nX/V3wO3kX9x9k3CzzOrqKhaLBYPBILbo8/PzNBoNxsfH8Xq9pNNpJElSGyaeI9Tt8UOQyWT6LGTy\n+fyWyXXKxI8sy30thsrHlasXu91OsVjs+9oRs42/c/Ql8rT4i3Et9ukxbDYblUqFbrfLwYMH8fv9\ntNttbty4wfXr1wmFQuRyuW2f+3neLe1FVNF+CJQE9/UUCgXhP7y+KymTyYjrlnq9jtlsZmBgYMsM\n2V/ad4YhvYlr2ShXa2lKpZKwTX399dc5evQoHo+HbDaLy+XihRdeuO/96/ooTZXdjyrah2BjoWer\n+VRldrVYLDI8PCwcG2u1mnA/XC/0jaugLMsUi0V+58xfJdOo0DZqGR4eplKpMDExISaA7t69S6FQ\n4Ny5cw9sTWy1Wn3Puj5qRGX3oYr2IXiQODqdDpIkEQ6HGRwcFNcvG8/C90P52hP+SX7z5S8wn4vx\nO9/4Y9F5lc1miUQiGI3GbZ0kHyTK+22lVZ59VNE+QmKxGF6vl1QqtclyZqsQaOh/I6hUKphMJqrV\nKjabjTOj49zNxvn9+hIDAwPk83kajQZarVb0QK8nn8+L11PYuJKvH+lT2Z2oon3ERCKRvorzdv3H\nW5FOp/vEbTeayXca1OQOf3LnPY4fP065XMZgMJDNZjdFWm51Plb6ptd/znYrtMruQBXtI0KWZbRa\nLQMDA33nxwfdjyqCTiQSuN1u4vG4EL3TZOVVzzR1uUNGalEoFLh37x5DQ0M0m02sVquIFWm1WpRK\npU1eVYpbxsbXexpEo9Gn9tp7CVW0j4hOp0On09lUxVVG6DZ+bD1Kq6TJZBIOiwr/y/FP89U3fpnv\nxe/yrR9fQJZlbt++jcvlwu12k0gkaLVaXL16lVarRSwWeyQ/TyqV2vbP6vX6Q3+/ZDKptlg+IlTR\nPiKUTqlWqyVEl8vlHjgKp5iOK/Y1G21gJEnCIxuwGsx8rbbK0aNHyWazJJNJIpEIyWSSZDKJ3++n\nUqn0repKe+XDokR/bkUmk6HT6TzU98vn8+h0OlW0jwhVtI+AeDwuzo3KzCtsbe2ycbu6XtgbzcSL\nxSKdTodhs5VTngkcBgvvLy3g8XgYHh6m1WpRq9UYGhrqS8FTsm6VLffDsLa2htfr3dZu9WHtapRk\nBnUk8NGhtjF+SCqVClqtVrQzKv+g6/X6lvEb64tNsiyj0+m2XbkUh8dut8sprYPu+Aj/8Pq3+blq\nhM8P7OPs2bPCVlWj0YhrJaXLauPWfDtvZoVQKITb7d7W9G27n2k7Wq0W6XT6gSl+Kg+HutJ+CDa2\nKa6nXC5vec3TbrfF9jkUCrF//34MBsOmlshut0sqlcLj8fTsUv37+WpkHqkLf8VzSFSOnU4nCwsL\nuFwu9Ho9jUZDnEcVgSpV5VQqhV6vF8Wr9YTDYVwu131FudNYkW63iyzL2/pUqXw4VNF+CO73j/JB\n97KlUkkEaQ0MDFAul/uuYm7cuMHRo0cJBoNMTEzg9Xo5ZxzluNlJIhYTxSAlUFqSJFwuF5cvX8br\n9YpGD/jp/W273abRaGwyNo9EIgwPD28757vx2e9Hu90mlUoRDAZ3FLup8vCoov2ApNPpvjbFhyWb\nzYpz3sZqcjQaxWq1Ui6XGRkZQafTIUkSnxnZj3NomK+UlvhecpFv3+0lDSgilCSJeDzOyMgId+7c\nwe12P/CKJxaL4XA4HhgwvdOIzGQyKax0Pujfjcr9Uf9WPyCyLH+gaqgsy32je/DTTiroidlqtQpL\nGUVM8Xicubk5Puc+QLBbZrmW53YizNDQEMViEZvNRiQSEefaRqOBLMt9977r3yiU72mz2XZUWNp4\nFbUdilGAannz+Hjg/4Uvf/nL4r/Pnz/P+fPnH+Pj7B4+6NRMvV7HarX2FXu63S5arZZKpUK1WiUa\njdJutxkeHqZUKtHtdkXr4RtTx3k3H+EPg1f5xYlZ8fXNZhOdTicq06Ojo8TjcQCx4jWbTbEFTiQS\nWCyWHXVHKW8KDyKfzyNJknq18wG4cOECFy5c2NHnPpRoVT48mUyG6elp8XulIttut8lkMng8HmKx\nGB/96EeJx+MsLCxQr9d5+eWXgZ5BXKtYRo+GxVyS8z/52LvvvsvP/uzPAr1VttVqbRKkslVOJpPC\njWMnFIvFbRP5FNSrnQ/HxgXxN37jN7b9XHV7/ARJp9Obmi2UWddQKMTY2JjwOIZe5GYgEMDlcrGy\nskI4HGZlZYVXPNO06XK1mSUej4sttcLdu3eZmZnp8zlWeqLT6fSWwwbb0el0Hng2VYpPG3uhVR4P\nqmifEJ1Oh3q9vmXBJxQKEQgEWFlZYXBwUKxW7XabbrfLoUOHRDZus9kkvriKHg1/dfIk7yeD1Go1\nZFmm0+lgsVioVqtoNBrMZrO47onH4xiNRrRa7bbdTlux/ry9FbIsiwq3UhFXebyoon1CRCKRTfeg\nuVyOdrstOpr8fr9IlIf+YYPJyUneffddbDYbwxojv/XaL/Ctpav8g8v/iSVt75x88+ZNPB6PmKc1\nmUziaqhQKCBJ0gdKGLjfVU8wGGTfvn1A7xpLHft7/KiifQIoM6yKfaqCUr2tVqvY7XaMRqM4d24s\n/iSTSRwOB9euXePggQN8bmqWI6NjNOjyz2/9OQeOHuHq1avo9Xp0Ol3f4P3CwgJDQ0M4nc6Hcq14\nUBh1NBoV6fQqTw5VtE+AQqGAw+HoayOs1+uUy2WRW2uz2URKHvy0Egu9Boq1tTUGBgaYmppCo9GQ\nyWT4pLcXEVJs1fmDuxeBXqHL4XCIN4h2u00ulxM9yEqjxU5QvKy2IpVKYbVa1audp4Aq2sdMJBIR\n3lLrmymuXLnC4cOHqdVqYjVrNpsYjUYSiQSjo6NYLBYuXbrElStX0Ov1OJ1OSqUSNpuNTCbDrRs3\n8WFCi4ZYPsP4+DhXrlwRr1MsFjGZTDu6X93I/SxylK22uhV+OqgDA4+RarWKwWAQ20dl1QoGg7jd\nbrLZ7KY2yE6nQ7vdFldAbrebffv2IUkS9Xqd+fl5jhw5QjAYpN1u80+mX+FKLcWgxohtwCYKT5VK\nBZ1OR7fb3bZfeLu+aeit2Ftd89RqtU0jgKDatD5J1JX2MbKVdWksFsNqtW4SrDLeF4lEgN7WVBkC\nqFarDA4OiohMs9mMJEmMjIzgRM9fcc7QqFb5jcU3abVbrK2tYbFYcLvddLtdarXall1PjUZjy+fe\nziKn3W6TSCS2dOJIJpM7GiZQ+fCoon1MrJ9vVVC2nIuLi5w6darvz9rtNtlsllwux+joKC6Xi3Q6\nTb1eZ2BggEuXLlEoFBgbG6NWq3HkyBFef/11Go0G09PT/GUpyvvVJHWvg0gkgl6vF2dopY95PfV6\nfdvtr9JnrASKKVND9+7dY2xsbMuv2Wmbo8qHRxXtY6DVatHpdDYVccxmszjDbvRtSiQSIh1er9cT\nDodF0vvKygo+n49ut4vdbqder3P06FEAhoaGiMVi/D+f/mU0wA9LIex2O51OR9yxbnVlszEtAXpC\njkQiRCIR0Szh8/kYGRkhGAxisVjUSvEzgPrW+BjY6swHvYb9RqPRZ3peq9W4ceMGo6OjYntZLBZJ\nJBLkcjl8Ph8OhwOfz8fy8jK3b9/m05/+NNAT3tDQEKOjo9y5eRu7wcwydcIOreiG2ijYjdveYrFI\nqVQCEF7KBw8e7CsyRaNRXC7XQ1WeVR4f6kr7GNiu42h4eBi9Xt83uVMqlXC73aKDqVgscv36deLx\nOGNjY0xPT+Pz+eh0Oty4cYOPfvSjfakFZrMZm83G2NgYXxo7QxuZoq43SbS+/dDlcoltrrLljUQi\nSJKE3+/H7/fjcrk2NUik02kGBgbEG4jK00ddaZ8wVquVer1OIpHA4/FgMBh47733OHnyJGtrayST\nSWKxGK+++mqfv9Of/Mmf8JGPfER0NCnNF4oflCzLDJut7DfY+XrqDod1cP4nV0ndbpd0Oi2EPDQ0\nxOzs7KZVuN1u921/Fdsau91OqVTatjOq0Wg8lA2NyodDXWkfM/l8Xmw/ofcPvFAoMDExgdFo5N69\nexgMBtLpNIVCgcXFRT772c/2Cfb69et0Oh3OnDkjPlYoFDAYDKKYNDIyQr3ZQNuBarfNt9oxrl27\nRjgcFokHLpdLvO56AbZaLYC+2dtarSYsc9Yb122FGlj9ZFFF+5hxOBx0u12Wl5e5ePEier1eCCCd\nTnP79m0OHDhAIpEgHA7zxhtv9BWwgsEg3W4Xn88nhKZUoTdO8fiqEv/hU7/EoNbAX3YyRFoVDAYD\nx48fR5IkdDodjUZj04qpJPJBr89YSZtXtsPtdntbd0aVJ4+6PX4CNJtNLBYLZ8+eJZvN8tZbb5HL\n5cTd6dtvv0273WZkZIRGoyEMxzOZjOhPbjabxGIx9Ho9uVyOmZkZbt68SalUolKpYLPZeP3111lb\nW+Mf7n+J/3vxIr+99h4zgy5R4DIYDKyurnL27NlNz6h4TUHP+0oZAlj/xqDybKCutE8Al8uFx+Oh\n3W5TLpc5ePAgL7/8MsFgUDRL+Hw+zpw5g9frxev1im3vxMQEDoeDs2fP4vV6sVgstNttQqEQa2tr\ndLtdUQG+fPkyrVaLj1g8OGQ9K9Uc/+ruj8RzKJ7LW51NG40GJpOJtbU1xsbG+pwc1ZT5ZwtVtE8A\nRQCdTodAIEC5XGZlZYWpqSlOnjwpXCSUO9JisUgmk8FqteJyuSiXy6Iinc1mGRwcRK/Xi689f/48\np0+fZm5uDpPJRKvV4p8ef52m1CVXKYnOJyVvSDnDKlSrVcxmM7FYDJfLJZokyuWyKthnEFW0TxCj\n0Ugmk2FgYEB0Mq2srLBv3z4mJibw+/3Y7Xbu3r1LOp0mlUqJ6SDobZcjkQhutxuv10skEulbFZXr\nmzNnztAqV/nt459jqZ7nL5duAb0t8PHjx0kmk+KZms0mlUoFWZYxm819Q/r5fP6BW+PHsX1W83Pv\njyraJ4xer2dlZQW/30+73cZkMompnGazSTabZWRkBK/Xy+zsLOFwmHA4zNWrV0mn07zwwgvodDqi\n0ai4992KkydP8nY1Tkkn869v97bIW7UaZjIZtFotsiz33S83m80dFZ/uN773QdkqslPlp6iifYIk\nk0lSqRRHjhyhVquRSCTYv783E9vpdIhEIqJVUNkCLywsMDs7y6FDhygWi8RiMe7evYvNZruvWGw2\nG3/Nd5SpgWG09VZfCl4mk6HZbAK96vTg4OCmPumNubZPCnVa6MGoon2CDA4OivzaxcVFTpw4AfT+\noa6urorkvE6nQ6vVIhwOY7fbmZycpFgscuLECbGFLRaLxONx0QCxFbPTB/nfJj/Gr4+/yM2bN8WK\nfujQIW7fvi2iRzYOATxN4ajTQg9GFe0TxGQy4fF4+OY3v0kgEKDb7QKwurrK5OQkiUSCQqEgjNDH\nxsb6PIsVQQcCAfx+PxaLhXA4TCgUIhKJbNpWSpKE1+ulrOny6ys/ZLWcBXpXP+12m7fffpuDBw9u\nek7Fm+ppbFPVO+EHo4r2CaPRaDh79izHjh2jXq/z3nvvodVquXz5Mnfu3CGfz2M0Gsnn84RCoZ5D\nxa1bFAoFcrmc2MYqZuaHDx9GkiS63S6VSkVUoJWKcSAQoJEvkaTBP7ryTfEc8XgcjUazpbG4krb3\noAGBndirqjx61OaKp4DiCFGr1Th27BjhcBiz2czBgwdFUwP0QrheeuklEokEGo0Gi8VCNBpFkiSS\nySROp5ObN29isVgwmUyEQiE6nQ4ej4elpSUGBgbQarUcO3iIl4NXuGNusZRPYKq0GRwcJBaLYTKZ\n+rbDGo1mx+kJj/rcuz6QW2V71L+hp4jBYCCZTAq/pfUje9CrzLbbbUqlErOzs31/JssyPp+PQCBA\nPB5HlmXm5uaA3vbWZDKh1WrRaDRcu3YNQ6ZC2i/z8a//a/722CnOa0eYmZnBZrP1Te+Ew+EdN/9v\nzL/9sCSTSXWSaAeoe5unQL1eZ21tTTTuj4yMYLVatxwwv3HjhihYbYfH42FoaIhgMEi9Xsfn8zE1\nNYVWq6XT6dBoNPiVz3yRCb0NExIfOzSL1+tFp9P1bYHz+fx9zdq63a7IB3pc7CRO83lHFe1TwGg0\nMj4+jtFoxG63k8vlNmXgtFotQqEQ09PTO/qHbDKZmJiYIJ/PC2Ep7ZBarZZ0KsXvnvp5pjQ2/vnb\n/xnf+BidTkdc/cCDzcZjsdhTuQZS6UcV7VNAEaHBYKBYLG7pu3ThwgUOHDiwo0zY9Xg8HhwOB6ur\nq+Tzea5cucLP/dzPcezYMZLJJFPaARbKGb61Mo/D4aBQKAA/HahXKJVKfZ1TQJ9v80bK5bIYdPgg\n7DSZT0UV7VOlVCpt6bsUj8cxGAwfuD3QZDLh9/tZWFig2+2K4QOHw8GXzr6BvgP/7P1vEY5ERBCX\nkibQ6XQIhUI0m82++9KNfcjrf5/P56lUKn2ZP8pqv9M7XzVSZOeoon2KWK3WTcPj6XQak8m0SQQP\nQ7fbJRQKYbVaOXXqFFeuXCEYDJJKpdB0ZPaZ7BRaDf7Rve/TaDRot9vIssz8/DzBYBCfz7fpuTb2\nIReLRex2O+l0mk6n07dtbrfbYkUOh8Mf6GdQ2R5VtE+RjVtNpShkNpvR6XQfqCgjyzLLy8sYjUYK\nhQLtdpvDhw/jcDhot3tXPf/ToddAlom0y3SNOr7//e8TiUQIBAKigLUT4vG4SD5Yj9LVVCgUNkVq\nblXIUlsXHw5VtM8IlUpFGJT/xV/8Ba+88spDfX2tViMajXLx4kURBXL69Gn8fj8mk4kjR44wPDzM\nj370I4a6OpAkOjJcXLzNvXv3OHjw4LaB0IlEYlMBKhqNYrPZts25lSRpyy2v0gW2nlQqpbYuPgSq\naJ8Rut2uKDo9aHKm2+0iSRK5XE50QFWrVTqdDqdOnSIQCNBqtTbdt1qtVjweD5IkYewCyPzbxj0O\nzs3S6XS27YDa2Fq4urqK0+kULo0bjdAfduVstVpq6+JDoIr2GUGpnCoztFshyzLxeJxbt25Rr9cx\nGo3C/rTZbOJyuURFemMlNpFIUK/XabVaaLVafrngRFOo0gL+3t1vEclnaLVaopoMvdWy0WhgMBiA\n3pvF0tISgUAAo9FIKBRCo9FsGST9oJhMlQ+O2hH1jFEoFNBoNESjUaA3KJDJZMR1y+joqFhFmtx7\nMgAAHP5JREFU8/k8+XxeFK9kWSaXyxGNRnuDAuUykiSRTqcxGo1iq+p2uzk6c4DBN6+Tt1toAoVs\njgP+8b4heFmWSaVSBAIBms0mkUiEqakp1tbWaDabTE5Obtl2qISFbUww2Aq1dfHhUf+2niK5XK4v\n5Dmfz1MulxkfHxdb4IGBgb5+ZACdTicqy5lML+JSWZ2bzSZarVacQePxOPv27WNwcJBarcbdu3dZ\nWlrqZfwsJskf84EsU7Xot832KRaLFAoFJicnSafT5HI5Tp48ueXnbhfepbCxuKa2Lj48qmifIkND\nQ33XKF6vl2KxyODg4I4qx8pWdv12OplMioGEWCyGzWYT21ez2Uy32yUajVIqlRhua2ms5kntc/Db\nd37MYZsLr9crzsK5XA6n0ykCw4LBIMPDw6JoJMuyGDpQiljpdJp2u/1QQlRbFx8O9Uz7DCFJEna7\nfUf/iMvlMvV6ve/cuH6FC4fDDA4ObjpvplIpEokE3W6XQCCA9lvvA3ClnOBX3/8GwWCQGzduiD5j\njUbD4OCgcGm02Wxks1kikQjz8/PYbDbq9br4/spKrxaWHh+qaHchtVqNQqGw5TWM1+slFAqJ6u56\n8vk8t27dwul0UqvVGBsbo91qoe9KIMtcKkT4QWKRZrPJ9773PdLpNI1Gg3K5jN1uJxqNCkscJRhM\nEbHC/VodlT9XUFLtVR4OVbS7jFarRSaT2TTGB73qbjgcZmRkZNOVUTqdZmVlBVmW0el0HD58GIvF\nQrfb5b/XTorP++PkbRKJBMPDw7jdbur1OvF4HJ1ORyAQwOVyMTg4SDQaFe4bjUaDTCZDIpEgHo/v\nuJNLOQqoPByqaHcRiiiVM+t6lOyd9UbnCtFolIWFBW7dusWpU6d47bXXkGVZBGdVgzGoNECWWSim\n+GEuyL1792i329jtdpxOp9hmJxIJbDYbOp2OdDrNrVu38Pv96HQ6ut1u34zt+iKbguKyofLBUUW7\ni6hUKuzbt08UnjqdDuVymXg8zvvvv8/AwIBIx1N+ffe732VpaYlqtUo6ncZms3H9+nXef/99KpUK\n3W6XZDKJ6evv9F5ElvlKY5XZ2VkcDoe4E1YMztPpNNevX0en02G1WqnVaqRSKSwWS1+TxHY9xw6H\nQ825/ZCo1eNdhHL+M5vNlMtlyuUyFouF4eFhRkZG+nqGM5kMKysrTE9PU61Weeutt3jjjTc4dOgQ\n8XiccrlMNpvF6/VSKBTQxwrUry7ByWmQ4b/UI5yrW8S5WBm7CwaDnDt3jlqtRrPZpFqt4nA40Ov1\nrK2tcezYMUKhEC6Xa8uuLrPZTC6XU10XPwTqSrtLUVoSBwcHMRgMQrClUomlpSWy2Sxzc3MMDw8T\ni8X42Mc+Jkzgms0mQ0NDwrx8bW2NarWK+1IYZBmQuRUNMjk5yeDgIIcPH8ZgMJDNZrl+/TrZbJZO\np0M4HMblconzdalUolQqbSvY9aitix8cVbR7hFqtRigUot1uMz09zf79+6lUKszPz3P8+PG+SnI6\nnWZsbIxut0s2m6VUKuFwOKhk87QivdjL76WXiFeLVCoV8QaxsrLCa6+9xp07d7h58yZOp5N6vY7F\nYhEV7a2KYBtR72U/HKpo9wj1ep2xsTHRrFGv15mfn+fFF18U+bYKkiQhSRI//vGPaTQaovHfbDbT\nvHint9rKMv/NX3yVWDJBNBolFApx6dIlZFnm4x//ONPT07Tbba5du0YikWB+fp5AICD6lKF3xSTL\nMpFIpO9Z17cubhw2eJokk8ln6nm2QxXtHmGjy0U8HufcuXOiu2n96jYyMsKFCxcYGhpienoaq9WK\nTqfrZeFeD0Ot5xuVlRv8ePEGTqeTN998k5deeon9+/ej0+lotVoMDg7yyU9+krfffpsLFy7w1a9+\nlWazKTybE4mECMVejzKK9zDOj4+bXC6HVqvdcvjhWUMtRO1RlH7lxcVFZFkWAwjQS423Wq2YTCaW\nlpYol8uUSiU6nU7vKuj3v8PA3/8rAHxLm2Hf7/8+2WwWs9ks+pAtFguLi4ssLS2Js+/U1BQ/+MEP\n0Gg0jI+Pc/jwYSKRyJbb4Ww2i81meybOtZVKhUaj8dB+XE8LVbR7nPHx8b4tK4DP52NgYIBarcbQ\n0BAXLlwgm80iSRI6nQ6zeYBWqw16HRmjTEbq8tnPfIZLly4RDocJBoNMT0+LALHvfe97jI+PUygU\n6Ha7HD16lLGxMZLJJCMjI6TT6b7Xr9frFItFJicnedo0m01WVlY4duzY036UHaNuj/c4GwWr4HQ6\ncblc+Hw+NBoNRqMRjUbD/v37MaRKHPjuovjcfysH+cp//hrpdJrl5WW0Wi3vvfceY2NjfOMb36Be\nr3PlyhX27duH3+9nYmKCWCy25SB+sVikXq+L52o2m5tEvdEF8nHR7XZZW1vb1n3jWUUV7XNMJpMR\n9686nQ6n0ylmd5uJDHSUs6jEdyth8vk8ExMT+Hw+7HY7b775JrlcDo1Gw9zcHIODg6IfWplA2pg6\nf+vWLWHz2mq1iEajm+Zu2+324/3Bf8LKyoowEthNqKJ9jlGM3vR6PaOjo2i1WhwOB4FAgJGREfw/\nWhKfm5ty0mq1+KM/+iNarRZ37tyhXC4zODhItVrl5s2bxONx8vk8a2trxGIxYcSuNFEkEglcLhel\nUklkD22cFX5SBINBxsfHAXZdiJh6pn1OabVafOITn+Dy5csiVkSn03Hnzh3MZjNWq5WDLjupWpum\nWUdbpyEXsOE3mfiDP/gDdDodBw8exGKxcOfOHQ4ePEin0xFje6dOncJms1EsFtHr9RSLRbRarVjJ\nFxcXOXDgwFP52aPRKCMjI30phLuJB4r2y1/+svjv8+fPc/78+cf4OCpPivHxccLhMPv37xdV5FQq\nRbVaZWRkBLvdzltvvoX7PQuhX3oJkPnxQIUvpLQEAgEmJiaoVquUy2VkWRZezfv27ePKlSt9W952\nu02hUKBcLmM2m7lcivMpx+RTabJIpVIMDAxgsVjIZDLbunU8aS5cuMCFCxd29LmSfB9vEEmSVE/a\nPU4qleJLX/oSDoeD8fFxLl68yPDwMFNTU2Iw4K1PTCDregL7Lec5bs/3QsG+9rWvYTAYROPGqVOn\nkGWZ2dlZJicnabfbJBIJms0mw8PDvW1zLsOXVr7Pvzr4ST7/kfNbPpMSav2oyefztNttXC4XhUJB\npBU+i9xPe7trM6/yyFlbW+OVV17h1Vdf5fXXX2doaAifz0c6nWb//v14vV7+WltZNSUuxO8hSRLz\n8/N4vV4OHDhAq9XC6XQSCoW4d++e2HKmUilisRiNRoNcLkc4HMbvHOH/mnydj4ztF88gy/JjrxhX\nKhVqtZrYAezmWV5VtM85g4ODjI6OMjc3R7lcFv+dzWap1+t4PB7GG3o+GZIAma/rMyxkYiJ5PhgM\n0mq16HQ6nDx5Eo/HQ7vdJhKJcPnyZdbW1tBoNJRKJZrNJl6vF6/b07c1DgaDj9VuVXG0VIbza7Xa\nM7Mt/iCoon3OGRoaEj3Lt27d4sUXX6RQKPDyyy9jsVgol8toNBqOW0eg09uuvT8zQLPZRKfT8alP\nfQpZljEajVy/fp1Dhw4xODhIuVxGq9UyMjLC8vIyy8vLHDlyhGq12tfWuLa2ht/v3xRF8qiOZbIs\nEwqFRKUYegMTu7EApaCKVkWkyc/MzIi+4KmpKQ4ePMjk5GRvKEDT+snViEzeBDrPMO12m3g8jsVi\n4cyZMxSLRW7evEk4HEav14s72hdeeAGns3dl9Oabb9LpdIhGo7z33nsMDAxsamVUwrAfBcvLy0xN\nTYnfd7vdXXfFs5Hd/fQqHxqXy8XExATHjh1jdnaWs2fPcvr0aebm5qhUKvz8z/88+/fvJ5/OYOjI\nP5m3hYhcp1AokMlkCAQChMNhJicn8Xg8IqpkeXlZ+EzV63UOHTrE0NAQDoeDUCjE6OjolttivV7/\nSBos1tbWGB8f79uKP64i15NEFa0K0Gt39Pv9+Hw+YYHq8/mIRCJoNBpOj8/whRUN1ngZkLig6yUh\nhEIh0uk0Fy5cEMkCkUhEBHRNT0+zuLhIq9XiO9/5jhiot9vtOBwOqtWqWJEVoSpTRB+GWCyGy+Xa\ntIo/yC1yN6CKVqUPn8+HJEnE43HOnDnDiRMneO2113A6nTQaDaZupgGZqkGm+pEZKpUKn//85zl4\n8CBra2tcunSJdruNx+NhYGCAq1ev0mw2+fSnP02tVsNgMJBOp5mamqJUKpHL5TaN8O10pU0kElua\nxymeVesjTqAn5N0yyXM/VNGqbMLtdqPVaoUBm8Fg4HOf+xyBQIDXXziHMd8Tylu6XjvinTt30Ol0\nVCoV/H4/Go0Gi8XCxMQEJ06cYGRkRASAAQwPDyPLMgaDgUQiwcLCAk6nUwzL63S6B4q2UqkgSdIm\nlwzl/nWrIYCN6X+7FVW0KluidEop964LCwvMzc1x9uxZfi7bW8FkSebaaC+HttvtYrfbcbvdHDhw\nALfbjdlsFuN5ineysgpnMhlGR0dJpVJotVoSiYToQ37Q9lgJBttoDFetVqlUKlsGf2WzWYaGhoTN\n625GFa3KtszMzAC97abT6WRmZga3283coWM4qr2t7NqEjYVmgXq9zszMDJOTkxQKBVqtFi6Xi1Qq\nhdlsFv3JNptNeCNHIhGKxSIGg4HJyZ+2NT7ozLm2tsbExETfx1qtFqlUatsik+J1tVUS/W5DFa3K\ntuTzeQYHBzl48CCxWAxJkrBYLMzOzvL5lAWaHUDiP7CG0WgUZ9JarYbFYulZs+r1LC0tCb/mWq1G\nLBbD7/dz9+5dGo0Gx48f3/E1jJJ7u17YsixvKWSFSqXSZ2ynFqJU9iRK+p3b7WZgYICjR48KA/Lh\n4WEmJib4mUhv3qQlw+TUFNlsVjRkwE8tVX0+H6lUSjQ4lMtlut0ukUiE8fHxHef51Go12u32Jh+n\nlZWVvrvYjWSzWYaHh8lkMk9sVvdxoopWZRPK+XP92dBsNuN2u1lbWxP5t4fsLmbqOo50LfzL6EX+\n0lyh0G3SaDSo1+skEgn8fj8LCwvs378fSZJIJBLIskytVsNut28KCbsfiURiU/V3bW2NQCCw7eqp\nOD8qPs0bQ8t2I6poVfpQTN/WZ94qTExMMDw8TKlUQq/XMzw8zOtlG6tylSBVvlUJ8d3wbRwOB9ev\nX8fv9yNJEpVKRczbhkIhhoeHicfjwjx9JyhRm+uJxWI4nc5tLXUAEQgWDodxOp3PjPvjh0EVrYpA\nadzfzkZUkiQmJiZE80UsFmPC56eqhZYsM97UcrJhEenuyso6MjKCLMvcvn1bxJfo9XoajcaO/Jmy\n2Sx2u72vPzmdTmM2m++7UitV4kwmw/Dw8K4/yyqoolVBlmWWlpbw+/0PnH4xGo2Mj49jsVgolUqY\nzWZek4YAmXK3Q7xSoNFoYLPZKBQKdDodHA4Hq6urSJIkAriU9sYHpREoW+314i4UCsiyvOVuYD2x\nWIzR0VHRgLEbPI13gipaFfL5PFNTU8L1/0HodDqOHDnCxMQE8/PzfNY0jq7RIWuS+a3SbcqVsmhv\n1Gq1mEwmBgcHaTQaHD58mNu3b/dN3dzv3jQWi/Vd4yh3sTuZ0lHS7AOBAMVicde5Lm6HKloVhoaG\nHnrrqPQVK4HS00tFAHKaNteHEYK9desWlUqFixcvcvLkSZrNpuhKkiQJg8GwbSNFOBzuc0p80F3s\netLpNLIsi4GEvbI1BlW0Kh+CQ4cO0Wg0etap0TLaTm9Q/la7QL5axuFwMDMzg9Vq5dChQ8RiMd58\n802sVis3b97EZDKJcOuNFAqFvrG9B93FbqRYLCJJElarlVartWledzejilblA6NkAI2OjmK323k1\n0ft4WtciHAqRy+UwGAxYLBZ8Ph9er5ehoSEmJiaw2+0UCgWCwSDBYJBoNNrnlVypVPryiVZWVvoS\nCe5nT1MsFimXywQCAfG5eykLVxWtygdmeHiYQ4cOcePGDQKBAMP5Fvq2TFuGt/waMWPbbDYxGo1E\nIhG8Xi+SJIls3NOnT+N0OkXSQTQa5cqVK2LSSFlhA4GA6JpKpVL3PZ8uLy8zPT3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      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.set_title(\"Naive triplet sampling with %d triplets\"%len(triplets))\n",
      "fig.tight_layout(); fig.savefig(\"/tmp/convergenceComparison.png\", dpi=500)"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Recursive algorithm ... ... TODO"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def clusterassist_recurse(actual_cities, pointmask, current_embedding, current_triplets, depth):\n",
      "    # Gather human labels\n",
      "    # For each human label, recurse into it\n",
      "    recursed_triplets = []\n",
      "    for cluster in xrange(len(set(human_labels))):\n",
      "        next_points = np.arange(len(cities))[human_labels==cluster]\n",
      "        subsetK = K[next_points,:][:,next_points]\n",
      "        subsetTriplets = cities_example_accelerated.random_triplet_subset(subsetK, 2, len(subsetK))\n",
      "        recursed_triplets.append(next_points[subsetTriplets])\n",
      "\n",
      "    if depth=0:\n",
      "        return k\n"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 280,
       "text": [
        "2364"
       ]
      }
     ],
     "prompt_number": 280
    }
   ]
  }
 ]
}