gistfile1.txt

{
 "metadata": {
  "name": "Triplet Which triplets are most useful"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Triplet embedding of all California post offices"
     ]
    },
    {
     "cell_type": "markdown",
     "source": [
      "In this notebook, we explore various strategies of subset triplet\n",
      "embedding (trying to learn a good embedding for a set of datapoints by\n",
      "just using comparison triplets). Specifically, we want to find a good\n",
      "way of learning a nice embedding by using as few triplets as we can\n",
      "get away with.\n",
      "\n",
      "We should be using perceptual data (eg. a vision task), but until\n",
      "then, I'm trying this out on all post offices in CA, a list obtained from \n",
      "the US Board of Geographic Names:\n",
      "http://geonames.usgs.gov/domestic/download_data.htm \n",
      "\n",
      "I'm also trying something new: running these in parallel. This might\n",
      "likely be a very bad idea."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%cd ~/batchembedding\n",
      "import pyximport; pyximport.install()\n",
      "import cities_example"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cities = cities_example.load_cities()\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)\n",
      "fig.savefig(\"/home/michael/cities/cali.eps\")\n",
      "print \"There are\", len(cities),\" cities\""
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "There are 1182  cities\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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IkZMVHg8PD6fTp0+XK/aKFT9TcHAwERGFhISUuAmCn58f1a1rQgcPHix07P79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\nEWSMaTUugowxrfZ/c01WDf8vC38AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numpy.savetxt(\"/tmp/CA-cities.csv\", cities, delimiter=\",\")"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "MDS pairwise city distances:"
     ]
    },
    {
     "cell_type": "markdown",
     "source": [
      "Multidimensional scaling learns an embedding using a *pairwise*\n",
      "dissimilarity matrix, where K[a,b] == distance between a and b."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn.metrics\n",
      "K = sklearn.metrics.euclidean_distances(cities,cities)\n",
      "# Fix numerical instability:\n",
      "K = (K + K.T) / 2"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Run MDS\n",
      "import sklearn.manifold\n",
      "mds = sklearn.manifold.MDS(dissimilarity=\"precomputed\",\n",
      "                           n_components=2,\n",
      "                           random_state=25)\n",
      "new_cities = mds.fit_transform(K)"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Graph the found points.\n",
      "import cv2\n",
      "st = SimilarityTransform()\n",
      "st.estimate(new_cities,cities)\n",
      "new_cities_mapped = st(new_cities)\n",
      "fig,(ax1,ax2) = subplots(1,2,sharex=True,sharey=True,figsize=(10,8)) \n",
      "ax1.set_ylim(31,43)\n",
      "ax1.set_xlim(-126,-112)\n",
      "ax1.scatter(cities[:,0],cities[:,1],s=0.1,color='b') \n",
      "ax2.scatter(new_cities_mapped[:,0],new_cities_mapped[:,1],s=0.1,color='r') \n",
      "avg_loss(cities, new_cities_mapped)\n",
      "fig.suptitle(\"Post offices in California, MDS on all pairs\",fontsize=18)\n",
      "fig.savefig(\"/home/michael/cities/cali-mds-cities.eps\")"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Triplet embedding, with a random subset of triplets:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from cities_example_accelerated import random_triplet_subset"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit (random_triplet_subset(K, 100, len(cities)))"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 23.4 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(cities_example)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<module 'cities_example' from 'cities_example.py'>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(t,a, fig, ax1, ax2) = cities_example.run_experiment(random_triplet_subset(cities_example.K,300,len(cities)), save_figure=True)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Triplet count: 354600 which is 0.00129163736563\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10  error is  131527.587606 , number of constraints:  0.057687535251\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20  error is  112202.82121 , number of constraints:  0.0540750141004\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30  error is  106468.39826 , number of constraints:  0.0399802594473\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40  error is  103478.25521 , number of constraints:  0.036046249295\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50  error is  101593.862889 , number of constraints:  0.034624929498\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 60  error is  100225.690539 , number of constraints:  0.0338014664411\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 70  error is  99162.9791308 , number of constraints:  0.0332374506486\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 80  error is  98300.3790339 , number of constraints:  0.0328793006204\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90  error is  97577.4089869 , number of constraints:  0.0327185561196\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100  error is  96956.9237977 , number of constraints:  0.032611393119\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 110  error is  96414.693649 , number of constraints:  0.0325465313029\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 120  error is  95934.0889173 , number of constraints:  0.0324675690919\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 130  error is  95503.2325586 , number of constraints:  0.0324337281444\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 140  error is  95113.3675 , number of constraints:  0.0324280879865\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 150  error is  94757.8655248 , number of constraints:  0.0323998871968\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 160  error is  94431.5977159 , number of constraints:  0.0323745064862\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 170  error is  94130.5195132 , number of constraints:  0.0323491257755\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 180  error is  93851.388608 , number of constraints:  0.0323604060914\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 190  error is  93591.5679925 , number of constraints:  0.0323970671179\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200  error is  93348.8852577 , number of constraints:  0.032461928934\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 210  error is  93121.5300326 , number of constraints:  0.0325042301184\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 220  error is  92907.9778805 , number of constraints:  0.0325239706712\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 230  error is  92706.9329262 , number of constraints:  0.0325578116187\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 240  error is  92517.2839881 , number of constraints:  0.0325719120135\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 250  error is  92338.0706031 , number of constraints:  0.0325803722504\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 260  error is  92168.4564135 , number of constraints:  0.0326170332769\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 270  error is  92030.662165 , number of constraints:  0.0334884376763\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 280  error is  91952.9065159 , number of constraints:  0.0326254935138\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 290  error is  91911.6447283 , number of constraints:  0.0326311336717\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 300  error is  91867.9584049 , number of constraints:  0.0326452340666\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 310  error is  91821.8415477 , number of constraints:  0.0326339537507\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 320  error is  91773.3105534 , number of constraints:  0.0326311336717\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 330  error is  91722.4032984 , number of constraints:  0.0326395939086\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 340  error is  91669.1798747 , number of constraints:  0.0326424139876\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 350  error is  91613.7229188 , number of constraints:  0.0326480541455\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 360  error is  91556.137421 , number of constraints:  0.0326593344614\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 370  error is  91496.5499851 , number of constraints:  0.0326818950931\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 380  error is  91435.1075367 , number of constraints:  0.0326903553299\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 390  error is  91371.9755104 , number of constraints:  0.0327072758037\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400  error is  91307.3355644 , number of constraints:  0.0327354765933\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 410  error is  91241.3829027 , number of constraints:  0.0327721376199\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 420  error is  91174.3232947 , number of constraints:  0.0328087986464\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 430  error is  91106.3701543 , number of constraints:  0.0328313592781\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 440  error is  91047.1347813 , number of constraints:  0.0328736604625\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 450  error is  91028.5737206 , number of constraints:  0.0328680203046\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 460  error is  91009.4834301 , number of constraints:  0.0328764805415\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 470  error is  90988.9603827 , number of constraints:  0.0328962210942\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 480  error is  90966.9433453 , number of constraints:  0.0328990411732\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 490  error is  90943.3794131 , number of constraints:  0.0329272419628\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 500  error is  90918.2231608 , number of constraints:  0.0329328821207\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 510  error is  90891.4379688 , number of constraints:  0.0329385222786\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 520  error is  90862.9974537 , number of constraints:  0.0329639029893\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 530  error is  90832.8868524 , number of constraints:  0.0329639029893\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 540  error is  90801.104287 , number of constraints:  0.0329610829103\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 550  error is  90767.6618513 , number of constraints:  0.0329751833051\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 560  error is  90732.5864603 , number of constraints:  0.0329582628314\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 570  error is  90695.9204096 , number of constraints:  0.0329949238579\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 580  error is  90657.7216016 , number of constraints:  0.0330231246475\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 590  error is  90618.3192287 , number of constraints:  0.0330541455161\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600  error is  90597.6633545 , number of constraints:  0.0330513254371\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 610  error is  90586.6591583 , number of constraints:  0.0330513254371\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 620  error is  90574.7373267 , number of constraints:  0.0330400451213\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 630  error is  90561.8365404 , number of constraints:  0.0330456852792\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 640  error is  90547.9015201 , number of constraints:  0.0330372250423\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 650  error is  90532.878532 , number of constraints:  0.0330287648054\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 660  error is  90516.7161801 , number of constraints:  0.0330456852792\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 670  error is  90499.366402 , number of constraints:  0.0330428652002\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 680  error is  90480.7855542 , number of constraints:  0.0330710659898\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 690  error is  90460.93556 , number of constraints:  0.0330795262267\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 700  error is  90439.78509 , number of constraints:  0.0330710659898\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 710  error is  90417.31074 , number of constraints:  0.0330710659898\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720  error is  90393.4981683 , number of constraints:  0.0330682459109\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 730  error is  90368.3431466 , number of constraints:  0.0330795262267\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 740  error is  90341.9090383 , number of constraints:  0.0331020868584\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 750  error is  90326.0116039 , number of constraints:  0.0330936266215\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 760  error is  90318.55324 , number of constraints:  0.0331049069374\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 770  error is  90310.4536699 , number of constraints:  0.0330851663847\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 780  error is  90301.6504005 , number of constraints:  0.0330936266215\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 790  error is  90292.096041 , number of constraints:  0.0331049069374\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800  error is  90281.7430038 , number of constraints:  0.0331077270164\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 810  error is  90270.5437522 , number of constraints:  0.0331020868584\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 820  error is  90258.4514018 , number of constraints:  0.0330992667795\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 830  error is  90245.4204294 , number of constraints:  0.0331105470953\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 840  error is  90231.4074721 , number of constraints:  0.0331133671743\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 850  error is  90216.3722053 , number of constraints:  0.0331020868584\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 860  error is  90200.2782871 , number of constraints:  0.0330992667795\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 870  error is  90183.0943483 , number of constraints:  0.0331077270164\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 880  error is  90164.7950053 , number of constraints:  0.0331105470953\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 890  error is  90145.4392161 , number of constraints:  0.0331415679639\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 900  error is  90135.1976666 , number of constraints:  0.033133107727\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 910  error is  90129.6599373 , number of constraints:  0.0331500282008\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 920  error is  90123.6204521 , number of constraints:  0.0331472081218\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 930  error is  90117.0387406 , number of constraints:  0.0331556683587\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 940  error is  90109.8751548 , number of constraints:  0.0331613085166\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 950  error is  90102.0889392 , number of constraints:  0.0331725888325\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 960  error is  90093.6385093 , number of constraints:  0.0331810490694\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 970  error is  90084.4818417 , number of constraints:  0.0332036097011\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 980  error is  90074.5769432 , number of constraints:  0.0331923293852\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 990  error is  90063.8824016 , number of constraints:  0.0331923293852\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1000  error is  90052.3580167 , number of constraints:  0.0332007896221\n",
        "Average loss is:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.260327340372 \u00b0. \u03c3= 0.242061272117\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "cities_example.py:74: 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",
        "  if -1 in triplets.astype('i32'):\n",
        "cities_example.py:77: 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",
        "  triplet_cities = tste.tste(triplets.astype('i32'), 2, 0, 1, 1,verbose=verbose)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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KpRLt27cXm6xZ7xBYvyfeeOMNABBrW2316tVL/Nzefffd2LRpE2bNmoXAwEDo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WTz6Bc/JkpGi1GFNbi5aWFimxvfbaiJPjzs5O7Ny5E+3t7VAoFKioqAj9SEtLg9ls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\nRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig.suptitle(\"\")\n",
      "fig.savefig(\"/tmp/example-homography.pdf\",dpi=100)"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Large-scale experiments w/ clusters and plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.parallel import Client\n",
      "rc = Client() \n",
      "print len(rc),\"engines\"\n",
      "lv = rc.load_balanced_view()"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4 engines\n"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set up our parallel clusters\n",
      "import numpy as np\n",
      "from matplotlib.pylab import *\n",
      "\n",
      "%cd ~/batchembedding\n",
      "import pyximport; pyximport.install()\n",
      "import cities_example\n",
      "import cities_example_accelerated"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/python2.7/site-packages/scipy/lib/_util.py:35: 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:35: 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:35: 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:35: 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:35: 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:35: 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:35: 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:35: DeprecationWarning: Module scipy.linalg.blas.fblas is deprecated, use scipy.linalg.blas instead\n",
        "  DeprecationWarning)\n",
        "/usr/lib/python2.7/site-packages/theano/scan_module/scan_perform_ext.py:85: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility\n",
        "  from scan_perform.scan_perform import *\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/store/home/michael/batchembedding\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import IPython\n",
      "def results_so_far(asr_set):\n",
      "    # Return the accumulated results of all our runs so far.\n",
      "    if any([isinstance(x, IPython.parallel.RemoteError)\n",
      "            for asr in asr_set\n",
      "            for x in map(rc.results.get, asr.msg_ids)]):\n",
      "        print \"Warning: Some engines report errors\"\n",
      "    return [[x[0] for x in map(rc.results.get, asr.msg_ids)\n",
      "             if x and not isinstance(x,IPython.parallel.RemoteError)] # len==2 means the second thing is an EngineError\n",
      "            for asr in asr_set]\n",
      "def progress(asr_set):\n",
      "    for i,asr in enumerate(asr_set):\n",
      "        print \"Run\",i+1,\":\",\n",
      "        if asr.progress==len(asr.msg_ids):\n",
      "            print \"done\"\n",
      "        else:\n",
      "            print \"%d/%d, %d sec\" % (asr.progress, len(asr.msg_ids), asr.elapsed)\n",
      "def progress_interactive(asr_set):\n",
      "    while True:\n",
      "        from IPython.display import clear_output; clear_output()\n",
      "        progress(asr_set)\n",
      "        import sys; sys.stdout.flush()\n",
      "        import time; time.sleep(10)\n"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Generates a set of triples. For each consecutive experiment, the\n",
      "# triples used are a *superset* of the triples used in older\n",
      "# experiments. This helps keep the graph less jittery because we're\n",
      "# only *adding* triples instead of replacing them each step!\n",
      "triplet_lengths = [1, 3, 5, 7, 10, 15, 20, 50, 100, 200, 500, 1000, 3000, 5000] \n",
      "triplet_lengths = [3, 10, 20, 100, 1000] "
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "source": [
      "The plot_avgloss function plots several loss curves."
     ]
    },
    {
     "cell_type": "markdown",
     "source": [
      "It's also useful to have a plot that sweeps through the loss and shows\n",
      "a sample embedding of triplets at that loss. This function does just\n",
      "that."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Experiment 1: Randomly sample triplets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "random_runs = [lv.map(lambda c: cities_example.run_experiment(\n",
      "                        cities_example_accelerated.random_triplet_subset(cities_example.K, c, len(cities_example.cities)),\n",
      "                        save_triplets=True),\n",
      "                      triplet_lengths)\n",
      "               for run in xrange(3)]"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "progress(random_runs)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Run 1 : done\n",
        "Run 2 : done\n",
        "Run 3 : done\n"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cities_example.plot_avgloss(results_so_far(random_runs), \"Randomly sampled triplets\") \n",
      "#savefig(\"/home/michael/cities/random_triplet_sampling_figs/loss.eps\")"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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UVKQZwvabb4CAAKmjISIiOWAL3gw4OmouQvOnP0kdCRER0W+Y4A1g9mzg0iXg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\nkxj1Jc36PmeMeY8m7Ya0ZuX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      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cities_example.plot_runs_sweeping_loss(results_so_far(random_runs), \"Randomly sampled triplets\",\n",
      "                                       \"/tmp/test-%s.png\")\n",
      "                        #\"/home/michael/cities/random_triplet_sampling_figs/triplets-%s.png\") "
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/python2.7/site-packages/matplotlib/axes.py:4747: UserWarning: No labeled objects found. Use label='...' kwarg on individual plots.\n",
        "  warnings.warn(\"No labeled objects found. \"\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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9+vRRhk6GhYVh2bJlapolQPPZrF69Go0aNUKlSpXQt29flC9fHrGxsbh79y62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\nHnDsAAAAAElFTkSuQmCC\n"
      },
      {
       "output_type": "display_data",
       "png": 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48ePh5OSEn3/+WWcuDn3IK4ZPPvkEAwcOhLW1NerUqWOUql8fXl5eCAoKwuXL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Bw+rHZriIhIixgsEB57DJg/X6o8njqldmuIiEhrmLNAAICnnpL1I7p3l4Wn6tRRu0VERKQVDBYo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\nqk+SiMiJZ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      },
      {
       "output_type": "display_data",
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FgwYNcOzYMaxatQp9+/bFu+++q6xbvnx5DBs2DEuXLkX9+vXRu3dvWFtbY+/e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\nj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      },
      {
       "output_type": "display_data",
       "png": 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REhKCsmXLYsSIEQavbd++PXr37o25c+fiwoULaNmyJdzc3PD48WOcOnUKd+/e\nxd27dwGQD0+JEiWwYcMG+Pj4oECBArC3t8fnn3+Opk2bwsXFBfXq1YOXlxeio6OVeTC6d++uvF94\neDgCAwPRs2dPvRkLZdzc3DB58mR89913aNy4MTp37ownT55gxowZKFu2rFa+gjlz5mDSpEnKMF+Z\nP/74AwEBAahfvz6GDx8OIYRykVRP4ezo6Ij27dtrjcPV1RUAULFiRQ2NQnh4OAYOHIgOHTrAx8cH\nDg4OuH//PlavXo2nT59izpw5ymsBmiOff/45Zs6ciZiYGNSqVQunTp3C8uXL0b17d9SpU0d57u3b\nt9GiRQu0bdsWJUqUgJ2dHS5fvozly5fDwsICixcv1hhjv379EBcXhzp16sDT0xMvXrzA1q1bceHC\nBbRp0wYdOnTQ+RnHxsZi2LBhmDRpktK8AUDpe9S2bVt069YNa9asgbOzM1q2bKk8Z8WKFUaHF371\n1Vc4cOAAvv/+exw6dAgBAQFwdHTEw4cPERYWBhsbGxw6dMhgH+lRqlQpfP/99/j111/RoEEDdOrU\nCXFxcVi8eDHi4+Oxfv16ozdXGQmdBIz/jgEUAqlrDixatAi7d+9GgwYNkC9fPty6dQtLliyBpaUl\nNm/erBXuv2vXLmU66YiICGWorxACLi4uGDp0KADKGeLp6Yl27dqhfPnycHd3V/oUnD9/Hp06ddLS\nWhjbNwA0adIEFy9exJdffomXL19q5QupW7euUrDt3LkzZs2ahQULFihD5+Pj47FmzRpcvXoVo0aN\nMhxmnZ53ZXreyDdv3hRmZmYanuJLliwR5cqVE9bW1qJQoUJi4MCBysiC3r17K88z5OEdFBQkFAqF\nluf6tm3bRJUqVYSVlZUoUqSIGD9+vDhw4IDO7HhRUVFiyJAhwtPTU1hYWAgvLy8xbNgwjdBLIciz\nX6FQiCNHjmiNI+2YZXr16qXlperv7683S+GDBw+EQqEQTk5OOsMcdfH+/Xvxww8/CF9fX5EvXz5h\nZWUlihUrJvr27SsiIiI0zj169KioV6+ecHBwEM7OzuLzzz8X169f1zmmokWLann2C6H/M9f1f5Kj\nIcLCwsSECRNEkSJFhJWVlahUqZJYv369Vt/6PpsHDx6IQYMGiaJFiwpLS0uRL18+UaNGDTF27Fjx\n+PFjrfdYp04dYW1tLQoWLCiGDRsmrl27ZnQ0hBBC/Prrr6J48eLCwsJCIzxrwoQJOt+7jK7PTI6Q\nuHjxoghcZ08/AAAgAElEQVQMDBR2dnbC1dVV9OjRQzx//lzj3MOHDwuFQqEzg+Pq1atF/fr1haOj\no7C2thbFihUT7du31wgzFoK8qevWrav00JY/zyVLlogmTZqIggULCktLS1GoUCHRsmVLER4ernG9\nHAo8btw4oz4rISiMrXLlysLa2lq4u7uLvn37aoXUCaH67uh6f6dOnRKNGjUS9vb2wsHBQTRv3lxc\nvHjRqPvrW3/u3r0r+vXrJ8qVKyecnJyEhYWFKFy4sGjfvr3OeSwEhXSPGzdOFC1aVFhZWQkfHx8x\nZcoUkZycrHHes2fPRPfu3UWZMmWEo6OjsLCwEN7e3qJXr17in3/+0ep32bJlwt/fX/n5Ozg4iNq1\na2uFbadl8ODBokaNGjojWM6cOSN8fX2Fvb298PX1FWfOnNE4Loczp43g0kdycrKYPXu2qFmzprCz\nsxN2dnaiVKlSolu3bhqhmoa+p8ZkqlyyZImoWrWqsLa2VmZQPH78uNZ5+tZVITIWOilj7HcsPDxc\n572PHTsmAgICRP78+YWVlZUoWrSoGDp0qN7sxOrhrwqFQigUCuXf6uvc+/fvxXfffSd8fX2Fm5ub\nMDc3F66uriIgIECEhIRkqW8hVCGouqIVdf0fY2JixHfffSdKlCghLC0thZ2dnfDz8zPqeyQJkYse\nhJ8wkZGRKFKkCPr164cFCxbk9XCyjLyzCQ8PR4MGDfJ6OHlC0aJFUbx48SzvynKLb775BqtWrUJE\nRIRWzQLmw6Jdu3a4fv06bty4kSUHbIYxliyZIRjjWbBgAVJSUrQyOTJMbrF//378+OOPLCh84CQn\nJ+Pw4cNYvnw5CwpMrsHCQg6zYcMGPHz4EMHBwWjevHm25ntnmIxw7dq1vB4Ckw2Ym5tn2nGVYTIL\nCws5TJcuXWBjY4MGDRpoJGb6GMipMKwPhU/9/TMM8+nAPgsMwzAMwxgka9kgGIZhGIb56GFhgWEY\nhmEYg7CwwDAMwzCMQVhYYBiGYRjGICwsMAzDMAxjEBYWGIZhGIYxCAsLDMMwDMMYhIUFhmEYhmEM\nwsICwzAMwzAGYWGBYRiGYRiDsLDAMAzDMIxBWFhgGIZhGMYgLCwwDMMwDGMQFhYYhmEYhjEICwsM\nwzAMwxiEhQWGYRiGYQzCwgLDMAzDMAZhYYFhGIZhGIOwsMAwDMMwjEFYWGAYhmEYxiAsLDAMwzAM\nYxAWFhiGYRiGMQgLCwzDMAzDGISFBYZhGIZhDMLCAsMwDMMwBmFhgWEYhmEYg7CwwDAMwzCMQczz\negCZpUqVKrh8+XJeD4NhTJ7KlSvj0qVLeT0MvfBcZhjjyMu5/MFqFi5fvgwhRK6+JkyYwPfke35w\n9zT1BzHPZb4n39O4V17O5Q9WWGAYhmEYJndgYYFhGIZhGIOwsJAB/P39+Z58zw/unow2n8r/nu/5\ncd0zL5GEECKvB5EZJEnCBzp0hslVTH2umPr4GMZUyMu5wpoFhmEYhmEMwsICwzAMwzAGYWGBYRiG\nYRiDsLDAMAzDMIxBWFhgGIZhGMYgLCwwDMMwDGMQFhYYhmEYhjEICwsMwzAMwxiEhQWGYRiGYQzC\nwgLDMAzDMAZhYYFhGIZhGIOwsMAwDJMdbN0K7NiR16NgmBzBPK8HwDAM81Hg5QVIUl6PgmFyBK46\nyTAfOaY+V0x9fAxjKnDVSYZhGIZhTBYWFhiGYRiGMQgLCwzDMAzDGISFBRMnNpZeOcHr18B33wH/\n/Zcz/TMMwzAfBx90NERQUJDyd39/f/j7++fZWHKK5cvp59dfa7ZHRABTpwJz5gC2tpnr29ERqFMH\nyJcva2NkTIvw8HCEh4fn9TAyxKcwlxkmo5jSXOZoiDxk1iygVi2gdm3958TH08+0AkFKCnDyJFC/\nfs6Nj/k4MPW5YurjYxhTgaMhPhG2bQOOHlX9XbYshWYbwtZWt+bAzCz7BAUhgJUrgaio7OmPYRiG\n+bhgYSEb+e8/4M0b/cdtbQFra9XfzZsDnp76z58+HTh+PPvGpw8h0h87wzAM8+nCZggDJCXRTwsL\n486fPBkoXBjo0yd77n/wIFC5MpA/f/b0x3yamLqa39THxzCmQl7OFRYWDDB7Nv0cMcK48+PjAUtL\nwDwdt1EhgA0bgHLlgFevgICArI2TYQxh6g9jUx8fw5gKeTlXPuhoiJymS5eMnW9sVEJKCnDnDv3+\n8iULCwzzyXD1KqkMR47M65EwTIZgYcEAbm4506+5OTB+PPDoEXDtWtb7O3UKSEhgoYNhTJ58+Qw7\nKjGMicIOjrmEEMCKFUBkpKrtyRPgn3+y3ndsLBATk/V+GIbJYTw8gI4d83oUDJNhWLOQS0RFkclB\nzpsAUI6FWrWy3nezZlnvg2GYXOL0acqq1q1bXo+EYYyGNQu5xPjxQKVKgI+Pqu3pU2DHjrwbk8y9\ne8DixXk9Cob5RLC1BZyc8noUDJMhWLOQS0yapO0D8fw5cPcukJycfgRFdrF3L61VDRuq2iQJULDY\nyDC5Q6VK9GKYDwh+ROQSa9cCu3ZptlWpAgweTNrIvXuz/56XL1OIpjqpqeQ/oU6xYkC/ftl/f4b5\nZHj3Dhg7Fnj4UP8516+zcxHzwcLCQi7RsCFQo4Z2u60tFXMKC8v+ewqhLRi0bAlwjR6GyWasrUn6\nNxRCtWkTcPhw7o2JYbIRTspkIuSmKYL5tDD1uWLq48s2hCCbH8NkEi4k9Ynw7Blw5IjuY9HR2mYK\nhmE+IlhQYD5gWFjIRW7eBM6e1X3swQNK7gZQgqVPYaPFMJ8k8+dT+CTDfECwGcKEePeOilYFBZGz\ndKdO2X+P5GQynbZvD1hZZX//jOlh6nPF1MeXaWJjAQcHlUbhzRv6/cgRoHRpzThqhjECrg3BAABm\nzAAKFQL69896pclTpyh/Qteumu1v3wI3bgBNmnA1S4bJNhITgZkzgd69gYIFqW3YMMqnMHs2CQmL\nF5NjkrGV6RjGhGBhwURITgb++w/47DPA2zvr/enL++LkBEyZkvX+GYZRw9wccHEhO2OrVhSz7O4O\nFC2qOmfAAPZbYD5Y2AxhQoSGAo0bA3Z2mbt+6VJyohw3LnvHxXzYmPpcMfXxGSQ4mCbs4MHkdLRk\nCTBrFvD4MRAXB1SokNcjZD4i2AzBAABat87a9RcvZo9WgmEYI4mKAk6epEqSf/9NJgdAcyJu20aZ\nz6pWzZsxMkw2wMLCB0xiIv2UHRXnzmUtJ8PkKj/+SJqEUqXImVEXSUlkY3z8mISKw4cplWqjRnT8\n+HESLry8cm/cDJNBOHQyD9i8mSpQZpWFC+klw4ICw+Qy27fTxCtdWn9qVEtL4MIFmvgAOSglJ6uO\nnzuXPbXqGSYHYZ+FdIiMBHbvptoJaR/Gd+8Ct25RCmVjEQL45RegXTugbNnMjys1VZVm3sUl8/0w\nHz+m7hNgkuO7fBl4/x6oWdPweUlJdJ6dHfDkCfDTT0Dx4sCgQeRNbGEBzJtHteirVuWKbUyWYJ8F\nEyYpCYiPJ5W/JGnmJnjxgsyV1auroqXSQ5JIc5lZXr2iTcjKlcCCBaxNYJgc4d49mvjpCQsWFiQk\nnDoFlCtHKkN7e+C33ygOOl8+YOjQ3Bkzw+QgrFlIh/fvabNgbk55Cb7+WvN4cDCZGzt2zJn7x8aS\nFtPamv4eNIgcIQsUICGFYdLDJHfuapj6+NLl+HHyLh4+XNX26hX5KGzeDBQuTBOXYbJIXs4VFhbS\n4d494IcfSIM4YADg6prjt9Rg2jQyMwwcSH8/fUpaDNZmMsZi6g9jUx9floiIAO7cAVq0SP/cuDjg\nyhWgbt2cHxfzQcLCQibIjQ/t0SNg5Ejg999pJ29pafy1CQnAli1A585ZqyYZE0P3tbHRbF+5kn72\n7Jn5vplPA1N/GJv6+DLF3bsU3SAvGqmpmhL+X38BZcpoJm0KDwcOHAB+/jk3R8p8QHDVSRPFywtY\ntoyinYwVFKKiqLbD06fkW7BmDflK7duXuTE4OWkLCgDg50cvhmFMkDlzgP37afLPnw80bAhcukTH\nnj2jcMu7d1XnnzwJODuzoMCYLCwspIOulMmGsLen/CteXsDo0SQw/Psv8PAhmTZnzcqecZUpQy+G\nYfIAIchPQQhg8mR6qR+bOpXCKadPB+rUodTPL17QcWdnoFcvzVDLBw9oh8EwJgoLCwY4dw5IScnY\nNTY2ZBqwsCDBYepUKi7n4UEax+zM/rphA21SGIbJZZ48oYiH169J9RgdraorHxgIrFoFlCxJP0uX\nBhwdgSJF6Li1NfDll4CZmaq/L7+kwjAMY6KwsKCHxETSHhqbK2XePPJN0kVcHJknPD2p2qM6t29T\naerM8OQJrVUMw+Qynp4kCLi6UsKUr7+m0tNTplDilcBAOs/cnGyYgYEUFaHO7t2UrIlhPgA4z4Ie\n9u+nok7lyhl3vrs7bR5kIiIopNLCAqhdm166WLaM1pL8+TNeufbbbzN2PsMw2cSyZUDFioCvL+VY\nKFNG5USUNpOjmRnQrZt2H2/fZn6nwDC5DEdD6OHiRYpo0PeQT48RI2iD0ayZ4fOSkshU+fx5+vlf\nUlNJiGnalEMnGeMx9WgDUx+fTnbuJDNDRtOwCgFs3UqLgy7P5bQcOwa4uWUt3Svz0cDRECZI1aqZ\nFxQA8mtKT1AASPPg7Z2+oABQnpfdu8mkwTBMHtKqlfYD/P59ld+CPhISgPPnaXeQHu/fU3TEmTOZ\nHyfDZBOsWTAR4uIon8PQobmf+In5uDH1uWLq4zOKd+8owmHSJHJozCq7d6tMHE2bGqeFYD56WLPA\nwMqKMjWq157ITv76Cxg/Pmf6ZphPHhsbYMkSw4JCaCiVp07L8uWUZ0Gda9coK1zr1iwoMCYBCwvZ\nSGoqFYm6fj3j11pa0tqwd2/2jwugUO9WrbTb5XBxhmGyiLqHsy5sbFRFXtTx8NBO6DJmDBd/YUwK\nNkNkMzt3AgEBgINDxq99+pR8mTKSVlrm1CmqQFmrFgktT59SdFd6PHgAjB1LYaIZTUDFfBiY6lyR\nMfXxKUlMpFBI9fwIOcWxY2TaaNo05+/FfDCwGeIjolWrzAkKAG0wMiMoAMDs2ZRhFiBNp3pCOUN4\ne1OdCRYUGCYdZs0CQkKMP/+vv6i4S2ZISiIHR4YxEViz8JGQkkLlrJ2cSMMQE0NZZRnG1OeKqY9P\nybNnZEowVrIOCqL6D5UqAd9/n7l7JicDc+dShkd398z1wXw0sGaBAUD+A5lN32xmBvzyC7BjBwkL\nLCgwTDZTsGDGVHBBQZS10Rh7oDoPHlAY5uPHNJktLDixCpPnsGbBhLh8mWpJrF5N60NGiYwknwcL\nC+C//8jkqV4Bl/k0MfW5YurjM4pLlyjd81dfqdp276YdwBdfqNpu36bKcoZ8EYKDgW3bKDxqxgyu\nGMcoYc0CA4C0lXPmZE5QAIBChVTX7t5NGWk/9DWYYfKU06cpLXN6uLlRuGN4uKrtxg1yiFTn+XNV\nGdqrV3X3NWIE0KULpY2Oj8/kwBkme2FhIROMGEHRAykpZFLMLiSJakSkx5UrwMSJhs/p3h24c4e0\nFQzDZJIdO6j8bHp4egJdu6oqSz56RKo+2c9ACNIW1KgBDBhAWRl15VwAyMt52DDyczAzo/KyDJPH\ncCGpTODmRuHSS5bQpiO7CzodPw6EhVESJUnSPu7lRemoDWFpCSxaxFEODJNh3r+nktMFCgDTphl/\nnXoBqYMHgRIlgGrV6O+EBBIQqlUj22DHjsb3y+pBxgRgn4Us8OIFRTgVKpS9/YaEALt2AZs2aWsx\nGSajmMJcMYTJjW/DBpUDUV7z9CmwfTsweDA5Oaak0E6iVq30k0AxHx15OVc+6EdRUFCQ8nd/f3/4\npy0Nm8O4uWV/n0lJVJCud2/jrzl2jLQRP/yQ/eNhPjzCw8MRrm47/wDI67msQYcOQPPmeXd/dR48\nANatA3r0IO3EyJGk8XB2ptoRzEeNKc1l1iyYGJs2ARcuZEz7+fIlOWM3apRz42I+XEx9rpj6+HKU\npCRSH+qyNwKkSTh6lNLC7t1LWSRbt9Z/PvNRk5dzhYUFEyMlhSpQcp4EJrsw9bli6uPLUSZMoFLX\nnTvrPv7oETkpAcC4ceQBrR6eyXxScOgko8TMLH1B4elT4PXrzN/j+XPK98IwTB4QGUk///kHKF+e\n7I5p+eEHoFkzMjscOEAahYoVyRTBMHkACwvZyKFDlHI5p1m9Gti6NfPXh4YCGzdm33gYhjGSiAiK\nvX77lvwRXr/WXUymfXugVy8KudqyhRIzBQdnPgkLw2QRNkNkIxMm0GagTp3M97F7N0VutWtHSZUC\nA2ktefiQoq4OHqSNxsCBQPHimbtHaipFY+VG8Twm7zHFuaKOqY8v2wgLI7NCy5bGJVSRef+ekjQ9\neEC5F7y8gHz5qL1ChZwbL2NycDTER0J6iZKMwd1dVWwuOZke7CdPAtevk7BQqxZpIkeNAhYvBlxd\nM34PTjPPMHmAuzvw5In+srTLl5MjY2QkULOmSotgbk67B3t7+n3nTkqk8uIFMG9e7o2f+aThx0Yu\nExVFSeH0UbMmULcu/T5wIODjQ2Wv5bBIe3vg889J65AZQQEgB8px4zJftIphmExQoQKFLR06pPt4\ncjLtFFasAG7dUrXPnElqS3t7YOFCqhdx/z5N4NywezIMWLOQ6zx5Aty8CbRpQ/M9IYGcoWX+/JMi\nItRrz+giK5kZbWxo8xIXR4X0GIbJIVJSKMxRVudNm0ZaAV0MGEC+DIsXa7Z7eABVqtB1xYoBtrbA\nb79RghV7+5wdP8P8P6xZyGWqVFFpCU6coEJ16jg7Z2/Y5K1blLkWoKyQUVHkq1C6NPlLZSWqgmGY\ndPj9dzIvyKQVFIQgzUFEBO0kevWixCnqWFnR6/ffKbukiwuZIk6epNAmhskFWLOQh3Trpt2WL5/K\n8XDfPqBePcDOTvOc1FTSPhojVGzcSNrP9u3J78HDg3yrRo6k9YYzxjJMDtKpE0nk0dFATAxNPltb\n1fEjRygs0s6O8sZPnUqLAEAT/cgR8ldwcaG0rgULUk6GWbMohvraNWD//rx5b8wnBUdD5ADXrlFq\n+a5dM37tsmUkLPTsSRqIzp1JG6FOaCitITNnqtpu36ZNRr16muemprJD46eOKc8VwPTHl2WmTaMU\nzXfvApUrkwAB0CQODibJPTBQ+7rffwf++gtYv57MGb/8QgLF2rW0SPzvf2SK2LSJbIvMRw9ncMwE\nefmhhYaSI6KHh+7jWREWjCE5mcwJhQqRFnPhQtJiliwJ9OsHzJ5N60jhwjlzf+bDwtQfxqY+vkyz\nbh1QqRJQqhQ93JOSyJwQHU2LSJs2VG++QQPg/HkKrRw1SnX9sWOkPqxYkf5euhRYtQq4ehVYsIBK\nXB84APz0Ezk/DR6cN++TyTU4g+MHxt27uiMJrlyhhEkVKmSPoLBzJ/Dff6q/d+0CNm+m6Cm50mVM\nDBWRKlECGDSItAh2dvp9qBiGySXkhCaWliQsWFuTs+ObN+RhbG9PUv6mTSQsHD6sunbBAjqvZElV\nm5cX7VDatKGEK2fPAl9+SVoHH5/cf3/MJwULCxkkOZmE/RIltI+ZmWWspHRICFWf1cetW+TzJJOY\nSOYGdZydgfnzSUgIDiZhpWLFjOV8YRgmGwkLI/Vet24qrYA6Xl5kYzQ3B6pXJ+1D//7kYLR2LQkJ\nT5/SZN65U3Xdvn1AeDiFX0ZF0a4lJIR+b9o0194e82nCDo4ZJDmZUrrHxmo7B5YvTy9jqVhR09cp\nLeoaSYAEFF1h1XfukBDh60v5XvTlfFHn5k1g5UrSUJQpQxqMHj2MHzvDMHooUcL4tMyVKwPx8fS7\npSVN5sePSYPQuDFQv77q3N9+IxOGtzepE62tScW5YgVN4rZts/2tMIwMaxYyiLU1mR4vXNB/jhDA\nv/+m31eNGkC5cpptly7p77tKFdqUREVp9zNjBvkpfPYZrVVyuKQ+UlKAe/eAGzdovLIDNsMwWcTb\nm/wQjGX8eNIgWFsDw4cDHTpQbHPaif7oEdkY3dyAoCCVN/OrVxRSuW0bTWaGyQFYWMgEEyZQFkV9\nnDwJdO9OQkVGuX2bNhf6uHBBZYp48YJ8nlJTNc8JCSHThExsLDB0KK01MhUqkKl00SKgeXNKV79i\nBWkX3rzJ+LgZhjGS48eBn3+m3+/dA969o2iIP/4gwaFnT7Ijnj9Px2SCg8kW+ddfFAq1Zw8JB0KQ\nb8PZs6pc8QyTzbCwkAmcnAyHIxYsSDUcZCE/OprmuDF06kQaAoBqTRw4oHl89GhVOuh37yiEW30z\ncfs2mT5Ll1a1OTgArVtTanp9pKZShNbBg7SWMQyTQ5QvTwsEQItFYCA5O+bPTxPR1pZslFOnkpT/\n9dfkPT1tGi0AtWrR35JEr5cvSZB49ow9m5kcg4WFHMDZmcKe16+nv2fNouiItM6J+ti0iXImNGlC\n2V3//ZcEjhkzKBvsxYskfHh5URE69eqRJUvSvaytVW2SRP307asyj6bll1/o+KNHpGlgGCaHcHEB\nGjWi321tKWOaJFFlyRIlNO2QTZuSc9TEiSRQhIYC/v4U/ZCYqNopJCZS/vjZs3P97TCfBiws5AAR\nERTSKPsjfPUVRTtt2ECOhelx/z6ZGOrUoV3+5s0kEFhZUQ6Hbt2oxL0uJIlSzPv5aaZy9vGhcehz\nqGzfnvypuGw1w+QAQpCknx4tWpCDY0QE/e3lReVmZem/QgVqe/pUe7I+eEALB8PkACws5AB+fmR+\nrFmT/nZ1pQd8yZK0qUiP0aNVgkbTpuTTZGcHDBtGEQ979lBmR0OsXk1RWDIKBTlC6uLtWxIk/vor\n/bExDJMJ9u9XFYVJS2qqyvHo8mVaQEqUAHbvJpXf27dkV0xNpfwMzs7kzKi+CLi40K7Czo4LvjA5\nAodO5gKJieQU2b+/8VUeX7ygtSElhcI1ZSQJKFqUfk9NpcRturQF795RCPetWxQeaahKpaUlbWb2\n7CFNKMMw2UxgoGaCJXXmzwdOnaJY6fh4VXy0mxsJGA0akKZh+XLyS3j1imybd+9Sn2/e0ARv2RIo\nXpw0Dt9/n3vvjfkkYM1CDvL0KT3QLS1ps/D0qcq5WV0A0MW6dfTgvn4dGDFCt0Pl1q0UafX0qWb7\nw4fkH+HjQ34TYWGG72VhQTkXhgwx/r0xDJMBLCzoQa6L16/JMWnxYhIUmjShqIYHD8gWCQBnzpDD\nY58+FJpZsSKpCuPiSNtw6xbtJJ4+pXjoW7dy7a0xnwYsLOQgP/5IZaglifKlhIbSnN+6FQgIIL8G\nfQweTM7P6jlZZBISaONQsSLVfzhxQvO4oyNtZAoVIo1Gu3bpj7VmTTJxbNpEwgnDMLnEoEFkB4yL\no+IuLVuSR/Plyyo/h6JFVQmcJImuKVdOVUbWwYH8GuLi6JxLl/L0LTEfHyws5CDBwZpVIH/7jRyZ\nAwJoc3DypP5rLSxo46ArEVxcHOVr8fAAJk2i6AW5VkVsLGk0O3YkbcSmTbRBMYY//6Twb654yzC5\nwNu3pDrMn59ipr28VI6NBQqQv8L166Q5qFuXhIiUFHq9fElRE0WKkMbCyooWBisr0izY2+fte2M+\nOlhYyEHy5aNNgIwkkXbx9GnKu2JtTane9ZkX9+wBxo5V/X38OOVQsLAgs4ZclXbLFkrOdPw4aQXm\nzaMwS4DWlLg448Z77hwwbhxtaBiGyWEOHwbWrKHfQ0JInVi1Kv198CDwzTd0/OJF2h307EmOjj17\nAg0b0mvLFkrQ5OxMi0uJEkCzZrR7uHIl794b89HBDo45TEICOTjKDoZbttCG4cIF8mWIjycHZl00\naqRZsMrCgs51dtasRtujB/XTvTttUtavp79TUsgPQV1g0YcQtCnZtYu0EgzD5DABAVQc6t9/ScuQ\nmEiJTi5coGyMz5+T+nHlSnJAatGCnB4bNyYbY4UK5Al95AiFXMm+D9WqkTqxcGEqUsUw2QBrFnKY\ntWuBX39V5U7p2JG0A3KiNVtbVYhlWqytKevi8OEUrXD0qGbJahkzM4qkcnEhQcTBgbSYdeuSmUId\n9dTQqamUkj45mQSMmzdpLWIYJhewtSUb4sSJZDP08aHIiO++Iw1DqVKU7TE5Gbh6lSZn8eJUivbx\nY6B3b0rgcv069VO/PuWYP3uWfB4CAvL6HTIfEZIQH2blEUmS8CEMPSGBsrV+8QWZHGWWLaMH+hdf\nGL5eCMp/IJesL1BAu+jT6dO0+ejbVzOXwrZt5AN18yZpJW7fJu3kr7/S8devSZgYNYrWrdevVWGZ\nzMeDqc8VUx9fjpKQQOaGrl1Jun/xgpK0nD5NwsHly/Tgd3UlNaGDA2kg6tenyIiSJWnSDxtGpooR\nIygiolgxUkMOHMjahY+IvJwrrFnIYaytyQ8gIkIzi2uFCvrDrgESEjZuJE1kixakjYyJUQkKiYkq\nLYVCQeZLIci3CSBNQVwcaTaCgykKw9lZswy1iwuFa8t5GNIKCoYqYDIMkwHi4siX4OxZEhCCg8mh\nyNqatAlywZcJEyiEElD5Kri6krR//TotHDVqkN/CuHHk4DhsGJ2/axdpJJycKERqxw7jc8wzTDqw\nz0Iu4OlJm4NDh0h72KoVOSgCZG4sXFjTNwEgzeOOHRShsHIlrRF796rqz1hZ0cN+6VLq29mZ1h4/\nP1XWx5s3ySTapQv5P+TLR4LB+/fa9Wb276e1aORIVdudO2SqqFYt5z4bhvkkMDenB3vBgiTd29pS\nm0xcHD3gu3Wjifz6NakS37yhKnDXr9PuompV8m+oXZuqzKknYPHwoEkeEUG7DE9PygKZkECLgKHq\ndwyTDiws5AJCkGbQwYEe2kOH0oZh0yaq9bB3L/Dtt+ScKGNhAaxaReZJgNYL9SyuiYm0Nhw9qnrw\nT5tGD/crV6iEdnQ0mTBcXUmD4OlJ512+DEyZojnGcuW0HS3TOjoePkzrV5EiWf9MGOaTwsYGWLJE\n9d9CKIAAACAASURBVLd6BjQhaFIePUqxy7VrUwKmvXtJWDh0iDI5XrpEyVXKlwfGjKES1uolqeUK\ncElJJFS0bg1MngzMnUtaDfUFhmEyCAsLuYAkAb16kQayalXaPDx4oKpGOXIkbSzSzmULC5VpQK4P\nAZDgUb489ZucTEnefHxIowCo1gy5vkRiIuV3qFqV1p7q1bXH6OmpEib0IVfFZWGBYbKRv/4iFeKc\nOfT3zp2qanKVK9PEbtqU1IqXL1MOhRs3SLWYNuNaUhLtGpycyAEyf37qgwUFJouwXioHCQ8nLeDp\n02RmLFGCTJW1a5Mfgrk5mRhSUnQ/qKOjgWPH6PeUFNpc3LhBpgQvL0rA1L499Z1WU6DO2bNkrgBo\nndGXdTY9vvqKhA6GYbIBIejVpAklWxGC6j+ULk0OinXrkv+CJJHEny8f2QRr1iTNw8GDmn0lJ1Ou\n97FjaYEBSIPRqBEd37qVTBIMkwlYs5CDmJvTpmHnTqr1UKECZVeMiCDBYfhwYPx40hKk9SEAyL/p\n2DFyfDYzIw2CtzetHZ06kZnCwYHSO8vVLHfsIB+ImjVpfTh0iB7wsv9UevzzD5k3HByy7WNgGEYX\ns2eTEGBmRpI4QDsACwt62CsUJDiUKEEmh+fPaUIvW0Yx1W3aqPoKDaWsagUKUJIVucLl8uXA339T\n2NW5c7Sz8PbO/ffKfPCwZiEH+fdf8kEyM6NsjOvXk1lhzRpVnZcuXSib69692tcHBJAwERtLm4Iv\nvlCZIkaPpgq1+fNrCgIpKapcCm/eUNXax49p3dm/X+UDoY/Vq2nMDMPkMK1bA599RovD2bO0C/j6\na1L92drSpN+yBdiwgdSU5cpR/HXRorQ4zJlDmoKnT2lH0KoV+TCoJ25p357yONy4QbsOFhSYTMJ5\nFnKQWbMoouDZM9IQyk7QDx9SToS4OJr/mzdTsqW1a3Vnc/znH9JMjB9Pgoc6O3aQFuDuXeoz7fFZ\ns0jzEBBAJbLfvaN+ZL+GtKSkaPfBfNiY+lwx9fHlOKdPA1WqUBilzNWrQFAQaRDi4+nVuDEwYAAd\nj46mFNHly9MiUKYMxVevWqWtppw+nXYcFSvSDoP5YMnLucLCQi6waBHNdYBK0Y8aRQ/4LVsoCurz\nz0k7MHMmbTRkrl6leV+6tHafmzdT+507JIBcu0aaTHmdkCvcbt5MPlHTp5OJ08KC1hlZ68l8/Jj6\nXDH18eU6f/5JUnv+/JQ74fp1EiQ8PGiRUCcoiMrFNmtGKkrZ+WnyZHJuatSIdikODhSaaWGhf6fA\nmDyclOkjZtcuKvD04gVpGMePpw1CaCjN/evXybdgwQJyUpQr0gJkYtSVFCksjKKskpNJy9iiBflH\nqW8oDh8mLeU339BakZBA1W979tQUFB4+JG2DLv78k9JHMwyTjSQn0+R8/pz+DgujiSizYwc5N9Wq\nRY5Oly5RdENQEGVme/OGkjcNHUqmDH9/Ugeqe0n7+dHO5NYt0ipER5PAwNUomUzCwkIOI1eXTUig\n3b6FBc3/06fJYfHIEfJT8vamqCf1io99+gBffqndp7MzXSMnS4qLI3PD4cOqc5o1o7TO1tYU3j1z\npspPQiY6mgSN8eN1j93Xlxy1GYbJQa5dIwcnGTc32ils20Zmg9GjabFo1YqcIs3MSOsQH091JIKD\nabchExlJJonhw2mCHzlCk/zUKdI2MEwmYGEhhxkwgB7Y5ubAvXvUFhREm4CkJMq/IEnkVzB5MgkS\nAM3r33/X3Wf16uTEeOkSbR4WLiSTQ1rfJSsr0lzY21NFyrRZIseOpWvUy2Cr4+amv8gVQKmmV61K\n7xNgGEYDc3N6kBcoQH9/9RXQoAH9Pn06ZV/r3Zs0AffvU8TEmTO042jYkCIdOnakBWLZMhIoChdW\n9e/gQJNdVjUOHkz929lpZo1kmAzA35xcwMODhH15nrq7kz9CRATN40GDSGBQNyV6epL2UB9Pn5JA\n8eYN8NNPtNmwtVVVuy1fnu7x1Ve0nqSkUJp4OVFTTAyNZ9EiVdhlRqlYkQQegCIt7t+nujUMw2SQ\na9fIHmlvr1oghKDdgIMDLRbFi1MWtzp1SJI3M1OFOrVqperL3p4yv8mYmakcIxkmk7CwkAvICdhk\nLC1JGyAjSdrXeHnRKy1v31KCpb59SWPx+jX5NezfT+bLdesohXz58hSR5ehIQsKTJ7T2lCtHPxMT\n6byslKSWBQ+AorlsbTPfF8N80ly9SupCR0dKlWplRZ7Qsh9C374U8jRgAO0q5JClv/+m37296fyR\nIymda1oSEijHvK5FhWGMgKMhcpF37yhFvExiIq0JGeHtW/JB6NtXlTjp5Enylxo5koSCtm2pPTaW\nBBP1iCyAIiQuXaI09MzHj6nPFVMfX45y8SKp/dRryz95QiaD48fJGVI9+ZI6//xDwkWhQrQDOHCA\nYqQtLFTnnDtHJokDB+heU6fm7PthchSOhvgEiIwEOnSg5Es3b5IZoHdv+l2dxERVUiVd2NlRVIV6\nhsWqVakvX1+VoADQOpJWUAgOJtMBayUZxgS4f58e+qdOqdrOnSO14JEjmqFK+/aR17LMnj2U0REg\n9WTTppqCAkApZE+fpsXnp59y7n0wHz0sLOQSBQsC9eqRBmDiRJr348Zp5lA4d46SrJUvT1la79+n\nTYectAmgiAo5Z4OMjY2mmcMQd++SX8PDh7TOREfT+qMesskwTC7Rrh05//zyC71SU8mZyNqa1IJy\n/Yfly8mbWD1d6zffUBz22rX09+LFFKutzk8/qVJHs52QyQIsLOQSkkSOjGXK0IZgyBAS+BMT6fjG\njRQmaWcHjBhBeRkOHSJTwokTQGAgRUd9/TVVnM0MFy4A8+dT9EN8PPlGmZuTP1Rmszbeu0ebF4Zh\nMkmlShTrfPQoZWi7c4cSqPzwgyrla5kyZLOUS8b+9Rf5NkRHk7ezEHR9XJxx94yMpOQsMTE5976Y\nj4oP2sExKChI+bu/vz/8TbwkoosLMGMGbQ4ePQK+/ZYe3nv20O7+zRsqU9+sGYVPb9lCWobx4yms\n2t2d2tSzPALks3D3LoVH6iMujnItTJ5MBe0OHiTNRd26JJhkNqLq+XO6N2M6hIeHIzw8PK+HkSE+\ntLmc7QwbRhnT1q0jgQGgnUPTpuSTUKQIRUEAtNO4fp0cGrdsIeejZ89oV1GrlnH3c3Oj8rdcMc6k\nMaW5zA6OecChQ7QZ+O03muODBlF4taMjCfxmZqrUzOHhlIyteXNK7LZxI/kdqIdVX7lCPlFyVVp9\nJCerhIK+fWmt6NCBNin/+5/uqAzmw8fU54qpj89kCAsjz+R9+8jpqH17EhBk08S6dRT10Lev/j5O\nnybHqYkTc2fMTLbCDo6fGHfuUOXJVq3IXOngQEnaoqJUTtEvXpAfwYQJqvwLX3xBmoa0uQwsLYHd\nu1UmDX2oaw/at6eNirMz5XRJKygsXEgJ5BiGMRHMzcl56e1bVdxyvXo0ec+cAcqWJV+HyEhybtJF\nuXLkGMUwGYSFhTxg4EAS7oOCKGOjnPYdoHTPwcEkQKQ1N5iZka9B2ge7qyslflIPw0xJ0Uw/n5bP\nPqN148oVTUdsGT8/oHLlzL5DhmGyTNoUqQ0bkr2yb19yZly9WnVszx5K4NS1K2V23LlTd5+OjuQU\nyTAZ5IP2WfiQkSR6+FeqRHN/9GhqFwI4dowcGqOjyQyhHmb944+kgVDn8WOq+zBrFuVakPuRX4Z4\n9Ah4+ZJMEupUrWr8e3nyhKK/AgONv4ZhmHRQT5GqTp8+lN/d0VHVNnky/UxNpUWCC0Yx2QxrFvIY\nWUhISSETg7k5JWUrW5Ye5JcuaT7w7ewoWyJAD+ixY0nrOG0ahWHKG4rYWBJE3N0N379lS6BHD8Pn\nCEGmkB9/1H387l0aJ8Mw2Ui5cqTemzUL2LBB85i3t3ae9kePaDKnplKoJMNkI/yNymNSUuingwPl\nWWjRgvwVvv+eTAu1apFDpBDku6TO06e0LhQpQg6P7u6qJE9XrpDvU3bx4AEJMbpo0IBCvhmGyQGa\nNdPtZ5CaSp7Q8m7C05Pirp2ccnd8zCcBR0PkMVWrUg2Z3r3JDPn8OVClCoU6Ll1K/ggbNlCY9cuX\npEEwlpQUMnUkJJA/g+zrcOYMmQ7atTN8/ZIl5ADZsaNx97t8mTLL2tkZP0Ym5zH1uWLq4zNZIiPJ\n2emXX4B8+TSPvXpFi8fdu2S3NDakkjFpOBriE6ZyZdIYyk6GBQqQ31K+fOT82KQJ+Sx06UIhlhlh\n3DgKt+zSBZgyRdUuScZpKX19SXAxBiEoydzx4xkbI8MwmaRQISobm1ZQuHqVytm+f09V5C5e1L52\nzx7OpsZkCHZwzENSUujVrx85KG7dSiGN/v60Dvz4I+VBOHaMHKHV/ZnSY+lSyuFw8CDleFFPK+3r\na1wfGYmG2L+ffLGaNTP+GoZhMsmJE5TnvVw5slsmJJBaD6Cqk61akQSvL/mKrW3mM7ExnyRshshD\nbtwgLeKKFfRAP32aisJ99RVlZTxxgjQOLVqQQJGRpEm3b9PDW5I0S0nLPHqUvdVqU1JIKypX1GVM\nB1OfK6Y+PpPj8GFyamrfnmyYgYFUcGrYMDp+6RKlfa1QgUwUzEdDXs4VFi3zkBcvgIgISn60bh3l\nV1m+nHwE6tQhh8XduymywVhBQXaELlWKtJDr11N6Z/XS2I8eUYjlkiU0hi1bKCpDoSBtxLVrmqHY\n0dGU8MlQZIWZGQsKDJMrlClDiVB69qTFoUwZal+3jlSSBQuSx3GRInk7Tuajgn0W8pAGDcipcd8+\nemjPn085E/r2Ja2Ajw9lVyxf3rj+7t4lR0m5gmRCAlWYjImhdUSuXOnlRYKCiwv5QBUtqhJGIiK0\nTZybNpH2g2EYE6BQIWDePAqXkgUFgByenJ1pIbh7FyheXH8fW7ZQ7DXDGAmbIUyAP/8kP4W9e8kv\nKTWVwiX//Zd8D6pWpfBpHx/D/aSmUviln5+qTU4VX7gw5XJRX1uMJTmZzAzqGSKZDwdTnyumPj6T\n5v17qhDn5UW1I8qXpwp16bF8OS0sGcm+xuQ5HA3xifPZZ+Rv1KQJzeFevSis8dgxqkT57h2ZI9JD\nodAUFABKFd+iBfVx7BiZH969M35sU6dShEN2CAq3bqnyQDAMkwFu3NCd6MTcnDQKtWsDbdsCNWuq\njr15Q9ESuorG9OnDggKTIdhnwQSIiKC1oFUrcnT09qYKtKdPU+XJO3eoCq2xPH1KZoVChejvDh1I\nK7F+Pfk+vX6t6cNgiEuXgIcPaQyJiSofqrScP095IJo21d/X2bPkoF22rPHvhWEYkMfy69eqSnMy\nCgU9+AFts8P79+QQVbiwquw1w2QSNkOYAOfO0cO2dWvVA/7ECWDGDHpIC0Gh08Y6Oc6dS+cOHarZ\n/uQJCR8rV1IoZatWxvW3ZYuqrPaOHbrPCQ8nn4j//c+4Ppncw9TniqmP74Pm+HGSztPmYmA+SNgM\n8YljZUX+SKNHqzK32tpSRsTq1WkzYYwZQqZWLd3VJgsXJo1lo0bG51rYv5/Wm/nzSaMZE6M6duUK\n0KkThXf6+7OgwDB5zr59lPI5Opr+rlBBu4YEw2QCFhZMAAcHEghmzlRpD8qUoURMJUpQ27lzVDTK\nGJt/iRKGhYFq1Si6Sh8HDwKzZ9PvDRsC/fvT7ytWUNVcmZkzSdvAaxHD5DFCkCQfHU3mh+fPSdU3\neTIQGprXo2M+AtgMYUKkpACTJlF65tKlKclRfDyFMoaFUe6D5s1zvvbCgweUi6FePc32xETKp2Bu\nTrUrBg6ktSi9KA0mbzH1uWLq4zMZUlNp56DLHrlzJ0n5f/xBvx8+TElTWrYkx8cbN9JXJ+7YQY5H\nffvmzPiZLJOXc+X/2rvv8CbL9Q/g33QwyihDNlU2FChtmYpUWlGQUUBAxjkiIAiiLM9RFEQoyuGH\nC4WD4jhHligoKuuUJZKWIZTRAlplF1qEyl7dzfv74zZN05GmaZL3TfL9XFcumuRNcgN50zvPcz/3\nw2RBQxRFWj6Hh8s+MC1ayO0ZGZIs9O0rnR2zsuQYezhwQBKSiIjSPW7XLhmh4AZ32qf1c0Xr8WnG\nhx/KNwXjUF9+OTnACy/IUOCIEdKsKTNTOj1WrizTE8uWSXOX4vz+u6ygKFhESZrBmgUCIF8YhgyR\nGoG+faVjKyDLH/v2lZ9TU6X3wpUr1j1nScskb96UImtrXLpk2ntm+3aZEomOloJrInKwwYOByMii\n7/PxkSLGP/+UkYUrV6Tl6tKl8piWLU271RndvSvfFIxatWKiQMXiyIIGGQxAu3amWoUKFUz3ZWcD\nK1dKMWHlypaf588/Zanj4sWWaxQMBilefPJJy8ft3y+JzMsvm247eFC+wBScsiDt0Pq5ovX4XEZa\nmiylXLHCVBkNSFvYr76SFtD5N4967z05eStXlg8Kb2914iarcWSBzHh5yYjiE0/IN3mDwXSfr6/0\nZZg8ueTnqV1bagqMCcDevearGfLLzpaaiYLOnJH6BUBWWeRPFADpAcNEgUglZ87IiQ3IEqoKFaSY\nKP8IQcWKUoVcsL3z+PEyZVGunKkOYts2WcdNVACTBY3q3VuG+XfskOnHW7dMezt062Z9jUH+ral3\n7Ci6CZyXl6y2OniwcOH0li3yOFudPCnPS0QOcPasrLEGJNufN08SCEAKoNavl+mJTp2AuXOl02Pf\nvnJM1arSyGniRPkQ2LJF1kPnn5og+gunITRKUaRz4gMPyPUPP5QpxzVrSrdVdWkYpzQfesh+z7lh\ngyQ548fb7zmpdLR+rmg9PpehKMDbb8towQMPSMtUvV62sN69W5YwZWfLN4hhw2SYMr8VK2Td9cMP\nqxI+lYyrIWzgaR8wJ0/KtGPt2jLSaOzweu9e6ZdS3rkjm1QFBdk9TNIgrZ8rWo/PpYweLb/wZ82S\n6999J1tVX7ggS6p69ABiYkyNVMilMFmwgad+wJw5I18OVqyQLxBvvQV88AHQsKH1z7FpkxQrlrTf\nxKJFMt3ZpQsQHGy6PTFRRjyZbLgGrZ8rWo/PpWzZIidnv36y1On8efOTNyMD+OUXrnpwUSxwpBKt\nWCHTj02byihi+/ZA8+ayK2R0tIwUWCsyUpKMgrKygGnTZBQDkCQhJ6dwvdPRo8CRI4UfbzDI6Me9\ne9bHQkR21Lu3jCL8738yZzl8uKn9c3a2DEsyUSAbcNdJF2EwAKtXy2ZTOh3w1FNye7NmUoB49SrQ\nqJH1z/fLL1L4/Morptt8fGQFhvF5HnxQLgWNGFH0c2ZlScH1jRuO7zJJRMXo2FGKGnv3lkrouDhp\nuDJunDRtatlS6hUiIqTrY6dO7K5GJWKy4CKGDZNh/6KKGxs1kpGH0nxhqF278I62ixfLL/ly5Qof\nf+KEbJs9aFDxz1mhghRcE5GK8rd1btlSLooCPP00sGqVLInq0kWShRUrZKqCLZ6pBKxZcBFjx8q5\n/ssv0rCpSxdTsyaDwdSwrbTu3ZORgIYNpZ+Cr6+pL8OJE0CtWkCNGjLtcOSIfDkh16L1c0Xr8bmd\nmzdlD4imTaUAsmZN8/7xR47IsspmzVQLkYqm5rnCkQWNW7hQflknJclS6Kws2R9mzBhpBx0YKLfb\nkigAUgdx9Cjw4oumZZq5uZJAfPutPG9KijRjundPGr6VLy/Toa1bS+JCRC7i+nVgzhxp0vTmm9JC\nGpD1zTduSMvnuDg58ZksUD4cWdC4wYOlT8qQIcD8+dK3wM8PCAiQ87qsPRcMBuCLL6Q19MyZctsr\nr8iy7H37JDn45hugXj1pOZ+TI1OhaWmSqISElP3vSI6l9XNF6/G5vFOngIQEKXQ6c0b2mf/HP2Ro\n8tQp+YDJzJRvBVWqAPXrS23DTz9Znnckp+PSSRt4ygdMRoZ0c42IkBEER1AUGU0wto2/dUtWYPn4\nSJJy5gzwzjuyRPP0aYmnenVJMKZMcUxMZD9aP1e0Hp/LS0iQNqr5d6tcu1Zaw3bqZH7foUNychu/\nJcyZ47gucFRqXDpJxapQQfqoeHnJJnH53yf//a/8Ui8rnc58fxl/fxm1uH1brjdtCowcCURFybSD\nt7d8vhhb0hORhoWEFN7Wun17KXxq2ND8vo4d5YTPyJBtZpnE0V84suBCXnkF6NpVdocEZJqgenWZ\nDrA3RTF9ocjMlJGHLVtkpURkpExfZGZK0yZLzp8Hzp0zr58i59L6uaL1+NzOt9/KqohGjaQY6osv\n5PaEBOCxx+TnrCwpdCxq7TSphiMLZJUpU2QPGKOuXR2TKABSH7FunTRkGj9eihrvv9/U/MnLy5Qo\nREcXvUEVIImCcZ8bItKAdu1k5ODMGWkLfd99svQp/45vx47JCU/0F44sUJESE6Ugulo1+Rxp3brw\nMRs3yn2NGkkBJLeq1iatnytaj8/tHT8uW9wOHSrXb9+WAsgBA2QYkTSDSyepzA4elKnGGjXs83zG\n5ODqVRlNyM42bzEPyPRm3brmPWDUduaMLPHkkk4iK+XmyrSDUWKiFDExUaB8OA3hJrZtAw4csO9z\nrlwJvPYa8OWXMp1Q0NKlwNSpwJIlZXudmBhZ4l1QwYJOaxw+LLUcRGSlkBDp+GZ0+7b0YzBuEkME\nTkOQBceOSZFjYKD5agmjU6dMm1uVZTn2vHkyhVGwCHLqVKBnT/M6DSo9rZ8rWo/PrV2+DLz9tnR4\nq17ddPvHH8uJ3auXerFRIeyzYAN+wBR25458y584UWoNykJRgD/+ABo0KHxfUhKwc6epnXx8vDRr\n6tSpbK9Z0Pnz0h/G19e+z+tptH6uaD0+t7B5s2z8EhFhfntOjuxQGRlpauSyb598O9DS/CIB4GoI\nshNfX2nzXtRGUKV16BDw6qsynVlQbq7UMBidPWteSF3wWIPBthgeeICJApFNMjPNr5crV/TwoI+P\nFDLm7/h2+bJ0XCPKhyMLbmjbNmnHbOzHUJTsbOCFF4Bp04A2bQrfrygybVmzpuk2g6HoLpK//SYN\nm1askCZS338vLamfeEJ2svT1ldEOe4qLk860jlo66k60fq5oPT6Xs38/8Nln0rXNUvdFg0FasrZo\nYX77t9/KcGHjxvIh8Oqrjo2XrMaRBbIrf/+SpyF8fYHRo2X32qLodOaJwvffF7/9dGCg7Elj/DJS\ns6Zp+nPIEMtJi61++UVqJoiogE6d5Bd8UYnC9etyASQheOstYNcu2TfC+EuoSRNZVlW3Ljd/oTwc\nWXBBubkyn9+kifNe89Yt4OLFwv0Wtm+Xb/n16snOt//8p/NiIuto/VzRenxuZdEiSSKMm7rMni3Z\n/cqV0mVNry96uoI0gQWONvDkD5jdu4Hly2WU0V5++EE+I9q2BZYtk8Zu1tQ+3Lgh0xAPPSSjmt7e\n9ouJ7EPr54rW43Mrxn4KxpM7IUEqoytXBr76SnaM48ZRmsWmTDaKiorK+zk8PBzhHrIBQVgYEBpq\n3+esU0d+0deqJTUMJRUWbtwIXLsGjBkjbac/+UR2tWUNgfr0ej30er3aYZSKp57LTlfwG4BOJ6MJ\nfn7Ad98B/frJiUyaoKVzmSMLZJPEROmUaFwu+eWXwMMPS00UaYvWzxWtx+c29u2TnSQHD5briiI7\nTo4fLxXRkyfLPOOaNRxd0CiOLJBLeecdKWC8dElaPut05g3giEiDfH3NRxZ0OhkSNNYorFghlcyc\nT6QicGSBSm33bpm2+PVXx6x0IPvS+rmi9fg8xp07MtpQtarakVAxWOBoA37AqK+4vgtG8fHA6tXA\nu+9yVFNNWj9XtB6fx1i0SJKFadPUjoSKwT4L5HDffCOdFkuyeTMwf751z/n663J8cVq1ksZMTBSI\nNG7JEmkH3a6dJA0//2y6b/ly2aGNPBqTBQ9x7ZoUJJbkwQdl8yZr/P3vhTd/yq9iReCxx+TnGzeA\nBQtkJ0ki0pjHH5cTPzMTSE2VE3b/fmn1WrWqnMzR0cDRo2pHSiphgaOHsLbd8n33ycUax49LJ8Xh\nw0s+tlw5KYosaUnmnTvSxpmInMjYyvX++4HevaXz28SJ0r/duKXskSNl36GOXBZrFjzEunXSA6Go\nfSBsdfSoTDG0a2ef57txQ1ZxvfcesGmTtIquW9c+z+3JtH6uaD0+j/Pjj7Iz3GuvcQ5RY7h0kuwm\nJwfIyJCGbPmlpZmat5XV5cvARx9J+/mCr1MW1asDb78NNGwo1/n7g0gFISFA+fLmicKWLVLR3Lev\nenGRqliz4GbWrpU+CPllZ0tPhFq17PMa1arJKEXFivZ5vvyaNJEl3pMmyX4TRORk5crJUOS+faat\nritVsu83A3I5TBbczODBwIsvmt/m7S21AufP2+c1KlQAHn1U9p7ht38iF/b776btYo3eeUd2nWzf\nHhg3ToqSDh9mG2gPx2TBzVSoIA2TACA2VnaC9PICAgKkuNle0tKkxqC07tyx7XFE5AAbNwI//WR+\n25QpUuV8+jTwt79J0WO3burER5rBAkc39tZbco5HRNjvOS9eBBo0KHz7Tz/JFGdJr7VkiYxscitr\n59H6uaL1+DzS11/Lxi/HjwO3bwOjRqkdEYFNmchBZs2SYsecHLm+bBnw+ee2P9+VK/Kl49Klwvcd\nOSIdG0sybpz1yziJyImys00/jxgBNGsme9cfO6ZeTKQZTBbc2J07UsR86ZKshgoNtdxEqSS1agGf\nfVZ04WGrVkDz5iU/R4UKshsuEWlIbq7sQFmwU+OKFcD77wMJCcDMmfKhYnTvniyLSktzbqykCk5D\neIiZM4G2bWUK0lEUxTWWZaemyp/G2g53p/VzRevxeYzjx2WL6oI7Ti5fLrft2yfLlV55BTh1StY6\nL1sGPP88O6k5CTeSsgE/YEpn82Zgzx5puewIO3ZIN9iqVaWxUlF1DVqxeLEkNZMnqx2Jc2j9XNF6\nfB7rhx9kGVW9ejIk2KiRjECkpAAvvCBNm554Qu0oPQqTBRvwA6b0cnMdt019Whrw229y8fYGZp5o\nmwAAIABJREFUOnQAWrSQ+/79b+Chh4COHR3z2qWVmyt/OurfQmu0fq5oPT6PtXcv4OMDdOlifntS\nEhATw6JHFTBZsAE/YKy3c6csoxw+XJopOdrnn0uy0L69XN+0CQgKki8m5HxaP1e0Hh9BdqKrUcM1\n5hndGFdDkEO1aiX1BP/+t1wMBtufa8aMkvs1PPecKVEAgMhIWZHx2WdFH3/1KvDf/5YtLiJykI8+\nAgYOBHbvVjsSUhH3hvAADRoAb74pUwEbNgBjxtjeufXxx61b9VCQsYtkUdLTpVETv1wSaVDPnkDn\nzjJcSB6L0xAe5OBB6exapYosX7SlhuDqVWkn/eGHZdu74cgR4PvvgXnzbH8Oso7WzxWtx0f5KIoU\n/fjwe6YaOA1BTtGpk2wal5oqDZZscd99wOzZZd86unlz4OGHi79/+3bZApuINEJRgDVrgGeeUTsS\nUgGTBQ9y+7bUHHTtKtMCttYItGlT9jqnKlWA3r2Lvz81Vfa1ICKNmDlT+r1XrMgCIw/EZMGDVKok\nhYcGA7B+vRQ4a8WHH0qTOKORI4ve5C4tDXj5ZVm9pSiscyByml69ZB7ygw9kdzryKJx48iDe3sBT\nT0kL+Oeek/bNasnNlZbzoaFy/f77ZYqjJBUrAmFhQP36kmDUqCEjFL6+0lCOiBzkwQelIVPFimpH\nQipgeuiBjhwBPv7Y/t/Kb9yQb/zWSEyUX/bGvWsGDQIaNiz5cTodMGAAUK6cLMl8/HFg5UqZSgVk\n6oL73hDZwebN5sOPFSoATz8tmfmdO8D//icfImfPclrCA3A1hIdyRDfHVauACxeA11+37vicHPsU\nVWdlyaioj48sDU1IAObMKfvzugutnytaj88jJCUBtWub7/I2f75MPRRcMrluHfDll1JYNGyY7E8/\nY4aszSaHYgdHG/ADxj727pWdaO2xqZIjV1Xl5Mj+E088wSZypaX1c0Xr8XmEmTNlTvCppywfl5Ii\niUHbtrIWu2VLYPBg8y5s5DBcOkmq2bfPfksUdbrSJQqlec9fvCjbbd+7V/q4iKgEs2cDQ4aUfFzd\nurJ17WOPyfrrJ55gouAhOLJAAGTa8fx5ICLCOa+XkQH8858yBcrRS8fS+rmi9fg8UloacPmybEld\nnPXrpTNbwY2myGE4skBO9d13skW90Z9/SlHgzp2mgkNHUhRg4kTpIJn/S8m9e2zERKQJW7bIjnCW\nZGbKhi87dgDJyc6Ji1TDZMEDBQYC7dqZrq9ZA3z9NXD8eMmbRNmDTge8+qp0lIyNNd0eGytxFJSZ\nKdOkw4axURORUwwaBERFWT6menUZffj0UxmWJLfGPgseqHVr8+svvCB1SgEBhe9zlFatgD17ZPWE\ncVStd2+ZAi0oJQX4/XdZWmnvFRxEVASdDihf3nQ9LU0Sgvx73PfsCaxYISexNeueyaWxZoEc6pdf\nZFqzZs3ij1m4UHo0NG8O3LoFTJggfRQKmjtXPrMefFBaVttjBYcn0Pq5ovX4PN4XX8gcYUoK8Pbb\ncltWlsxlxsbKqogWLYDVq4GqVVUN1d2xZoFc0unTUqhoyfr1QExM0bdv3iw/Dxwov/xv3JD9Kwqe\nC1u3AufOSTOmq1eB336TJd6A1Fjk5JT970JE+Xz7rSQHgGzk0rMn8K9/SWYfHy8n3fXrsjrCz0/q\nFlatUjdmcihOQ5BFx4/LFMCff8pyauNuk4oCfPIJ8MgjQP/+puPv3gXS04G4ONnhctYsuT05Wbo2\n9uol12vWNC2zbNJELseOSV+Y8uWB//5XPpPCwmSFVrVqMkU6erTcZvTxx9KQafJkh/9TEHmOixcl\ne2/Y0Lz3QpMmpuZN06cD3brJNwZvb/kGcOEC0KiRVDCTW+E0BFl0+LD8kr95UxKG4GBZjv3RR5Iw\nvPiiqUlSWhowZox8tpw+Dbz2muk5tm+XbrEvvVT8a2VlyWeOt7c8Pi4OeOAB01bWP/4InDkj0xRG\nV67I61uzr4Sn0vq5ovX4qBjr10vP9rg46cbm6yvLmz7+WJo2kd2xg6MN+AHjfMePy+ZTdevKFwgv\nL/nice6cTGW+954URzdtat5lMS5OuskOHWr9axlXZ8yfb/e/hsfR+rmi9fiogKQkwN9fsv9jx4Dn\nn5c/vb1lWHDUKGDJErWjdEusWSCXEBRkmoaoWVO+SACyY+TQobIFto+PTFvk17mzTHn+7W+SZFhj\n8GDLoxAFZWebpli1ZvduLvkkF7d9uwwt3rkjw4erV0tGn5kJjBsnG0llZ8s8JJcsuSXWLJBNjPVP\ns2bJZ0Pz5jL6WLWqfF4YV1gdOCC7XP7xB7BtG/Doo7IfxcKFlreUjo2VEYtGjeTzadAgWSnx/ffA\nO+8A//iHFDzGxkpt1a1bMurx73+bP8+mTcClS8D48Q77pyjRrl1SD+as7phEdpeQIN8GunQxzTXG\nxEiHt4LNUX76SZ0YyaE4DUE2ycmRGgPjJnWzZsm05fz55lMQGzYAU6fK8XfvSmvnP/+ULyTr1km/\nhaL8/rvUI/j6Si3Eo4/KjrhjxsjtDRrI59b8+bLLZUiI1DLUqmX+PCdPyrf6zp0d8+/gCrR+rmg9\nPrJgzRrpsJZ/yLBtW2DKFOC559SLy02pea5wZIFs4uNjvmlU584yTTF4sPwSf/VVud24xDE3V4qm\nt22T0YfKlYGXX5aVFF27Am3amCcZrVqZJxJHjwI//CCNnA4cAObNk4ShcWNJOoqSmCgjHGrsUnnv\nnnzBiox0/msTOUV2NjB8OPDGG6bbypeXbwLHjqkXFzkEaxbILvr3lymDiAhpmmQ0fjywYAFQv75M\nO1SqJNObqakyKvHqq0B4uHRvHDvW1G5aUYDFi2UJ5c2bMtWwa5d0mzQYgBMngHfflZGG334rHE9a\nGvDWW8CpUzLtsXq16b4NG8yvO8KFC/K67AFBbik7W07YY8eAN9+U5ZSAJAqpqVL8mJurboxkV5yG\nIKfIzgaio4FJk2TFRG6ufAnJypLVE9nZ0kvB31+mKQICZNRg716gWTO57cIFqV/429+AOXOkg+PR\no9KPYcgQGa3ILzNTXiMhQV7T2Er62DEZ5fCUqQmtnytaj4+KsXGj/BkZKSfjlSum+wIC5OS0VJhE\npcbVEOT2nnxSaqJSUuTbtqLItEXNmtLEKSNDWjw//LD0gtmzR0YA3nxTRh+ys6XnQoMGMpUxapQk\nCY0aScOnO3dMr/Xnn8BXX5la24eEmO850a6d5yQKRA5Tu7acbDqddGcD5Gdvb+nxfvhwyS1eyWVw\nZIEcLjdX6hTS081bOZcvL9MW1arJyqyePYFnn5URiJs3gQ4dpFZqyxapPejaVQoab96UkYXXXpNL\nwVUGJ05Iv5jp09WpV9AarZ8rWo+P/pKTI53RevUqfGL9/LNUMqeny9xfbq4UHY0ZI8OJRW32QqXG\nAkcbReXbQjU8PBzh4eGqxULF8/aWb/K//y57O9x3n0wLZGZKAvD661K0eOOGjGb6+Mi+NJMmSTJw\n+rSMeM6YAXz2mXzuJCcDoaFSTFlQy5byubVyJTBihOd9Tun1euj1erXDKBWeyy4gJUUy+W7dTHN+\nBw/KFtWzZ8t65n/9S05snU6mIH75RYqPypeXLW179FD37+BitHQuc2SBnGbzZmDYMEkedDqZOhg1\nSj5r5s6VLyHNmpk/5tQp+RyKjwc+/1ySh1atpNCxfXtg5kwpyG7RQqYthg6VzpKrV8tqhIULpQ7i\n1CkZMTW2jvYkWj9XtB4fWXD2rAzz1a8vGf+vv8o3gipVgFdekZPxuefkm4KxEIlsxpoF8gibNkkf\nhBYtpIlTxYoyzZCSIr/4v/5aNorKP8158aLUTa1aJaMQ990nLaZPnJCEIyxMCiRv3JAvMenp8riq\nVaVY299frp87x9VcRHb300/SBKV+fZmmuHVLbr97F/jgAzkxK1eWmgYmCi7NpachyLXUrClfNAD5\nkpGWJqOae/dKArFrl4xWxsZK/QIgyyqNI9K//CIjA1OmyMosAOjTR/6sXFl6Lxg1bCijoAaDXIzP\nR0R2FBYmDVDi4qSKOCdHWraWLy91C8ePywlILo/JAjlNv36SCFy6JL/Mb92SqYE6daRzbFqajBIY\nW0UXdO2aFGD/+qs0XBo5svjX2rtXEog//5SiSmOTKCKyo5YtZevZZ56RkzMpSYb9bt6UE7BqVSYL\nboLTEOQ0lSrJyIBx9MCYNNSqJZ8rAQHyy93bW0YZvv3W/PHdu8s0xYEDwPnzUvMweTLwn//I/dnZ\n0n1WUaQ4cscOqWMYMMDpf1Uiz7F3r5yUPj4yujB0qBQn9ewJvP++JAzk8pgskNMEB8tW1sePA8uX\ny/RCixZSs1Chguwb0batTFesXi0bUxUUGysJwMGDwBdfyJcWY13Cli3SQtq4w+PHH0sB9osvAl9+\nKUXZJ09aF+vdu5KEZGfb429O5Abu3pUsvaBq1YAaNWRb6jVrpMnJ0aPSGOWll7jlqpvgNAQ5nbe3\ntIfu31+mOM+ela2v838BGT1aEgKjjAzZ4O7ECamlevRRWaH10UemYzp0kDoqY7Gwv7+0rV+wQB7b\nrp3sIzFzZskxpqfLKEd2tmkrbiKPtmmTrGqYO9f8dmN/97NnZRVEnTpy4uTmSk1D+fLA7dscYXBx\nXDpJqnvjDfly8tRTwP33y2eMophvVHXlimw//fLLlj9zFKVwv5gNG4BPPpGRhcREx0xLGAzSutpY\neKklWj9XtB4f/UVR5E2+c6cMEzZoYLpv505JEpKTgQ8/lLXONWrIGudy5SRZmD5dvdjdhJrnCpMF\nUl12tvwyP3ZMVjrExspt06aZH3fokIwMLFigTpyWrFoFnDkD5OstpBlaP1e0Hh8VsHChNGbq3Fmq\njbdulds7dpSllNHRsla5fHlJGipXliFEjiyUGfsskEfz9ZVCxR49pH6hXTvpvFhQYCDQpAmwYoXz\nYyzJgAHSe4bI7f3jH6bNVSpWlLlBg0FGFqpVk6mI69cl4794UbaXrVLFfF6RXA6TBdKM4cNleeSF\nC/K5U1ClSjIFeueOthosbdsmPR7yj8oSeYQmTWRY8KWXZGvqW7eke1qNGjL1MGOGJA0JCcDgwdo6\ncalUmCyQpjz9NNCmDfDNN0XfHxho6sugFWFhEjeRphkMMk1g72FsLy8pMOreXTZ92bdPei7odDL9\nkJsrG7ls3y5bzZJLYrJAmmMwAPv3A5GRRfdzeeopmbbQCj8/mToh0rQrV2Rp45UrjnuNsDBpzJST\nY1rqtHev3NeqFbeBdWFMFkhz2reX+f+iWsl//73sU0NEpVSnjmzbWru2/Z87LU2SgzNnpI7h7l1Z\nI60osukLuTwmC+RUu3fL7pMlCQyUpY5eBd6hv/8unR+JSEPee086qe3ZIw1KANn1rVmzorP727dl\npQS5DC6dJKfavVtqoPr1K/tzGQzSe2HgQGkWR0XT+rmi9fiogOvXZZe2/FMKqamyRDI9XbamvnhR\nRhr27pV6hp9/Np+r+9//5Lb8u79Ribh0kjxGWJh9EgVAPqsqVZKeL8W5dEm6z5ZGUpL5NtlE9Jfc\nXGmGcuiQXI+Lk65nderIyVizpvRYX7JE9oeoUEFO0Jo1gaVLpTL5jTekBSsTBZfCZIFcQlqajCQo\niqmY++OPpe9LvXrFP+7WLfnSUxpLl0pfGSIqwNtbNnjp2FGu5+aaVyH/9pucmFeuABMmyGZS3btL\nM5VKlSSpaNNGTtxZs2R1BrkETkOQS3jlFdmdslw5aQT37LNSv1Cxov2nINLT5QuRuxRua/1c0Xp8\nZCVFkdUWQUGSEJw4ISfoffdJogBIz4X4eGnqNGUK8OSTQESEunG7EE5DEJUgKEhGCAYMkE3s/vUv\nSRocUatw+LBMuRJRKZ0/L6MIKSnAnDmy1/yJE1K/sGMHMGiQZP5r18qaYz8/tSMmK3FkgVxGZqZ8\nDh07JquxevSQUc6Cbt+WS8OGtr3Ohx/KVtmPPVa2eLVC6+eK1uMjGyQkyKZSaWlA06ZS5Lh4sUxD\nnDolUxfe3rKF7JtvmvaZJ4u4kZQN+AHjeV59VUY0X3nF/PbUVKmzioyU6ytWyOfUrFmle/70dOD0\nafdrMqf1c0Xr8VEpGQyyf0Tz5jKycPUqoNfLNMX588D8+cCNG9LX/cQJ2Zly/ny1o3YJap4rPiUf\nQqQN167JTrj37sm+NHo9sGmTjHj+9pspWRg5UqZGSys2VjrSvv++XcMmcl+pqTJCcN995rc3ayYn\nZu3aUr8ASBGQwSBd15o1k30jwsOL3jWONIc1C+Qy+vaVqc/QUCmmbtpU+r906ABMn246zstL7i+t\nXr1kNOLll1mzQGSVr76S+oP8vLyASZNkFcTkyXL9jz/kvvh4Obl275Z5vtRU6bkwbJisWSbNYrJA\nLqN9e+kF8+uvwNat8rNxube9+PsDDz/smI64RG5n0iRZIlmUJ54ADhyQjaUmTZLWrZUrA126SLKw\nfDkwe7YMFe7ezT7uGsdpCHIZDzwgv8RXr5YllM8/D/TpY/kxv/8O3H+/9UXXXl6ymouIrODra349\nLk7m8mbNkmHA48eBDz6Q+x54QJZQJiTInGJmpgwPfv+91Dd06OD8+MlqHFkgl7F7t3zOZGbKF5V6\n9aT/y5gxwJ07Mpp544bctnmzFGIvXw78+KP1rxEdLZ9dRGSDZs1Mv/R9fKQAqHVruVSqJAWO//d/\nUqvQv78UCv38M3D0qGTp586pGj4VjyML5DIMBhlR8PcHtm2TQurt24GAABndPHIEqFtXpiv27JEp\ninnz5DOrJNeuSSLi6ysb5wHyhSg4WDrTEpEVatQAevcu/n69HsjKkiHBDz6QE7d+faldqF1bhgFJ\nk5gskMvo3l32lUhMBH74QbosVqoEDB4shdZvvGE6tl076bVQXKKQlSX3GXe1TEwE9u8HHnxQWkQD\n8gWpSRPLMe3cKZ1vuUycqAiKIjtSjh8vJ0nDhqbix927gbFjpffC/fdLMaS3t9oRUzGYLJBLycqS\nAsecHLmem1t0X4SSmsO9845MoY4cKdfDwuSiKDJtkZAAPPJIyfHs2SNJy8MPl/7vQuT2bt+WFRP1\n6snyylu3ZOrh0CG5ZGXJZfRo92tw4mbYlIlcisEAtGwpTZf++U+gWzfgoYeAatVK9zzJydIuuqgR\ngeXLZeq1Wze7hKw6rZ8rWo+PyshgANatkx3adDopOvrqK2DaNJl+6NlT5hdnzHCfDVkchHtDEFnJ\ny0tGNR9/XKYNYmJkGrS0AgKKnzrw95eW0kRURteuyfLIcuWkwGjVKumvUKuWVBI/95wkD9evc194\njePIArmkwYNlFHP7dpkGNW5qR4Vp/VzRenxko1u3pCZhwgQpBqpSpfAx774LnD0LLFok1cUcWbCI\n7Z6JSikrS0YZypVjokCkSf7+kgQ0aFD0/b/+KkspmzWT4cKAAFMREWkOkwVySRs2AAsX2rYHBBE5\nSXGJQmKitGHdvVs6PH71lUxNkGZxGoLIzWn9XNF6fGRHmzfLqohFi2Sb1x9+kDbPTBSswmkIIiJy\nf9nZst55+XJTjQITBZfAZIGIiBzryBEpZBwyRO1IyEZcOklERJZlZQELFti+d7vBIBdAerAfOCA/\np6SYOqwB0sd9//6yxUoOwZEFIiKyzMdH9m6wdelRx45yMf7cooX8/O670jSlenXZlKVyZVniRJrD\nAkciN6f1c0Xr8ZED3bolrVSPH5dRC0ubUJGq5wqTBSI3p/VzRevxEWkF2z0TEZHn2bIFWLNG7SjI\nCqxZICIiddSpU3QbaNIcTkMQuTmtnytaj49IKzgNQURERJrFZIGIiIgscumahaioqLyfw8PDER4e\nrlosRFqh1+uh1+vVDqNUeC4TFaalc5k1C0RuTuvnitbjIyfIzATefFP6LHTtKvvPUyGsWSAiIs9z\n4oRsT52WBgQHA598Avz8szRruntX7egoH5eehiAiIhdz/brsNlm9OrB1KxATAzRuDAwdCvTqJR0d\n33tPWku/8ILa0dJfOA1B5Oa0fq5oPT6ysw8/lGRh6tTij7l7F/D2BipWdF5cLoDtnm3ADxgi62j9\nXNF6fGRnmZmSLOTfMCo9nYmBFVizQEREnqF8efNE4cwZYPRo4M4d1UKiknFkgcjNaf1c0Xp85GCK\nIoWOrVqpHYnmcWSBiIg8k04niUJWFrByJZCRoXZEVAQmC0REpL6MDODsWVkdsXq12tFQAZyGIHJz\nWj9XtB4fOdnRo8CFC0BkpNqRaA5XQ9iAHzBE1tH6uaL1+Ii0gjULREREpFlMFoiIiMgiJgtERERk\nEZMFIiJyvuXLgbg4taMgKzFZICIi56tZUzaLKs5vv8mFNIG7ThIRkfOVtDTy4EHp7hgY6Jx4yCIu\nnSRyc1o/V7QeHznJrVvA/PnASy8BdeuqHY0mcekkERF5tipVgM6dZXqCNIfJQino9Xq+Jl/T5V6T\nCvOU/3uXek0vL2DwYMDX13mvWQaedi4zWSgFT3lD8jXd6zWpME/5v+drutdrqonJAhEREVnEZIGI\niLTtq6+A+Hi1o/BoLrsaIiQkBEePHlU7DCLNCw4ORkJCgtphFIvnMpF11DyXXTZZICIiIufgNAQR\nERFZxGSBiIiILGKyQERERBYxWSAiIiKLmCwQERGRRUwWiIiIyCImC0RERGQRkwUiIiKyiMkCERER\nWcRkgYiIiCxiskBEREQWMVkgIiIii5gsEBERkUVMFoiIiMgiJgtERERkEZMFIiIisojJAhEREVnE\nZIGIiIgsYrJAREREFjFZICIiIoucmixkZGSgS5cuCAkJQevWrTFjxoxCx+j1evj7+yM0NBShoaGY\nN2+eM0MkIiKiAnyc+WIVKlTArl274Ofnh5ycHHTr1g179uxBt27dzI7r3r07Nm7c6MzQiIiIqBhO\nn4bw8/MDAGRlZSE3Nxc1atQodIyiKM4Oi4iIiIrh9GTBYDAgJCQEderUQUREBFq3bm12v06nw759\n+xAcHIw+ffogMTHR2SESERFRPk5PFry8vJCQkICUlBTExsZCr9eb3d++fXskJyfj6NGjmDx5MgYO\nHOjsEImIiCgfnaLimP9bb72FihUr4uWXXy72mMaNG+Pw4cOFpiuaNWuGM2fOODpEIiIiTWjatClO\nnz6tyms7dWTh6tWruHnzJgAgPT0dO3bsQGhoqNkxqampeTULcXFxUBSlyLqGM2fOQFEUl7rMmTPH\npV6nLM9T2sdae3xJx5Xlfmf9/2jt/9mZr1Xk8wB2j8Fe7ydrjinufld7PzkzZnu+jq3PpdZnVEnH\nWLpPzS/ITl0NcenSJYwaNQoGgwEGgwEjR45Ejx498OmnnwIAJkyYgHXr1mHp0qXw8fGBn58f1qxZ\n48wQHSo8PNylXqcsz1Pax1p7fEnHlfV+V+LMv4urvafs9X6y5hi+p9R9HVufS63PqJKO0er7SdVp\niLLQ6XRw0dBJo6KiohAVFaV2GJ5HpwPc8Fzm+4nsTc3fe+zgSPQXrWb05Jr4fiJ34lEjC3q9XIw/\nG8/l8HDTz0TkZG46skBkb2qOLHhUsmD+eH4+EWkCT0Yiq3AagoiIiDSLyQIRERFZxGSBiIiILGKy\nQERERBYxWSAiIiKLmCwQERGRRUwWiIiIyCImC0RERGQRkwUiIiKyiMkCERERWeTUZCEjIwNdunRB\nSEgIWrdujRkzZhR53JQpU9C8eXMEBwcjPj7emSESERFRAT7OfLEKFSpg165d8PPzQ05ODrp164Y9\ne/agW7duecdER0fj9OnTOHXqFA4cOICJEydi//79zgyTiIiI8nH6NISfnx8AICsrC7m5uahRo4bZ\n/Rs3bsSoUaMAAF26dMHNmzeRmprq7DCJiIjoL05PFgwGA0JCQlCnTh1ERESgdevWZvdfvHgRAQEB\nedcbNmyIlJQUZ4dJREREf3F6suDl5YWEhASkpKQgNjYWer2+0DEFt+DU6XROio6IiIgKcmrNQn7+\n/v7o27cvDh06hPDw8LzbGzRogOTk5LzrKSkpaNCgQZHPERUVlfdzeHi42fMQERG5Mr1eX+QXajXo\nlIJf4x3o6tWr8PHxQbVq1ZCeno5evXphzpw56NGjR94x0dHRWLJkCaKjo7F//35MmzatyAJHnU5X\naASiNHQ6wHl/cyIqFk9GIquU9fdeWTh1ZOHSpUsYNWoUDAYDDAYDRo4ciR49euDTTz8FAEyYMAF9\n+vRBdHQ0mjVrhkqVKmHZsmXODJGIiIgKcOrIgj2VJcO6fBmoVw84cgQIDbVzYERUOhxZILKKmiML\nHtnBsW5d+bNvX+DkSXVjISIi0jqPTBaM3noL6NULuHhR7UiIiIi0S7XVEFowdixw7RrQsyewezdQ\noD8UERERwUNrFuTxpmnS6dOB2Fjgxx+BypXtFCARWYc1C0RWUbNmgckC5M9x44CUFGDTJqBcOTsF\nSUQlY7JAZBUmCzawd5+FnBzgqaeA8uWB1asBb287BElEJWOyQGQVrobQAB8f4OuvgdRUYNIkfnYR\nEREZMVnIp0IFYMMGIC4OmDNH7WiIiIi0waNXQxSlalVgyxYgLAyoWROYOlXtiIiIiNTFZKEItWsD\n27ebEoann1Y7IiIiIvUwWSjGAw8AW7cCjz4KVK8u3R6JiIg8EWsWLGjdWmoYxoyRpk1ERESeiMlC\nCbp0kaWUQ4YAR4+qHQ0REZHzOTVZSE5ORkREBNq0aYO2bdti8eLFhY7R6/Xw9/dHaGgoQkNDMW/e\nPGeGWKTHHweWLAH69AHOnFE7GiIiIudyas2Cr68vPvjgA4SEhODu3bvo0KEDHn/8cQQGBpod1717\nd2zcuNGZoZXoqaeA69clcdi7V7a4JiIi8gROHVmoW7cuQkJCAACVK1dGYGAg/vjjj0LHabWp5IQJ\nsvlUr17AjRtqR0NEROQcqtUsJCUlIT4+Hl26dDG7XafTYd++fQgODkafPn2QmJioUoRUphFtAAAY\nVUlEQVRFmzkT6NED6NcPSEtTOxoiIiLHU2Xp5N27dzFkyBAsWrQIlQts89i+fXskJyfDz88PW7Zs\nwcCBA3Hy5MkinycqKirv5/DwcISHhzswaqHTAe+/D4weLUWPGzYAvr4Of1kiIvIwer0eer1e7TAA\nqLCRVHZ2Nvr164fevXtj2rRpJR7fuHFjHD58GDVq1DC73d4bSZVWdjYwaJB0fFy1CvDiuhIi23Aj\nKSKreMxGUoqiYOzYsWjdunWxiUJqamreP0ZcXBwURSmUKGiBry/wzTdAcrK0hOZnHRERuSunjizs\n2bMHjzzyCNq1awedTgcAmD9/Pi5cuAAAmDBhAj766CMsXboUPj4+8PPzw8KFC/Hggw8WDtyGDEuv\nl4vxZ+OsRXi46efSunlTHjtoEDB7tm3PQeTROLJAZBU1RxacPg1hL2r+oxV0+TLQrRvw0kvAiy+q\nHQ2Ri2GyQGQVNX/vcW8IO6hbF9ixQzaeqlEDGDFC7YiIiIjsh8mCnTRuLFtbP/aYbDz1xBNqR0RE\nRGQfrOG3o6Ag4IcfgJEjgZ9/VjsaIiIi+2CyYGdduwIrVwIDBwK//KJ2NERERGXHZMEBevcGPvxQ\npiLOnVM7GiIiorJhzYKDjBgBXLsG9OwJ7NkD1KmjdkRERES2YbLgQJMmScLwxBPS18HfX+2IiIiI\nSq/U0xAGgwH79u1zRCxuafZs6cHQvz+Qnq52NERERKVnU1OmkJAQJCQkOCIeq2mpKVNJDAbg6aeB\nu3eB778HfDieQ2TCpkxEVnG5vSEee+wxrFu3zmV+WavNywtYvhzIyQHGjZPkgYiIyFXYNLJQuXJl\npKWlwdvbGxUqVJAn0ulw+/ZtuwdYHFcaWTBKSwMefxzo0kW2uf5rewwiz8aRBSKrcG8IG7hisgAA\nN24AjzwC/O1vwIwZakdDpAFMFois4pJ7Q2zYsAGxsbHQ6XTo3r07IiMj7RmX26peHdi2TYoea9YE\nxo9XOyIiIiLLbKpZeO2117B48WK0adMGgYGBWLx4MWZY8TU5OTkZERERaNOmDdq2bYvFixcXedyU\nKVPQvHlzBAcHIz4+3pYQNa1+fWD7dmDuXGDdOrWjISIissymaYigoCAkJCTA29sbAJCbm4uQkBAc\nP37c4uMuX76My5cvIyQkBHfv3kWHDh2wfv16BAYG5h0THR2NJUuWIDo6GgcOHMDUqVOxf//+woG7\n6DREfkePSg3D6tXyJ5FH4jQEkVVcbjWETqfDzZs3867fvHkTOiuq9erWrYuQkBAAUiQZGBiIP/74\nw+yYjRs3YtSoUQCALl264ObNm0hNTbUlTM0LDga++w74+9+BuDi1oyEiIiqaTTULM2bMQPv27RER\nEQFFURATE4MFCxaU6jmSkpIQHx+PLl26mN1+8eJFBAQE5F1v2LAhUlJSUMdN+yWHhQFffCFNm3bt\nAvINshAREWlCqZMFg8EALy8v/Pzzzzh48CB0Oh0WLFiAevXqWf0cd+/exZAhQ7Bo0SJUrly50P0F\nh1mKG7WIiorK+zk8PBzh4eFWx6Al/foB774L9OoF7N4NPPCA2hEREZHa9Ho99Hq92mEAsLFmoUOH\nDjh8+LBNL5idnY1+/fqhd+/emDZtWqH7n3/+eYSHh2P48OEAgFatWiEmJqbQyII71CwUtGgR8PHH\nsvFUrVpqR0PkJKxZILKKy9UsPP7443jvvfeQnJyM69ev511KoigKxo4di9atWxeZKABA//79sXLl\nSgDA/v37Ua1aNbedgiho6lRg6FDZ4tqJ/a2IiIgssmlkoVGjRoWmBnQ6Hc6ePWvxcXv27MEjjzyC\ndu3a5T1+/vz5uHDhAgBgwoQJAIBJkyZh69atqFSpEpYtW4b27dsXDtwNRxYA+YL1wgvAiRNAdDTw\nV4NMIvfFkQUiq7hUB0eDwYBvv/0Ww4YNc1RMVnHXZAEAcnOlw2NWFvDtt9x4itwckwUiq7hUsgCU\nrWbBXtw5WQAkUYiMBBo2BP7zH+4jQW6MyQKRVTymZoGsV66c9GD49VfgtdfUjoaIiDyZ3WoWAODc\nuXN2Ccoa7j6yYHTtmmw8NWoUMH262tEQOQBHFois4nIbSSUlJdk5DCpOzZqyj4Rx46mxY9WOiIiI\nPE2ppiHeeeedvJ+//fZbs/tmzpxpn4iokAYNZKfKN94AfvhB7WiIiMjTlCpZ+Prrr/N+nj9/vtl9\nW7ZssU9EVKQWLYDNm4EJE6QtNBERkbPYVOBI6mjfHvjmG2DYMODQIbWjISIiT8FkwcWEhwOffy7L\nKk+cUDsaIiLyBKVaDeHt7Q0/Pz8AQHp6OipWrJh3X3p6OnJycuwfYTE8ZTVEcZYtA+bOlY2n8m3S\nSeR6uBqCyCousxoiNzfXUXFQKY0ZA1y/DvTsKQnDffepHREREbkrm/osaIGnjywYzZgB7NwplypV\n1I6GyAYcWSCyisu1e9YCJgtCUYDx44GkJFktUb682hERlRKTBSKrMFmwAZMFk9xc2draywtYswbw\n9lY7IqJSYLJAZBWX2xuiLJ599lnUqVMHQUFBRd6v1+vh7++P0NBQhIaGYt68eU6O0PV4ewNffQXc\nuCHbW/Nzl4iI7MnpycKYMWOwdetWi8d0794d8fHxiI+Px6xZs5wUmWsrX166Ox45AvCfjIiI7Mnp\nyUJYWBiqV69u8RhOL9imShVgyxbg+++BhQvVjoaIiNyF5poy6XQ67Nu3D8HBwejTpw8SExPVDsml\n3HefbDy1aBGwYoXa0RARkTuwaddJR2rfvj2Sk5Ph5+eHLVu2YODAgTh58mSRx0ZFReX9HB4ejvDw\ncOcEqXEBAbLxVEQEUKOGdHskIiLXotfrodfr1Q4DgEqrIZKSkhAZGYnjx4+XeGzjxo1x+PBh1KhR\nw+x2roYo2cGDQN++wLp1wCOPqB0NUTG4GoLIKh61GqIkqampef8YcXFxUBSlUKJA1unUCfj6a2DI\nECAhQe1oiIjIVTl9GmLEiBGIiYnB1atXERAQgLlz5yI7OxsAMGHCBKxbtw5Lly6Fj48P/Pz8sGbN\nGmeH6FZ69AA++QTo0weIiQGaN1c7IiIicjVsyuQh/vMfYP58YM8eoH59taMhyofTEERWcZmNpMh1\njRsHXLsG9OolIwyc2SEiImtxZMGDKAowfTqwdy+wYwdQqZLaERGBIwtEVuLeEDZgsmAbRQHGjgUu\nXQI2bADKlVM7IvJ4TBaIrMJkwQZMFmyXkyMrJCpWBFavlg2oiFTDZIHIKlw6SU7l4yO7U166BEyZ\nws9pIiKyjMmCh6pQQaYh9u0D5s5VOxoiItIyrobwYP7+wNatQLduQM2awOTJakdERERaxGTBw9Wu\nLRtPhYXJcsq//13tiIiISGuYLBAaNZIRhh49gOrVpdsjERGREWsWCADQpg2wfj0wapT0YSAiIjJi\nskB5HnwQ+PJLYNAg4NgxtaMhIiKt4DQEmenVC1i8WKYiYmOBJk3UjojckV4vFwCIAhAVJT+Hh8uF\nqLTyv6f0etP7iO8p+2BTJirS0qXA++/LxlN166odDbk1NmUiO3PXt5RHNWV69tlnUadOHQQFBRV7\nzJQpU9C8eXMEBwcjPj7eidGR0cSJwOjRMtJw86ba0RARkZqcPg0xZswYTJ48Gc8880yR90dHR+P0\n6dM4deoUDhw4gIkTJ2L//v1OjpIA4PXXgatXgUceASIjAV9fDu8REXkiVaYhkpKSEBkZiePHjxe6\n7/nnn0dERASGDRsGAGjVqhViYmJQp04ds+M4DeEcBoOskLhxA/jhB9l4iv/sZFfuOmZMqnHXt5RH\nTUOU5OLFiwgICMi73rBhQ6SkpKgYkWfz8gK++EJ+fvZZdWMhIiJ1aHI1RMHMSafTFXlclLGEGkB4\neDjCOSbuEL6+wDffSP0CAMycCdSvb36pW1db212zMpqIXJ1er4fe+EGmMk1OQ4SHh2P48OEAOA2h\nJbdvy34S8+YBf/xhfklNBapVK5xENGhgfr12bcDb27lxu+uQpNvgfxDZmbu+pdT8vae5kYX+/ftj\nyZIlGD58OPbv349q1aoVShRIHVWryp+vv174PoMBuHKlcBJx5AiwebPp+rVrQK1ahZOKgpeaNWUK\nhIiI1Of0ZGHEiBGIiYnB1atXERAQgLlz5yI7OxsAMGHCBPTp0wfR0dFo1qwZKlWqhGXLljk7RLKB\nlxdQp45cQkOLPy4nR0Yh/vgDuHjRlETs3WueZNy5I1MbRSUS+Ucr/P3lWwSZUxT5t87JAXJzTT8X\ndV3tY5ZDimh1usIXoOjbS7qvLI/lfa5/H9kfmzJRqeicNLyXkQFcvlx4pKLgJSur5FGKli2B8+e1\n88vRmmPK+twGA+DjI1M+Pj6mS8HrWjhmzLM6LF+mQFFgdgFQ6DZH3qfGa/I+x9x37hynIez+2kwW\nqCRaLha8dw+4dMl8lCL/5eJF4MwZICDA8b/4tPCL1/izl5cLfctyVgZKHsNd31JMFmzAZIGs5a4f\nHG6D/0FkZ+76lmKfBSIiItIsJgtERERkEZMFIiIisojJAhEREVnEAkdyS1pewUEFuGs1GqnGXd9S\nXA1hAyYLRG7CXT/ZSTXu+pZiu2ci8ij5R36iABj3hOPID5E2cWSBiNTlrl8Dyak8YeqR0xA2YLJA\n5CaYLBBZhU2ZiIiISLOYLBAREZFFTk8Wtm7dilatWqF58+Z4++23C92v1+vh7++P0NBQhIaGYt68\nec4OkTyU3jjhSWQHfD+RO3FqspCbm4tJkyZh69atSExMxNdff43ffvut0HHdu3dHfHw84uPjMWvW\nLGeGSB6MH+5kT3w/kTtxarIQFxeHZs2aoVGjRvD19cXw4cOxYcOGQse5a+Gisz487PU6ZXme0j7W\n2uNLOq6s97sSZ/5dXO09Za/3kzXH8D2l7uvY+lxqfUaVdIxW309OTRYuXryIgICAvOsNGzbExYsX\nzY7R6XTYt28fgoOD0adPHyQmJjozRIdytRORyYK2MVko+7FMFsy52mdUWZ6LyUIpKU60bt06Zdy4\ncXnXV61apUyaNMnsmNu3byv37t1TFEVRoqOjlebNmxf5XE2bNlUA8MILL7zwwotHXJo2beq4X9Al\ncGoHxwYNGiA5OTnvenJyMho2bGh2TJUqVfJ+7t27N1544QVcv34dNWrUMDvu9OnTjg2WiIiIADh5\nGqJjx444deoUkpKSkJWVhbVr16J///5mx6SmpubVLMTFxUFRlEKJAhERETmPU0cWfHx8sGTJEvTq\n1Qu5ubkYO3YsAgMD8emnnwIAJkyYgHXr1mHp0qXw8fGBn58f1qxZ48wQiYiIqACXbfdMREREzsEO\njkRERGQRkwUiIiKyyK2SBb1ej7CwMEycOBExMTFqh0Nu4t69e+jUqRP+97//qR0KuYHff/8dEydO\nxNChQ/Hf//5X7XDIDWzYsAHjx4/H8OHDsWPHDoe8hlslC15eXqhSpQoyMzMLLckkstU777yDYcOG\nqR0GuYlWrVph6dKlWLNmDbZt26Z2OOQGBgwYgM8++wyffPIJ1q5d65DX0GSy8Oyzz6JOnToICgoy\nu72kTajCwsIQHR2NBQsWYM6cOc4Kl1yAre+pHTt2oHXr1qhVq5azQiUXYet7CgA2bdqEvn37Yvjw\n4c4IlVxEWd5TADBv3jxMmjTJMcGp1g7KgtjYWOXIkSNK27Zt827LyclRmjZtqpw7d07JyspSgoOD\nlcTERGXlypXKtGnTlIsXL+Ydm5mZqQwZMkSN0EmjbH1Pvf7668q0adOUnj17KgMGDFAMBoOKfwvS\nkrJ+TimKovTv39/ZYZOG2fqeMhgMyvTp05Uff/zRYbE5tc+CtcLCwpCUlGR2W/5NqADkbUL12muv\nYeTIkQCAH374Adu2bcPNmzcxefJkJ0dNWmbre8q4RfqKFStQq1Yt6HQ6Z4ZNGmbreyomJgbff/89\nMjIyEBER4eSoSctsfU8tXrwYO3fuxO3bt3H69GlMmDDB7rFpMlkoSlGbUB04cMDsmCeffBJPPvmk\ns0MjF2XNe8po1KhRzgqLXJg176nu3buje/fuzg6NXJQ176kpU6ZgypQpDo1DkzULReE3OrI3vqfI\n3vieInvTynvKZZIFazahIioNvqfI3vieInvTynvKZZIFazahIioNvqfI3vieInvTyntKk8nCiBEj\n0LVrV5w8eRIBAQFYtmyZ2SZUrVu3xrBhwxAYGKh2qOQi+J4ie+N7iuxNy+8pbiRFREREFmlyZIGI\niIi0g8kCERERWcRkgYiIiCxiskBEREQWMVkgIiIii5gsEBERkUVMFoiIiMgiJgtEKvP29kZoaGje\n5cKFC2V6vqNHj2LLli151zdt2oS33367rGFatHjxYrRu3TpvF7ziYino8OHDmDp1qsXnTkpKQlBQ\nkMVjzp8/j6+//tr6gImoVFxm10kid+Xn54f4+Pgi7zP2TCvNZjLx8fE4fPgwevfuDQCIjIxEZGRk\n2QO1YOnSpdi5cyfq169vMZb8cnJy0KFDB3To0KHMr3/u3Dl89dVXGDFiRJmfi4gK48gCkcYkJSWh\nZcuWGDVqFIKCgpCcnIwXXngBnTp1Qtu2bREVFZV37MGDB/Hwww8jJCQEDz74IG7fvo3Zs2dj7dq1\nCA0NxTfffIPly5dj8uTJec/96KOPIjg4GI899ljeBjWjR4/G1KlT8fDDD6Np06b47rvvioxt4cKF\nCAoKQlBQEBYtWgQAeP7553H27Fk88cQT+PDDD/OOzcrKKhRLVFQURo4ciW7duuGZZ55BTExMXiJj\nvK9r165o0aIF/vOf/xR6/dzcXLzyyivo3LkzgoOD8dlnnwEAXnvtNezevRuhoaFYtGgRfv31V3Tu\n3BmhoaEIDg7G6dOny/4fQ+TJFCJSlbe3txISEqKEhIQogwYNUpKSkhQvLy/lwIEDecdcv35dURRF\nycnJUcLDw5Vjx44pmZmZSpMmTZRDhw4piqIod+7cUXJycpTly5crkydPznvs8uXLlUmTJimKoij9\n+vVTVq5cqSiKonzxxRfKwIEDFUVRlFGjRilDhw5VFEVREhMTlWbNmhWK89ChQ0pQUJCSlpam3L17\nV2nTpo2SkJCgKIqiNGrUSLl27VqhxxSMZc6cOUrHjh2VjIwMRVEUZdeuXUq/fv3y7gsJCVEyMjKU\nq1evKgEBAcqlS5eUc+fOKW3btlUURVE+/fRTZd68eYqiKEpGRobSsWNH5dy5c4per897HkVRlMmT\nJyurV69WFEVRsrOzlfT0dGv+K4ioGJyGIFJZxYoVzaYhkpKS8MADD6Bz5855t61duxaff/45cnJy\ncOnSJSQmJgIA6tWrlzeMX7lyZQAydaEUs+XL/v37sX79egDA008/jenTpwOQaY6BAwcCAAIDA5Ga\nmlrosXv27MGgQYNQsWJFAMCgQYMQGxuL4ODgYv9uBWPR6XTo378/ypcvX+hYnU6HAQMGoHz58ihf\nvjwiIiJw4MABs+ffvn07jh8/jnXr1gEAbt++jdOnT8PHx/yj7KGHHsK//vUvpKSkYNCgQWjWrFmx\nMRJRyTgNQaRBlSpVyvv53LlzeP/99/HTTz/h6NGj6Nu3LzIyMoqtYyipvqG4RKJcuXIWj9HpdGa3\nK4pS4msVdb+fn5/Fx+Tn5VX4I2rJkiWIj49HfHw8zpw5g8cee6zQMSNGjMCmTZtQsWJF9OnTB7t2\n7bL6NYmoMCYLRBp3+/ZtVKpUCVWrVkVqaiq2bNkCnU6Hli1b4tKlSzh06BAA4M6dO8jNzUWVKlVw\n586dvMfn/wXftWtXrFmzBgCwevVqPPLII1bHERYWhvXr1yM9PR337t3D+vXrERYWZvExBWOxRFEU\nbNiwAZmZmbh27Rr0ej06depkdkyvXr3w8ccfIycnBwBw8uRJpKWloWrVqmavc+7cOTRu3BiTJ0/G\ngAEDcPz4cav/nkRUGKchiFRW1Lfv/LcFBwcjNDQUrVq1QkBAALp16wYA8PX1xdq1azF58mSkp6fD\nz88PP/74IyIiIrBgwQKEhoZixowZ0Ol0ec/373//G2PGjMG7776L2rVrY9myZUW+ZlExhYaGYvTo\n0XnTI88991zeFEFxIwwFYynqdYzXdTod2rVrh4iICFy9ehWzZ89G3bp1kZSUlHfMuHHjkJSUhPbt\n20NRFNSuXRvr169Hu3bt4O3tjZCQEIwePRqZmZlYtWoVfH19Ua9ePbz++usl/TcQkQU6pbgxSSIi\nJ5o7dy4qV66Mf/7zn2qHQkQFcBqCiDSjNP0kiMh5OLJAREREFnFkgYiIiCxiskBEREQWMVkgIiIi\ni5gsEBERkUVMFoiIiMii/wevrdq2H3KWUwAAAABJRU5ErkJggg==\n"
      },
      {
       "output_type": "display_data",
       "png": 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CACTEAMCRI0eUz4NOnTpppBxPSEjAunXrULZsWTWtRkbysm8NMguX0JdnQQgK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\nAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.externals import joblib\n",
      "#_=joblib.dump(results_so_far(random_runs), \"/home/michael/cities/random_triplet_sampling_figs/results\")\n",
      "random_results=joblib.load(\"/home/michael/cities/random_triplet_sampling_figs/results\") \n",
      "plot_avgloss(random_results, \"Randomly sampled triplets\") "
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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h6HoyKhcXwNUVyMoydiRERFQbJnlqNDbZExGZNiZ5ajQmeSIi08YkT43GJE9E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\niMhC/T80cGsC+i2gGAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Experiment 2: Sorted triplet-subset embedding"
     ]
    },
    {
     "cell_type": "markdown",
     "source": [
      "This Cython code enumerates all triples."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "import numpy as np\n",
      "cimport numpy as np\n",
      "cimport cython\n",
      "from libc.math cimport abs\n",
      "\n",
      "# This implementation is taken from heapq.py in the standard python\n",
      "# library.\n",
      "\n",
      "@cython.boundscheck(False)\n",
      "@cython.wraparound(False)\n",
      "cdef heap_siftup(double[:] heap, int[:,:] triplets, int pos, int endpos):\n",
      "    cdef int startpos = pos, rightpos\n",
      "    cdef double newitem = heap[pos]\n",
      "    cdef int newa = triplets[pos,0], newb = triplets[pos,1], newc = triplets[pos,2]\n",
      "    cdef int a,b,c\n",
      "    cdef int childpos = 2*pos + 1\n",
      "    while childpos < endpos:\n",
      "        # Set childpos to index of smaller child.\n",
      "        rightpos = childpos+1\n",
      "        if rightpos < endpos and not heap[childpos] < heap[rightpos]:\n",
      "            childpos = rightpos\n",
      "        # Move the smaller child to the top of the heap\n",
      "        heap[pos] = heap[childpos]\n",
      "        a = triplets[childpos,0]\n",
      "        b = triplets[childpos,1]\n",
      "        c = triplets[childpos,2]\n",
      "        triplets[pos,0] = a\n",
      "        triplets[pos,1] = b\n",
      "        triplets[pos,2] = c\n",
      "        pos = childpos\n",
      "        childpos = 2*pos + 1\n",
      "    heap[pos] = newitem\n",
      "    triplets[pos,0] = newa\n",
      "    triplets[pos,1] = newb\n",
      "    triplets[pos,2] = newc\n",
      "    _siftdown(heap, triplets, startpos, pos)\n",
      "\n",
      "@cython.boundscheck(False)\n",
      "@cython.wraparound(False)\n",
      "cdef _siftdown(double[:] heap, int[:,:] triplets, int startpos, int pos):\n",
      "    cdef double newitem = heap[pos]\n",
      "    cdef int newa = triplets[pos,0], newb=triplets[pos,1], newc=triplets[pos,2]\n",
      "    cdef double parent\n",
      "    cdef int a,b,c\n",
      "    cdef int parentpos\n",
      "    # Follow the path to the root, moving parents down until finding a place\n",
      "    # newitem fits.\n",
      "    while pos > startpos:\n",
      "        parentpos = (pos - 1) >> 1\n",
      "        parent = heap[parentpos]\n",
      "        a = triplets[parentpos,0]\n",
      "        b = triplets[parentpos,1]\n",
      "        c = triplets[parentpos,2]\n",
      "        if newitem < parent:\n",
      "            heap[pos] = parent\n",
      "            triplets[pos,0] = a\n",
      "            triplets[pos,1] = b\n",
      "            triplets[pos,2] = c\n",
      "            pos = parentpos\n",
      "            continue\n",
      "        break\n",
      "    heap[pos] = newitem\n",
      "    triplets[pos,0] = newa\n",
      "    triplets[pos,1] = newb\n",
      "    triplets[pos,2] = newc\n",
      "\n",
      "cdef insert_value(int[:,:] triplets, double[:] values, int triplet_count, int a, int b, int c, double D):\n",
      "    values[0] = D\n",
      "    triplets[0,0] = a\n",
      "    triplets[0,1] = b\n",
      "    triplets[0,2] = c\n",
      "    heap_siftup(values, triplets, 0, triplet_count)\n",
      "\n",
      "cdef float hyperplane_length(ab:float, ac:float, bc:float):\n",
      "    \"\"\"Consider a triangle with sides A,B,C. This function returns the\n",
      "    distance between A and the line that bisects (and is perpendicular\n",
      "    to) segment BC. See Mike's ipy notebook, 'The bisector of a\n",
      "    triangle', for details and tests. \"\"\"\n",
      "    return abs(bc/2.0 - (ab*ab - ac*ac + bc*bc)/(2.0*bc))\n",
      "\n",
      "@cython.boundscheck(False)\n",
      "@cython.wraparound(False)\n",
      "def sorted_triplet_subset(int inner_count, int triplet_count, double[:,:] K, mode):\n",
      "    result_triplets = []\n",
      "    cdef int ncities = len(K)\n",
      "    cdef int nummode\n",
      "    nparraytriplets = np.zeros((ncities, ncities, inner_count,3), 'i') - 1\n",
      "    nparrayvalues = np.zeros((ncities, ncities, inner_count), 'd') - 999999999999\n",
      "    npK = np.array(K)\n",
      "    cdef int[:,:,:,:] triplets = nparraytriplets \n",
      "    # triplets holds, for each element of the affinity matrix, the top `inner_count` triplets\n",
      "    cdef double[:,:,:] values = nparrayvalues\n",
      "    # values holds, for each element of the affinity matrix, the top `inner_count` values.\n",
      "    cdef double D\n",
      "    cdef int a,b,c\n",
      "\n",
      "    assert npK.shape == (len(K), len(K)), \"Distance matrix must be square\"\n",
      "    # assert np.all(npK+np.eye(len(npK)) != 0), \"Distance matrix must NOT have any off-diagonal 0 entries\"\n",
      "\n",
      "    if mode==\"largest_ratio\": nummode=1\n",
      "    elif mode==\"smallest_ratio\": nummode=2\n",
      "    elif mode==\"largest_triangle\": nummode=3\n",
      "    elif mode==\"smallest_triangle\": nummode=4\n",
      "    elif mode==\"largest_hyperplane_dist\": nummode=5\n",
      "    elif mode==\"smallest_hyperplane_dist\": nummode=6\n",
      "    else: raise ValueError(\"Invalid sorting mode\")\n",
      "\n",
      "    for a in xrange(ncities):\n",
      "        for b in xrange(ncities):\n",
      "            # For element (a,b), find the top c that maximize the operation.\n",
      "\n",
      "            for c in xrange(ncities):\n",
      "                if a != b and b != c and c != a:\n",
      "                    # Heap sort by distance, finding the N largest.\n",
      "\n",
      "                    if nummode==1:\n",
      "                        # Largest ratio\n",
      "                        D =  K[a,b] / (0.00001+K[a,c])\n",
      "                    elif nummode==2:\n",
      "                        # Smallest ratio\n",
      "                        D = -(K[a,b] / (0.00001+K[a,c]))\n",
      "                    elif nummode==3:\n",
      "                        # Largest triangle ratio\n",
      "                        D = ((K[a,b]+K[a,c])/2) / (0.00001+K[b,c])\n",
      "                    elif nummode==4:\n",
      "                        # Smallest triangle ratio\n",
      "                        D = -((K[a,b]+K[a,c])/2) / (0.00001+K[b,c])\n",
      "                    elif nummode==5:\n",
      "                        # Largest hyperplane distance\n",
      "                        # D = hyperplane_length(K[a,b], K[a,c], (0.00001+K[b,c]))\n",
      "                        D = abs(K[b,c]/2.0 - (K[a,b]*K[a,b] - K[a,c]*K[a,c] + K[b,c]*K[b,c])/(2.0*(K[b,c]+0.00001)))\n",
      "                    elif nummode==6:\n",
      "                        # Smallest hyperplane distance\n",
      "                        # D = -hyperplane_length(K[a,b], K[a,c], (0.00001+K[b,c]))\n",
      "                        D = - abs(K[b,c]/2.0 - (K[a,b]*K[a,b] - K[a,c]*K[a,c] + K[b,c]*K[b,c])/(2.0*(K[b,c]+0.00001)))\n",
      "\n",
      "                    # (Maybe!) insert a,b,c into the heap. values[0]\n",
      "                    # is the smallest element in the heap.\n",
      "\n",
      "                    if values[a,b,0] < D:\n",
      "                        if K[a,b] < K[a,c]:\n",
      "                            # only keep consistent triplets!\n",
      "                            insert_value(triplets[a,b,:,:], values[a,b,:], inner_count, a,b,c, D)\n",
      "                        # else:\n",
      "                        #     insert_value(triplets[a,b,:,:], values[a,b,:], inner_count, a,c,b, D)\n",
      "    \n",
      "    # TODO: Change this to make sure each triplet is represented once as the instigator\n",
      "    result_triplets = []\n",
      "    for a in xrange(ncities):\n",
      "        # For each city, find the top `triplet_count` triplets where\n",
      "        # that city as instigator maximizes D (I think! TODO check this)\n",
      "        instigator_triplets = nparraytriplets[a,:,:,:].reshape(ncities*inner_count, 3)\n",
      "        vals= nparrayvalues[a,:,:].reshape(ncities*inner_count)\n",
      "        vals = vals[instigator_triplets[:,0]!=-1].copy()\n",
      "        instigator_triplets = instigator_triplets[instigator_triplets[:,0]!=-1].copy()\n",
      "        indices = np.argsort(-vals)[:triplet_count] # top `triplet_count` largest\n",
      "        result_triplets.extend(instigator_triplets[indices])\n",
      "    return np.array(result_triplets)"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = sorted_triplet_subset(2, 15, cities_example.K, \"smallest_ratio\") \n",
      "# first number: return this many for each element of the affinity matrix\n",
      "# second number: this is similar to the \"inner_count\" mentioned earlier\n",
      "\n",
      "cities_example.run_experiment(t, True, filename=\"/tmp/triplets-smallestratio3-%d.png\")"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Triplet count: 17730 which is 6.45818682817e-05\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10  error is  105.445743 , number of constraints:  0.0019176536943\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20  error is  89.4468223562 , number of constraints:  0.00174844895657\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30  error is  82.146489815 , number of constraints:  0.00141003948111\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40  error is  77.4041683444 , number of constraints:  0.00141003948111\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50  error is  73.8538768492 , number of constraints:  0.00141003948111\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 60  error is  70.9612228808 , number of constraints:  0.00135363790186\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 70  error is  68.4641834152 , number of constraints:  0.00129723632262\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 80  error is  66.2177586536 , number of constraints:  0.00129723632262\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90  error is  64.1358251236 , number of constraints:  0.00129723632262\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100  error is  62.1650429529 , number of constraints:  0.00129723632262\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 110  error is  60.2716052322 , number of constraints:  0.00129723632262\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 120  error is  58.4338712966 , number of constraints:  0.00129723632262\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 130  error is  56.6379707039 , number of constraints:  0.00129723632262\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 140  error is  54.8750290199 , number of constraints:  0.00124083474337\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 150  error is  53.1393492272 , number of constraints:  0.00124083474337\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 160  error is  51.4272038739 , number of constraints:  0.00118443316413\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 170  error is  49.7360478446 , number of constraints:  0.000846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 180  error is  48.0640348536 , number of constraints:  0.000846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 190  error is  46.4097547545 , number of constraints:  0.000846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200  error is  44.7721258511 , number of constraints:  0.000846023688663\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 210  error is  43.150387832 , number of constraints:  0.000507614213198\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 220  error is  41.5441514875 , number of constraints:  0.000451212633954\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 230  error is  39.9534722192 , number of constraints:  0.000451212633954\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 240  error is  38.3789250801 , number of constraints:  0.00039481105471\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 250  error is  36.8216687765 , number of constraints:  0.00039481105471\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 260  error is  35.283493837 , number of constraints:  0.000282007896221\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 270  error is  33.7668553798 , number of constraints:  0.000282007896221\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 280  error is  32.2748932056 , number of constraints:  0.000225606316977\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 290  error is  30.8114411356 , number of constraints:  0.000225606316977\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 300  error is  29.3810235318 , number of constraints:  0.000169204737733\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 310  error is  27.9888298755 , number of constraints:  0.000169204737733\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 320  error is  26.6406485169 , number of constraints:  0.000112803158488\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 330  error is  25.3427294937 , number of constraints:  5.64015792442e-05\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 340  error is  24.1015367575 , number of constraints:  5.64015792442e-05\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 350  error is  22.9233486002 , number of constraints:  5.64015792442e-05\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 360  error is  21.813681985 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 370  error is  20.7765640238 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 380  error is  19.8137566912 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 390  error is  18.9241394301 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400  error is  18.1035119152 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 410  error is  17.3450201583 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 420  error is  16.7976937776 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 430  error is  16.4124575993 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 440  error is  16.2264238529 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 450  error is  16.0357716756 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 460  error is  15.8366053533 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 470  error is  15.6278182151 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 480  error is  15.4092006729 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 490  error is  15.1808414532 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 500  error is  14.9429758289 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 510  error is  14.6959591242 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 520  error is  14.4402731359 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 530  error is  14.1765409879 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 540  error is  13.9055424934 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 550  error is  13.6282253983 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 560  error is  13.3457083582 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 570  error is  13.0592718491 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 580  error is  13.0496581686 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 590  error is  12.6905033132 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600  error is  12.6105649498 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 610  error is  12.5254078341 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 620  error is  12.4343654621 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 630  error is  12.3371341093 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 640  error is  12.2335523803 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 650  error is  12.1235350691 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 660  error is  12.0070611967 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 670  error is  11.8841761922 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 680  error is  11.7549961337 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 690  error is  11.6197107652 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 700  error is  11.4785841306 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 710  error is  11.3319521664 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720  error is  11.1802168355 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 730  error is  11.0238423684 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 740  error is  10.9115080205 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 750  error is  10.8664355263 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 760  error is  10.8199241824 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 770  error is  10.7703512326 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 780  error is  10.71709907 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 790  error is  10.6598793234 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800  error is  10.5985006239 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 810  error is  10.5328077122 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 820  error is  10.4626699796 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 830  error is  10.3879814772 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 840  error is  10.3086632034 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 850  error is  10.2246655847 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 860  error is  10.1359705607 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 870  error is  10.0425930228 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 880  error is  9.94458177098 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 890  error is  9.8632230925 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 900  error is  9.83588470082 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 910  error is  9.80636515434 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 920  error is  9.77434817919 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 930  error is  9.73963418349 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 940  error is  9.7020468311 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 950  error is  9.66141552066 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 960  error is  9.61757301704 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 970  error is  9.57035674739 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 980  error is  9.51961104728 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 990  error is  9.46518976242 , number of constraints:  0.0\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1000  error is  9.4069590394 , number of constraints:  0.0\n",
        "Average loss is:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2.96490760371 \u00b0. \u03c3= 1.19743553862\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "cities_example.py:74: 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",
        "  if -1 in triplets.astype('i32'):\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "(6.458186828169176e-05, 2.9649076037091255)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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TJzqMmrqzatUqvPLKKxg2bBgefPBBvPnmm9ixYwcKCgrwzDPPOLS9cuUKkpKS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48fJzs7G7VajcFgoLOzEwC3201BQYHkFVYqlZIHGSB14n8mSqUShUJBVlaWdN+FMFVcy8i8XZAF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\nZCIiIiKiAkyQiYiIiIgK/H8ZBzwZzGcdzwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t_rand=cities_example_accelerated.random_triplet_subset(cities_example.K, 15, len(cities_example.K))\n",
      "cities_example.run_experiment(t_rand, True, filename=\"/tmp/triplets-random-%d.png\")"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Triplet count: 17730 which is 6.45818682817e-05\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10  error is  6308.92950794 , number of constraints:  0.0595036661027\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20  error is  5315.47626408 , number of constraints:  0.0429780033841\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30  error is  5039.39794574 , number of constraints:  0.0391426959955\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40  error is  4888.47422491 , number of constraints:  0.0374506486182\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50  error is  4787.80541877 , number of constraints:  0.0376198533559\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 60  error is  4715.0025385 , number of constraints:  0.0368866328257\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 70  error is  4658.18384351 , number of constraints:  0.0366610265087\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 80  error is  4611.82624034 , number of constraints:  0.036717428088\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90  error is  4572.62752123 , number of constraints:  0.0366046249295\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100  error is  4538.45118998 , number of constraints:  0.0362098138748\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 110  error is  4508.68844185 , number of constraints:  0.0355329949239\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 120  error is  4482.29709324 , number of constraints:  0.0354765933446\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 130  error is  4465.55874058 , number of constraints:  0.036491821771\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 140  error is  4443.07197379 , number of constraints:  0.0354201917654\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 150  error is  4432.32666138 , number of constraints:  0.0357586012408\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 160  error is  4421.63396283 , number of constraints:  0.0358150028201\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 170  error is  4410.97986752 , number of constraints:  0.0358714043993\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 180  error is  4400.38223145 , number of constraints:  0.0360406091371\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 190  error is  4389.85581509 , number of constraints:  0.0362098138748\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200  error is  4379.40809573 , number of constraints:  0.036266215454\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 210  error is  4368.96273043 , number of constraints:  0.0363226170333\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 220  error is  4357.78865197 , number of constraints:  0.036491821771\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 230  error is  4347.41907514 , number of constraints:  0.036943034405\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 240  error is  4340.53383838 , number of constraints:  0.0373942470389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 250  error is  4334.65249895 , number of constraints:  0.0372814438804\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 260  error is  4331.97711356 , number of constraints:  0.0373942470389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 270  error is  4329.12782557 , number of constraints:  0.0373942470389\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 280  error is  4326.10045048 , number of constraints:  0.0375070501974\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 290  error is  4322.88894783 , number of constraints:  0.0375634517766\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 300  error is  4319.48836967 , number of constraints:  0.0376198533559\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 310  error is  4315.89603977 , number of constraints:  0.0376198533559\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 320  error is  4312.10411223 , number of constraints:  0.0376762549351\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 330  error is  4308.06660488 , number of constraints:  0.0377326565144\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 340  error is  4303.66862084 , number of constraints:  0.0375634517766\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 350  error is  4298.98973123 , number of constraints:  0.0379018612521\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 360  error is  4293.70528649 , number of constraints:  0.0378454596729\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 370  error is  4288.06496131 , number of constraints:  0.0377890580936\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 380  error is  4282.8720117 , number of constraints:  0.0377890580936\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 390  error is  4278.15622694 , number of constraints:  0.0381274675691\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400  error is  4275.5186286 , number of constraints:  0.0380146644106\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 410  error is  4274.11141719 , number of constraints:  0.0380146644106\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 420  error is  4272.61353047 , number of constraints:  0.0380710659898\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 430  error is  4271.01194337 , number of constraints:  0.0381274675691\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 440  error is  4269.29711874 , number of constraints:  0.0380710659898\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 450  error is  4267.46107829 , number of constraints:  0.0381274675691\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 460  error is  4265.4977303 , number of constraints:  0.0383530738861\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 470  error is  4263.4027244 , number of constraints:  0.0383530738861\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 480  error is  4261.17253576 , number of constraints:  0.038578680203\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 490  error is  4258.80360535 , number of constraints:  0.0386914833615\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 500  error is  4256.2923622 , number of constraints:  0.0386914833615\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 510  error is  4253.63585408 , number of constraints:  0.038578680203\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 520  error is  4250.83271104 , number of constraints:  0.0386350817823\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 530  error is  4247.88431434 , number of constraints:  0.0386914833615\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 540  error is  4244.79490866 , number of constraints:  0.0387478849408\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 550  error is  4241.56947722 , number of constraints:  0.0386350817823\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 560  error is  4239.15505117 , number of constraints:  0.0386350817823\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 570  error is  4238.25766219 , number of constraints:  0.0386914833615\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 580  error is  4237.28784084 , number of constraints:  0.0386914833615\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 590  error is  4236.23261283 , number of constraints:  0.0386914833615\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600  error is  4235.08421739 , number of constraints:  0.03880428652\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 610  error is  4233.83654834 , number of constraints:  0.0388606880993\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 620  error is  4232.48897007 , number of constraints:  0.0389170896785\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 630  error is  4231.04424594 , number of constraints:  0.03880428652\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 640  error is  4229.50149814 , number of constraints:  0.0387478849408\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 650  error is  4227.85623175 , number of constraints:  0.03880428652\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 660  error is  4226.1035371 , number of constraints:  0.0389170896785\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 670  error is  4224.23925459 , number of constraints:  0.0390862944162\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 680  error is  4222.2601074 , number of constraints:  0.0390862944162\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 690  error is  4220.16373649 , number of constraints:  0.0390862944162\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 700  error is  4217.94915001 , number of constraints:  0.039255499154\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 710  error is  4216.60116066 , number of constraints:  0.0391990975747\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720  error is  4215.84631755 , number of constraints:  0.0393683023125\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 730  error is  4215.15424578 , number of constraints:  0.039255499154\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 740  error is  4214.41041804 , number of constraints:  0.0391990975747\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 750  error is  4213.604927 , number of constraints:  0.0393119007332\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 760  error is  4212.73210302 , number of constraints:  0.0393683023125\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 770  error is  4211.78693483 , number of constraints:  0.039481105471\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 780  error is  4210.76462865 , number of constraints:  0.0395375070502\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 790  error is  4209.66051187 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800  error is  4208.47004287 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 810  error is  4207.18897347 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 820  error is  4205.81374417 , number of constraints:  0.0395375070502\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 830  error is  4204.34180791 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 840  error is  4202.77101233 , number of constraints:  0.039481105471\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 850  error is  4201.35371072 , number of constraints:  0.0395375070502\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 860  error is  4200.86954145 , number of constraints:  0.0393683023125\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 870  error is  4200.38308645 , number of constraints:  0.0393683023125\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 880  error is  4199.85135975 , number of constraints:  0.039481105471\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 890  error is  4199.2698483 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 900  error is  4198.63337652 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 910  error is  4197.93575291 , number of constraints:  0.0395939086294\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 920  error is  4197.17091715 , number of constraints:  0.0397067117879\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 930  error is  4196.33665062 , number of constraints:  0.0397067117879\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 940  error is  4195.43569001 , number of constraints:  0.0397631133672\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 950  error is  4194.46964041 , number of constraints:  0.0397067117879\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 960  error is  4193.43523917 , number of constraints:  0.0396503102087\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 970  error is  4192.32625319 , number of constraints:  0.0397631133672\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 980  error is  4191.13517187 , number of constraints:  0.0398195149464\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 990  error is  4189.87320775 , number of constraints:  0.0399887196842\n",
        "Iteration "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1000  error is  4189.25770632 , number of constraints:  0.0399323181049\n",
        "Average loss is:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.695629675603 \u00b0. \u03c3= 0.456396705896\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "cities_example.py:74: 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",
        "  if -1 in triplets.astype('i32'):\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "(6.458186828169176e-05, 0.69562967560252531)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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txdy5c/HYY4859Jq68/777+PFF1/E+PHj8cADD+CVV17Bnj17oNVqMX/+fIdt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pNBpNzXHD4fCUB5AIwnSJAFmYErsdli2TJ8YBJBJyF4nPfhaWL5/580+lrWVLizzg5J3vnPn1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\n"
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "largeratio_runs = [lv.map(lambda c: run_experiment(largeratio_sorted_triplet_subset(c, K, len(cities)),\n",
      "                                                   save_triplets=True),\n",
      "                          triplet_lengths)\n",
      "                   for run in xrange(5)]\n"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": 615
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Run large-scale \"shortfirst\" experiments"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Combine \"shortfirst\" with \"random\" and see if it improves"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "progress(largeratio_runs)"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Run 1 : "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done\n",
        "Run 2 : 12/14, 3100 sec\n",
        "Run 3 : 9/14, 3100 sec\n",
        "Run 4 : 0/14, 3100 sec\n",
        "Run 5 : 0/14, 3100 sec\n"
       ]
      }
     ],
     "prompt_number": 676
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_avgloss(random_results, \"Randomly sampled triplets\",\n",
      "             results_so_far(largeratio_runs), \"Sorted large-ratio first\")\n"
     ],
     "language": "python",
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Warning: Some engines report errors\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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WUlKC1NRU7NmzB++++y4AeTX60aNHAaBS7A8ePMAHH3yA69evIz8/H1KptM4N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\nSUVORK5CYII=\n"
      }
     ],
     "prompt_number": 675
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_runs_sweeping_loss(results_so_far(largeratio_runs), \"Ratio-sorted triplets\",\n",
      "                        \"/tmp/triplets-%s.png\")"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "TODO: Try longest-first with David's variation constraint"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "TODO: Try closest-to-one first with David's variation constraint"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Try triangle constraint"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = sorted_triplet_subset(2, 15, cities_example.K, \"largest_triangle\") \n",
      "# first number: consider the top (this many) for each element of the affinity matrix\n",
      "# second number: clip above to this many per instigator\n",
      "cities_example.run_experiment(t, True, filename=\"/tmp/triplets-largesttriangle-%d.png\")\n"
     ],
     "language": "python",
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "source": [
      "Try hyperplane distance constraint"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = sorted_triplet_subset(2, 50, cities_example.K, \"smallest_hyperplane_dist\") \n",
      "# first number: consider the top (this many) for each element of the affinity matrix\n",
      "# second number: clip above to this many per instigator\n",
      "cities_example.run_experiment(t, True, filename=\"/tmp/triplets-smallest-hp-dist-%d.png\")\n"
     ],
     "language": "python",
     "outputs": [],
     "prompt_number": "*"
    }
   ]
  }
 ]
}