benchmarking/Convex_Function_1D_HyperOpt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of optimizing a convex function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goal is to test the objective values found by HyperOpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Store_Optimal_X = []\n",
    "Store_Results = []\n",
    "num_of_tries = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(x):\n",
    "    return {'loss': x ** 2, 'status': STATUS_OK }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [00:00<00:00, 530.67it/s, best loss: 8552.546942409852]\n",
      "100%|██████████| 35/35 [00:00<00:00, 575.78it/s, best loss: 9304.369301009883]\n",
      "100%|██████████| 45/45 [00:00<00:00, 607.53it/s, best loss: 4748.407564266735]\n",
      "100%|██████████| 65/65 [00:00<00:00, 508.96it/s, best loss: 1678.0477923344608]\n",
      "0 : [-92.47998130627975, -96.45915872020595, -68.90869004898246, -40.963981646495995]\n",
      "100%|██████████| 25/25 [00:00<00:00, 672.08it/s, best loss: 10384.190955066613]\n",
      "100%|██████████| 35/35 [00:00<00:00, 559.64it/s, best loss: 0.15782122682645602]\n",
      "100%|██████████| 45/45 [00:00<00:00, 512.69it/s, best loss: 169.75323514395754]\n",
      "100%|██████████| 65/65 [00:00<00:00, 489.35it/s, best loss: 1540.450346360892]\n",
      "1 : [-101.90285057380197, -0.39726719827649504, 13.02893837363419, -39.24857126521795]\n",
      "100%|██████████| 25/25 [00:00<00:00, 687.63it/s, best loss: 5084.601776173399]\n",
      "100%|██████████| 35/35 [00:00<00:00, 584.99it/s, best loss: 736.8051942514502]\n",
      "100%|██████████| 45/45 [00:00<00:00, 226.26it/s, best loss: 2239.859390361071]\n",
      "100%|██████████| 65/65 [00:00<00:00, 249.99it/s, best loss: 74.43921455327487] \n",
      "2 : [71.30639365564213, -27.144155802887852, -47.32715278105235, -8.62781632588889]\n",
      "100%|██████████| 25/25 [00:00<00:00, 314.87it/s, best loss: 26526.858108686643]\n",
      "100%|██████████| 35/35 [00:00<00:00, 349.47it/s, best loss: 2314.110632372544]\n",
      "100%|██████████| 45/45 [00:00<00:00, 235.83it/s, best loss: 3701.7114329698397]\n",
      "100%|██████████| 65/65 [00:00<00:00, 380.48it/s, best loss: 1733.8293281914926]\n",
      "3 : [-162.87067909444795, -48.10520379722493, -60.84169156893848, -41.63927626882452]\n",
      "100%|██████████| 25/25 [00:00<00:00, 496.22it/s, best loss: 750.9754539286907]\n",
      "100%|██████████| 35/35 [00:00<00:00, 446.72it/s, best loss: 681.8228848332678]\n",
      "100%|██████████| 45/45 [00:00<00:00, 506.41it/s, best loss: 4860.251192689264]\n",
      "100%|██████████| 65/65 [00:00<00:00, 460.05it/s, best loss: 1307.894438908565]\n",
      "4 : [-27.403931358998307, 26.11173844908201, 69.71550181049595, 36.16482322518064]\n",
      "100%|██████████| 25/25 [00:00<00:00, 438.03it/s, best loss: 5241.170884476388]\n",
      "100%|██████████| 35/35 [00:00<00:00, 416.50it/s, best loss: 19967.704295557327]\n",
      "100%|██████████| 45/45 [00:00<00:00, 322.69it/s, best loss: 1978.1030230901506]\n",
      "100%|██████████| 65/65 [00:00<00:00, 422.45it/s, best loss: 244.2686892367242]\n",
      "5 : [72.39593140830766, -141.30712754690518, -44.475870121787956, 15.629097518306175]\n",
      "100%|██████████| 25/25 [00:00<00:00, 562.86it/s, best loss: 6382.986679820092]\n",
      "100%|██████████| 35/35 [00:00<00:00, 564.16it/s, best loss: 1.7933059939131948]\n",
      "100%|██████████| 45/45 [00:00<00:00, 560.94it/s, best loss: 2043.7550269444678]\n",
      "100%|██████████| 65/65 [00:00<00:00, 501.18it/s, best loss: 138.91275299220035]\n",
      "6 : [79.8935959875389, -1.3391437540134348, 45.20790889816148, 11.786125444445275]\n",
      "100%|██████████| 25/25 [00:00<00:00, 544.43it/s, best loss: 542.9610788455099]\n",
      "100%|██████████| 35/35 [00:00<00:00, 489.72it/s, best loss: 1.6691044021617059]\n",
      "100%|██████████| 45/45 [00:00<00:00, 449.09it/s, best loss: 4090.435590895825]\n",
      "100%|██████████| 65/65 [00:00<00:00, 517.88it/s, best loss: 92.75997363491282]\n",
      "7 : [23.30152524719165, 1.2919382346543142, -63.95651327969517, -9.631197933534168]\n",
      "100%|██████████| 25/25 [00:00<00:00, 524.42it/s, best loss: 9041.767163132989]\n",
      "100%|██████████| 35/35 [00:00<00:00, 470.91it/s, best loss: 68.12027421338253]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 498.85it/s, best loss: 1593.0135390991468]\n",
      "8 : [-95.08820727689101, 8.253500724745987, -15.087648695968682, 39.9125736967581]\n",
      "100%|██████████| 25/25 [00:00<00:00, 427.67it/s, best loss: 925.3188528171119]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 274.83it/s, best loss: 1899.0431343347818]\n",
      "9 : [30.419054107863246, -141.93475327883544, -29.377166412643682, 43.57801205120286]\n",
      "100%|██████████| 25/25 [00:00<00:00, 382.22it/s, best loss: 49.92519353205796]\n",
      "100%|██████████| 35/35 [00:00<00:00, 284.15it/s, best loss: 390.92348841106855]\n",
      "100%|██████████| 45/45 [00:00<00:00, 316.80it/s, best loss: 1882.2944316259877]\n",
      "100%|██████████| 65/65 [00:00<00:00, 157.97it/s, best loss: 151.19303875301358]\n",
      "10 : [7.065776215820733, 19.771785159946194, 43.38541726923907, -12.296057854166659]\n",
      "100%|██████████| 25/25 [00:00<00:00, 320.98it/s, best loss: 247425.49353432696]\n",
      "100%|██████████| 35/35 [00:00<00:00, 267.50it/s, best loss: 74.55767901321559]\n",
      "100%|██████████| 45/45 [00:00<00:00, 221.82it/s, best loss: 2508.1726433606577]\n",
      "100%|██████████| 65/65 [00:00<00:00, 266.89it/s, best loss: 3085.5771470041545]\n",
      "11 : [-497.4188311014441, -8.63467885987751, -50.08165975045813, 55.547971583165435]\n",
      "100%|██████████| 25/25 [00:00<00:00, 497.65it/s, best loss: 4202.394651515134]\n",
      "100%|██████████| 35/35 [00:00<00:00, 516.79it/s, best loss: 1802.3481638744283]\n",
      "100%|██████████| 45/45 [00:00<00:00, 266.79it/s, best loss: 1605.323247449226]\n",
      "100%|██████████| 65/65 [00:00<00:00, 125.60it/s, best loss: 1562.5533898747042]\n",
      "12 : [-64.82587948894434, -42.45407122849855, -40.06648533936095, -39.5291460807681]\n",
      "100%|██████████| 25/25 [00:00<00:00, 317.31it/s, best loss: 19457.747775480253]\n",
      "100%|██████████| 35/35 [00:00<00:00, 354.67it/s, best loss: 6209.557079860657]\n",
      "100%|██████████| 45/45 [00:00<00:00, 399.53it/s, best loss: 93.4620087714973]\n",
      "100%|██████████| 65/65 [00:00<00:00, 472.35it/s, best loss: 616.2612322687263]\n",
      "13 : [-139.49103116501882, 78.80074288901505, 9.667575123654188, 24.82460940818055]\n",
      "100%|██████████| 25/25 [00:00<00:00, 546.43it/s, best loss: 460.32114574750466]\n",
      "100%|██████████| 35/35 [00:00<00:00, 431.05it/s, best loss: 37.38985845999943]\n",
      "100%|██████████| 45/45 [00:00<00:00, 230.90it/s, best loss: 1630.5447301833806]\n",
      "100%|██████████| 65/65 [00:00<00:00, 246.65it/s, best loss: 169.05990901691627]\n",
      "14 : [21.45509603212031, -6.114724724793376, -40.380004088451756, 13.002303988790459]\n",
      "100%|██████████| 25/25 [00:00<00:00, 381.74it/s, best loss: 10749.11481429349]\n",
      "100%|██████████| 35/35 [00:00<00:00, 299.85it/s, best loss: 278.6043842846701]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 482.11it/s, best loss: 2121.9336739968758]\n",
      "15 : [103.67793793422732, 16.691446440757318, -49.99982786947757, -46.064451304632684]\n",
      "100%|██████████| 25/25 [00:00<00:00, 571.25it/s, best loss: 12910.830032844002]\n",
      "100%|██████████| 35/35 [00:00<00:00, 504.31it/s, best loss: 234.88086223232216]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 227.05it/s, best loss: 2181.368055524084] \n",
      "16 : [-113.6258334748045, -15.32582337860913, 10.741537896990042, 46.70511808703714]\n",
      "100%|██████████| 25/25 [00:00<00:00, 337.77it/s, best loss: 206.12656434848074]\n",
      "100%|██████████| 35/35 [00:00<00:00, 430.87it/s, best loss: 6811.2370058541765]\n",
      "100%|██████████| 45/45 [00:00<00:00, 539.57it/s, best loss: 5942.155163025669]\n",
      "100%|██████████| 65/65 [00:00<00:00, 369.04it/s, best loss: 2024.3890451080608]\n",
      "17 : [-14.357108495392822, -82.53021874352555, -77.08537580517896, -44.993211100210004]\n",
      "100%|██████████| 25/25 [00:00<00:00, 369.21it/s, best loss: 32196.560418777317]\n",
      "100%|██████████| 35/35 [00:00<00:00, 354.36it/s, best loss: 1215.0504524865578]\n",
      "100%|██████████| 45/45 [00:00<00:00, 317.84it/s, best loss: 9255.443725867623]\n",
      "100%|██████████| 65/65 [00:00<00:00, 331.98it/s, best loss: 767.4226745570633]\n",
      "18 : [-179.4340001749315, -34.85757381813252, 96.20521672896757, -27.702394744084188]\n",
      "100%|██████████| 25/25 [00:00<00:00, 565.86it/s, best loss: 0.03764013280100085]\n",
      "100%|██████████| 35/35 [00:00<00:00, 211.93it/s, best loss: 1315.8226282670998]\n",
      "100%|██████████| 45/45 [00:00<00:00, 201.95it/s, best loss: 2534.4208836176363]\n",
      "100%|██████████| 65/65 [00:00<00:00, 203.50it/s, best loss: 1384.888008753369] \n",
      "19 : [0.19401065125657624, 36.27426950700868, 50.34303212578317, -37.21408347324127]\n",
      "100%|██████████| 25/25 [00:00<00:00, 544.25it/s, best loss: 104890.73568493724]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 35/35 [00:00<00:00, 511.73it/s, best loss: 23821.812013509978]\n",
      "100%|██████████| 45/45 [00:00<00:00, 498.73it/s, best loss: 7915.541726866686]\n",
      "100%|██████████| 65/65 [00:00<00:00, 405.38it/s, best loss: 2379.20673698534]\n",
      "20 : [-323.86839253767454, 154.34316315765327, 88.96933025973999, -48.777112839787264]\n",
      "100%|██████████| 25/25 [00:00<00:00, 363.60it/s, best loss: 5707.612776800954]\n",
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      "21 : [-75.54874437607123, -19.349969932422937, -8.17985039722737, -19.089226972892902]\n",
      "100%|██████████| 25/25 [00:00<00:00, 586.12it/s, best loss: 3.093149848750778]\n",
      "100%|██████████| 35/35 [00:00<00:00, 575.24it/s, best loss: 144.39616553510515]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 220.77it/s, best loss: 3473.9965570907325]\n",
      "22 : [-1.758735298090869, 12.016495559650707, -120.58304032148021, 58.94061890658031]\n",
      "100%|██████████| 25/25 [00:00<00:00, 539.02it/s, best loss: 918.6880688613493]\n",
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      "23 : [30.309867516393886, -303.39230261216323, 58.83493922580229, -51.433175429199025]\n",
      "100%|██████████| 25/25 [00:00<00:00, 757.80it/s, best loss: 5803.48226078376]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 487.49it/s, best loss: 0.37286157594374664]\n",
      "24 : [-76.1805897902068, 97.7195925499195, -0.17919820550493704, 0.6106239234944424]\n",
      "100%|██████████| 25/25 [00:00<00:00, 622.95it/s, best loss: 786.0848996476991]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 543.20it/s, best loss: 71.73141288056297]\n",
      "25 : [28.037205631940196, 25.576961659685026, 10.051432193636856, 8.469439939013853]\n",
      "100%|██████████| 25/25 [00:00<00:00, 760.17it/s, best loss: 3036.327685868018]\n",
      "100%|██████████| 35/35 [00:00<00:00, 668.46it/s, best loss: 972.6226860284603]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 547.50it/s, best loss: 26.844888848423903]\n",
      "26 : [55.102882736459605, 31.186899269219765, -55.86858432725217, -5.181205347062004]\n",
      "100%|██████████| 25/25 [00:00<00:00, 609.09it/s, best loss: 1842.424373866972]\n",
      "100%|██████████| 35/35 [00:00<00:00, 673.22it/s, best loss: 9676.616238723045]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 554.27it/s, best loss: 124.03425123346258]\n",
      "27 : [-42.92347113022166, -98.36979332459251, 41.25169916586103, -11.137066545256097]\n",
      "100%|██████████| 25/25 [00:00<00:00, 654.37it/s, best loss: 3556.5570258973053]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 487.05it/s, best loss: 1957.0161967069819]\n",
      "28 : [59.63687639286036, -15.361413892522307, 12.098004122236127, 44.23817578412317]\n",
      "100%|██████████| 25/25 [00:00<00:00, 679.81it/s, best loss: 3785.0198357377426]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 556.32it/s, best loss: 35.29140982467656]\n",
      "29 : [-61.52251486844261, 89.21752363612445, 101.13566530598655, 5.940657356276034]\n",
      "100%|██████████| 25/25 [00:00<00:00, 739.10it/s, best loss: 6.742142131876458]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 503.99it/s, best loss: 835.0695304360823]\n",
      "30 : [2.5965635235588707, 21.071735615787816, -78.698147047383, -28.897569628535933]\n",
      "100%|██████████| 25/25 [00:00<00:00, 779.23it/s, best loss: 58418.92219565749]\n",
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      "31 : [241.70006660250942, -7.130376062140546, -94.43674150214571, 12.852120667946053]\n",
      "100%|██████████| 25/25 [00:00<00:00, 660.54it/s, best loss: 609.8684686438467]\n",
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      "66 : [-102.31924344890797, 56.814169637532245, -21.4525692435845, -41.34379891835478]\n",
      "100%|██████████| 25/25 [00:00<00:00, 463.30it/s, best loss: 95490.60014903586]\n",
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      "67 : [309.01553383128794, 76.5230286522119, 21.413279860615432, -40.07107301737739]\n",
      "100%|██████████| 25/25 [00:00<00:00, 267.04it/s, best loss: 7927.7685709030075]\n",
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      "68 : [-89.03801755937184, 198.27933070019662, -46.892042560592216, 14.76887808306267]\n",
      "100%|██████████| 25/25 [00:00<00:00, 389.10it/s, best loss: 13604.823340340407]\n",
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      "69 : [116.63971596476222, -103.95874405662207, 15.960239493775518, 66.78696303069665]\n",
      "100%|██████████| 25/25 [00:00<00:00, 427.86it/s, best loss: 551.0804801899151]\n",
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      "70 : [23.475103411698, -115.79856786309574, -12.863383530698371, -66.26281741488037]\n",
      "100%|██████████| 25/25 [00:00<00:00, 252.13it/s, best loss: 29123.402147597048]\n",
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      "71 : [170.65580021668484, 83.54914614034459, -27.90141520678125, 19.614185428281317]\n",
      "100%|██████████| 25/25 [00:00<00:00, 529.47it/s, best loss: 21106.272837563614]\n",
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      "72 : [145.27998085615104, -152.91247710184254, 51.62436136325664, -1.5161586415056427]\n",
      "100%|██████████| 25/25 [00:00<00:00, 682.16it/s, best loss: 5626.63201897117]\n",
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      "73 : [75.01087933740791, 56.30786583488134, -32.991395785255634, -10.671752332295]\n",
      "100%|██████████| 25/25 [00:00<00:00, 332.44it/s, best loss: 91922.68208809794]\n",
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      "74 : [303.1875361687844, -74.7532382000243, 0.6697207374955383, 18.615634098540994]\n",
      "100%|██████████| 25/25 [00:00<00:00, 346.92it/s, best loss: 46627.80298822739]\n",
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      "75 : [215.9347192746627, 177.3331188904874, -122.4695886347157, -21.464380044777307]\n",
      "100%|██████████| 25/25 [00:00<00:00, 578.45it/s, best loss: 21021.582233619294]\n",
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      "76 : [144.98821411969766, 35.45786193264695, -69.55075695885353, 23.18263512906759]\n",
      "100%|██████████| 25/25 [00:00<00:00, 537.43it/s, best loss: 917.357680899971]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 497.71it/s, best loss: 233.700708223807]\n",
      "77 : [30.287913115630317, -54.46291183830374, 48.47436122299919, -15.287272752973534]\n",
      "100%|██████████| 25/25 [00:00<00:00, 704.63it/s, best loss: 113218.06984665465]\n",
      "100%|██████████| 35/35 [00:00<00:00, 571.05it/s, best loss: 2205.8871737827617]\n",
      "100%|██████████| 45/45 [00:00<00:00, 539.86it/s, best loss: 579.3144110265902]\n",
      "100%|██████████| 65/65 [00:00<00:00, 489.03it/s, best loss: 920.5457451628832] \n",
      "78 : [-336.4789292758978, -46.96687315313594, 24.068951182521232, -30.340496785037704]\n",
      "100%|██████████| 25/25 [00:00<00:00, 564.15it/s, best loss: 17951.074515735607]\n",
      "100%|██████████| 35/35 [00:00<00:00, 552.54it/s, best loss: 70076.76835293899]\n",
      "100%|██████████| 45/45 [00:00<00:00, 558.22it/s, best loss: 72.79974619601374]\n",
      "100%|██████████| 65/65 [00:00<00:00, 443.66it/s, best loss: 22.799622441443805]\n",
      "79 : [-133.98162006684203, 264.720169901991, -8.532276729924654, -4.774895018892437]\n",
      "100%|██████████| 25/25 [00:00<00:00, 591.08it/s, best loss: 55589.1260418247]\n",
      "100%|██████████| 35/35 [00:00<00:00, 477.66it/s, best loss: 4380.158152327697]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 441.36it/s, best loss: 2469.0590206324596]\n",
      "80 : [235.77346339616912, -66.18276325696667, -4.297553990076267, 49.689626891660794]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25/25 [00:00<00:00, 514.06it/s, best loss: 119395.64958040063]\n",
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      "81 : [345.5367557589216, 45.897508389951696, 42.48313092966623, -18.736200104508214]\n",
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      "82 : [85.8865235203574, -42.1212628035064, -20.1950940077484, 52.12264679548366]\n",
      "100%|██████████| 25/25 [00:00<00:00, 693.77it/s, best loss: 5729.823876652563]\n",
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      "83 : [75.69560011422436, -13.707870065595671, -3.1972894636848253, 4.781163074771662]\n",
      "100%|██████████| 25/25 [00:00<00:00, 762.66it/s, best loss: 21144.154349696673]\n",
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      "84 : [-145.4102965738557, -52.39381378074905, -20.52274407197092, -50.83097786834337]\n",
      "100%|██████████| 25/25 [00:00<00:00, 746.06it/s, best loss: 21315.001191652318]\n",
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      "85 : [-145.99657938339624, 33.93783700688073, -29.548683460048835, -9.670308775932881]\n",
      "100%|██████████| 25/25 [00:00<00:00, 362.51it/s, best loss: 10412.176102048768]\n",
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      "86 : [102.0400710605827, 32.92973811707993, 19.896514259428372, 49.55338374105672]\n",
      "100%|██████████| 25/25 [00:00<00:00, 392.02it/s, best loss: 20430.468903372606]\n",
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      "87 : [142.93519126993397, 65.31540685106222, 42.88046148049505, 32.285893497963855]\n",
      "100%|██████████| 25/25 [00:00<00:00, 334.99it/s, best loss: 762.9854698594505]\n",
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      "88 : [27.62219161941084, -1.9072910522385467, -73.86284720451744, 74.70470004386347]\n",
      "100%|██████████| 25/25 [00:00<00:00, 428.49it/s, best loss: 26211.07817587179]\n",
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      "89 : [-161.8983575453185, 23.34471844504685, -97.79933924071227, 74.18721620321958]\n",
      "100%|██████████| 25/25 [00:00<00:00, 627.44it/s, best loss: 2585.6274264540966]\n",
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      "90 : [-50.849065148280715, -22.200031204220977, 55.7440213427962, -67.94762740978209]\n",
      "100%|██████████| 25/25 [00:00<00:00, 450.41it/s, best loss: 1870.9360882675298]\n",
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      "91 : [43.25431872388617, -59.902071491505296, 23.232523255333945, 2.0851967465453356]\n",
      "100%|██████████| 25/25 [00:00<00:00, 308.95it/s, best loss: 5972.014141769612]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 469.98it/s, best loss: 585.3583042539423]\n",
      "92 : [-77.27880784386889, -34.96497530443423, -14.234393137811821, -24.194179139907646]\n",
      "100%|██████████| 25/25 [00:00<00:00, 564.46it/s, best loss: 3104.1803791667603]\n",
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      "93 : [55.715171893899424, -25.129667902457157, 21.426228455337693, 46.205255986076445]\n",
      "100%|██████████| 25/25 [00:00<00:00, 559.42it/s, best loss: 32197.30112705827]\n",
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      "94 : [179.43606417623596, -65.31874062825796, 150.6435928882547, -10.836557910379973]\n",
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      "95 : [140.36641472750398, -5.566106148469771, -3.827216342769759, -44.24400349115422]\n",
      "100%|██████████| 25/25 [00:00<00:00, 236.64it/s, best loss: 1604.175128875528]\n",
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      "96 : [-40.05215510900166, -51.44693326459037, -119.18051601641122, -3.7472182596502535]\n",
      "100%|██████████| 25/25 [00:00<00:00, 515.74it/s, best loss: 21522.75306036197]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 466.14it/s, best loss: 1222.2214426633846]\n",
      "97 : [146.7063497615627, 98.91610080423854, -33.42840208690359, -34.96028378980046]\n",
      "100%|██████████| 25/25 [00:00<00:00, 287.19it/s, best loss: 78252.46549608362]\n",
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      "100%|██████████| 65/65 [00:00<00:00, 262.25it/s, best loss: 4604.79052623484]\n",
      "98 : [279.7364214686454, 129.2902803950692, 124.52244535443509, -67.8586068692457]\n",
      "100%|██████████| 25/25 [00:00<00:00, 245.80it/s, best loss: 767.5851821080099]\n",
      "100%|██████████| 35/35 [00:00<00:00, 181.69it/s, best loss: 15329.716316400154]\n",
      "100%|██████████| 45/45 [00:00<00:00, 403.10it/s, best loss: 11597.51548206273]\n",
      "100%|██████████| 65/65 [00:00<00:00, 408.58it/s, best loss: 132.79021903859578]\n",
      "99 : [27.705327684544898, -123.81323158855096, 107.69176144005971, -11.523463847237764]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Since 5 random were tried in Bayesian, here we add 5 extra\n",
    "\n",
    "for i in range(num_of_tries):\n",
    "    best_20 = fmin(fn=objective, space=hp.uniform('x', -5000, 5000), algo=tpe.suggest, max_evals=25)\n",
    "    best_30 = fmin(fn=objective, space=hp.uniform('x', -5000, 5000), algo=tpe.suggest, max_evals=35)\n",
    "    best_40 = fmin(fn=objective, space=hp.uniform('x', -5000, 5000), algo=tpe.suggest, max_evals=45)\n",
    "    best_60 = fmin(fn=objective, space=hp.uniform('x', -5000, 5000), algo=tpe.suggest, max_evals=65)\n",
    "    print(i,':',[best_20['x'],best_30['x'],best_40['x'],best_60['x']])\n",
    "    Store_Optimal_X.append([best_20['x'],best_30['x'],best_40['x'],best_60['x']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "Store_Optimal_X=np.array(Store_Optimal_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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faOSoiFi/x3zLUPrSWgb4Qa+yjwJrDJBUDqc51bCr58gqKT2tRes4jtKE/e6IOLl3gl/t5wsp+/1KSrc6jXy56lqke75VeTFx7L1fl1BdI/ojYEpEfKH6gbCEiNiw6uKn239Uw69GxEuu/eo1rtlzDkoSM59yPeoh1bhpDcr9gPL+/mJE7NAgnmWi1+O8ImJ8RGwWERs2EVdvfW5jk/t3IHOqYVev5fR3bjZzHL5fDY+LiDV6rGcc5Tq3ZYDv1VjeS0TE1Gh8YfMrquGAT7Wpzv2LKDWc36P0fTdQp+HfrIaf7/l5VnWt8xFKLXu/7x01ZnOtRtLZlKauNYH/GKBp6Wrgg1VSdS0v9pO3DKUbg/ndBTPz+xGxHeU6kr9GxOWUbgZWpfS39wbKB8ThNWI9vFrv6RGxG6W5s7ufvOcpH1yNbgy5FLgoIi6i3FG7DeWX6GNVfD3NAD4JfCcifgr8A3g8B3h8T3Ujx36UhGNP4K6I+Dnlho7ux5q9mvLls3dm9nXt4bXALVU/aE9U821NuRniKw1i3R74ZURcTfnQ/b/MvJSR0d3X2rER8f8otRqvovxo+DnNJS1LyMwHqmP9M0pS9p6I+CXl2HU/1mwVSoJ3QB9fXH+nXH/1p4j4GeW6wv0p5+/ZmXn1IEL5KKWJ6iRKH4fXUJLPdSg3BGxP6Vz67iruX0XEl6qYb4uI7n7y1qTUIF/Pi0nZHZQbW94VEc9Szpmk1Pb2e0NQddnFBZSa+H+hJCo/b1Du0YjYn+oRaRExg1LbnpT30Oso18Yt12O2V1I6L7+H8uNqKGZS3qNfjojXUH6QkZlfqqbX2r+D0My5OVCML5GZv42IrwCfopxfF1LuhN2D0jXNNZQfaUNxEbAgIq6nfH4EpdZze8rnwpWDWMY3KZ9Lcynn2vEN8sZZmTmr+59q286g1JL/odq2CZTP/FWBI9OnXTSn3bf3+lo6XvDSXuMblJlDP12CUJomHqnKbNlHmcnV9GmUD9xLKB+ACylJye79rP+tlC4dHqZcFP8gpXnmS8BmDbZn1gDb80pKM/M91fLmUjqU3b5B2UOqZR5SxXEd5QP4cUrfc5v0sY5jKV9u3X3fzalxTILS9cellOTiGUpCci3lEUENn4bBkk+8+DgvPvHifkpHrSs1mGfFal/cR7lgu3fXH/11odLV3/5qMK2LBt18UBKEH1VxPkVJHD5F+bFaa/0D7NcVq+NyDS/e6PJgdW69k76f3jKHF5948Y0qzkXV8a37xIsJlGTkt5QEfBHlh8sMSu1yoyeC7Em5w/YxXnzixUXAG3uV275azhOUROOFfdTfcamm/xMvdh/zHwPsx8mUJ3/8pTq/5lfn2n8Bb+9jX9Q5/7tjbbT/3kPprPspGnx21dm/fZ2PQzk3B4qR/p948a7q3PxHtV9vBT5P46e+zOlrn9Lg/UH5cXsRpc/FhdW5dHO1LS/p0qqP5c7qcY709Wq4L6tj+nvKZ+c/KHf3vnUw6/XV+BXVjpWGXdV8MZtyS37Da2LixZ7vz8nMQ0YsuCGKiEMotYXvz8xp7Y2mf9VNMAcDr05/HbdNRGxGSQC/nZkfbnc8kjqP1+RpJH2CUvvUb3OkNEZ09xd3X1ujkNSxvCZPwyoiXkW5AWBjyrMP/49y15U0JkXEVpQbiP6Z0lza6AYeSRoykzwNtw2AL1Ou77gCOCIHvtNK6mTbAkdSrk87PDP/1OZ4JHUor8mTJEnqQF6TJ0mS1IFsru1l9dVXz8mTJ7c7jI7x5JNPsuKKK7Y7DA0Tj2/n8xh3No/v0u/GG2+cm5lrNJpmktfL5MmTueGGWs9gVj9mzZpFV1dXu8PQMPH4dj6PcWfz+C79IqLPjsxtrpUkSepAJnmSJEkdyCRPkiSpA5nkSZIkdSCTPEmSpA5kkidJktSBTPIkSZI6kEmeJElSBzLJkyRJ6kAmeZKkWmbPns2HP/xhttpqK8aNG9fwiQmTJ08mIpZ4rbXWWiMfrDSG+VgzSVItt956K5dddhk77rgjzz77bJ/l3v3ud3PkkUe+8P+ECRNGIjxJFZM8SVIte++9N/vssw8A+++/P3Pnzm1Ybu2112bHHXccydAk9WBzrSSplmWW8atDWhr4TpUkDYvvfe97TJgwgUmTJrH//vtzzz33tDskaUyxuVaS1HL77LMPO+64I+uuuy633XYbJ554IjvvvDN//OMfmTRpUrvDk8YEkzxJUst9/etff+HvnXfemZ122oltttmGH/zgBxx99NFtjEwaO2yulSQNu9e85jVsuumm3HTTTe0ORRozTPIkSSOiu788SSPDJE+SNOz+9Kc/cfvtt7Pddtu1OxRpzPCaPElSLQsXLuSyyy4D4P7772f+/PlceOGFAOy5557MnDmTH/7wh7z1rW9lnXXW4fbbb+dLX/oSr3rVqzjkkEPaGLk0tpjkSZJqefjhhznggAOWGNf9/9133816663Hww8/zNFHH83jjz/Oaqutxlve8hZOOeUUVlpppXaELI1JJnmSpFomT55MZvZbZsaMGSMUjaS+eE2eJElSBzLJkyRJ6kA210qSapl6ztQhzT/z4JktikRSf6zJkyRJ6kAmeZIkSR3IJE+SJKkDmeRJkiR1IJM8SZKkDmSSJ0mS1IFM8iRJkjqQSZ4kSVIHMsmTJEnqQCZ5kiRJHcgkT5IkqQOZ5EmSJHUgkzxJkqQOZJInSZLUgUzyJEmSOpBJniRJUgcyyZMkSepAJnmSJEkdyCRPkiSpA5nkSZIkdSCTPEmSpA5kkidJktSBTPIkSZI6kEmeJElSBzLJkyRJ6kAmeZIkSR3IJE+SJKkDjdokLyL2j4j/iIjfRMT8iMiI+GGN+b9bzZMRsdFwxipJkjTaLNvuAPpxHLA1sAC4D9hssDNGxN7AB6p5Jw5LdJIkSaPYqK3JA44BNgFWAo4Y7EwRsQbwHeB84MbhCU2SJGl0G7VJXmbOzMy/ZGbWnPXb1fAjrY5JkiRpaTGam2tri4hDgLcDb8/MRyOizRFJkiS1x6ityasrItYHvg78MDMvaXc8kiRJ7dQRSV5ELAOcQ7nR4mNtDkeSJKntOqW59hhgF2CvzJxXd+aIOAw4DGDNNddk1qxZrY1uDFuwYIH7s4N5fDtfo2N80MSDhrRMz5nRw/dwZ1vqk7yI2AT4V+AHmXlZM8vIzG9T3bAxZcqU7Orqal2AY9ysWbNwf3Yuj2/na3SMTzznxCEtc+Z+M4c0v1rH93Bn64Tm2i2AlwHv79H5cUZEUmr3AP5SjXt7+8KUJEkaOUt9TR4wB/heH9P2AtYCLgDmV2UlSZI63lKf5GXmLcAHG02LiFmUJO9zmTl7JOOSJElqp1Gb5FVNq93Nq2tVw9dFxLTq77mZ+YkRD0ySJGkpMGqTPGAb4OBe4zaoXgD3ACZ5kiRJDYzaGy8y84TMjH5ekwexjK6qrE21kiRpTBm1SZ4kSZKaZ5InSZLUgUzyJEmSOpBJniRJUgcyyZMkSepAJnmSJEkdyCRPkiSpA5nkSZIkdSCTPEmSpA5kkidJktSBTPIkSZI6kEmeJElSBzLJkyRJ6kAmeZIkSR3IJE+SJKkDmeRJkiR1IJM8SZKkDmSSJ0mS1IFM8iRJkjqQSZ4kSVIHMsmTJEnqQCZ5kiRJHcgkT5IkqQOZ5EmSJHUgkzxJkqQOZJInSZLUgUzyJEmSOpBJniRJUgcyyZMkSepAJnmSJEkdyCRPkiSpA5nkSZIkdSCTPEmSpA5kkidJktSBTPIkSZI6kEmeJElSBzLJkyRJ6kAmeZIkSR3IJE+SJKkDmeRJkiR1IJM8SZKkDmSSJ0mS1IFM8iRJkjqQSZ4kSVIHMsmTJEnqQCZ5kiRJHcgkT5IkqQOZ5EmSJHUgkzxJkqQOZJInSZLUgUzyJEmSOpBJniRJUgcyyZMkSepAJnmSJEkdaNQmeRGxf0T8R0T8JiLmR0RGxA/7KLtxRHw6Iq6KiHsj4pmIeCgiLomIqSMduyRJUrst2+4A+nEcsDWwALgP2KyfsicDBwJ/Bi4DHgM2Bd4GvC0ijsrMM4c3XEmSpNFjNCd5x1CSu9nALsDMfsr+EjgtM2/uOTIidgGuAE6PiAsy8+/DFawkSdJoMmqbazNzZmb+JTNzEGWn9U7wqvG/BmYBE4CdWh+lJEnS6DRqk7wWerYaPtfWKCRJkkZQRyd5EbE+8CZgIXB1m8ORJEkaMaP5mrwhiYiXAT8CXgZ8KjPntTkkSZKkERODuOSt7SKii3LjxY8y8z2DKD8OOA84ADgfOKi/a/si4jDgMIA111xzu+nTp7cibAELFixg4sSJ7Q5Dw8Tj2/kaHeM7H71zSMvcZLVNhjS/Wsf38NJv6tSpN2bmlEbTOq4mr0rwfkhJ8H4CvGegmzcy89vAtwGmTJmSXV1dwx3mmDFr1izcn53L49v5Gh3jE885cUjLnLlff50laCT5Hu5sHXVNXkSMp9TgvQv4MfDuzPSGC0mSNOZ0TE1eREyg1NztA5wLvD8zn29vVJIkSe3RETV51U0WF1ESvO9hgidJksa4UVuTFxFvB95e/btWNXxdREyr/p6bmZ+o/v4msCcwF7gfOD4iei9yVmbOGraAJUmSRpEhJXlRMqnVgBUy82+tCekF2wAH9xq3QfUCuAfoTvJeXQ1XB47vZ5mzWhWcJEnSaNZUkhcR2wLHAbsCKwLZc1kRsQpwajX+mMx8qu46MvME4IRBlu2qu3xJkqROVjvJi4j3At8FxvdVJjPnRcSGwFRK7Zkdz0mSJI2gWjdeRMQWwHcoCd6ZwBTKdXCNnAMEsMdQApQkSVJ9dWvyjgUmAN/IzKMBImJxH2VnVMPtmoxNkiRJTarbhcpUynV2pw1UMDMfAJ4C1msiLkmSJA1B3SRvHeDJzLxvkOUXAsvXXIckSZKGqG6StwiYEA06oeut6qB4ZeDxZgKTJElS8+omeXdRbrrYZBBldwfGAbfWDUqSJElDUzfJu4xyx+zR/RWKiJfzYj95P2suNEmSJDWrbpL3NeAJ4LCIODkiVu45MSKWj4h9gd8BmwEPAt9uSaSSJEkatFpJXmbOBQ4AngY+BzxEeZQYEfEAJQG8ANgUWADsn5lPtjJgSZIkDaxuTR6ZeSWwI+VJFuMp190FsBal372opr0uM69rVaCSJEkavKaeXZuZfwTeFBHrA6+ndK0yjtI8e21mzm5diJIkSaqrqSSvW2beA9zTolgkSZLUIrWbayVJkjT61UryImJyRJwREUcNouzHq7I+1kySJGmE1a3Jey9wFOXmioGsUJV9T92gJEmSNDR1k7w9quHFgyj7I0oyuFfNdUiSJGmI6iZ5k4GFmTlnoIKZeRewEFi/fliSJEkairpJ3qrAohrlnwbWqLkOSZIkDVHdJO9xYOXq2bT9qsqsDMxvJjBJkiQ1r26SdzPlOrsDBlH2wGr5f6wblCRJkoambpL3U0qS95WI2KqvQhGxNXAakMBPmg9PkiRJzaj7xItzKN2ibAlcHxHfAf6HF596sT6wN/BBYDngVuB7rQlVkiRJg1UrycvMZyPibcDlwEbAR6tXbwH8Bdg7M58bcpSSJEmqpfZjzTLzbmA74F+Bv1MSup6vB4CTgO0G09WKJEmSWq9ucy0AmfkP4AvAFyLiVcBalOvvHszMe1sYnyRJkprQVJLXU2b+DfhbC2KRJElSi9RurpUkSdLo13RNXkQsA2xMeQrG+P7KZubVza5HkiRJ9dVO8iJibeDLwP7A8oOYJZtZjyRJkppXK/mKiHWA/wXWodxJO6jZ6gYlSZKkoal7Td4JwCuBBcDHKJ0fj8/MZfp7tThmSZIkDaBuM+oelObXD2TmhcMQjyRJklqgbi3bGsBzwMXDEIskSZJapG6S9zDwlI8qkyRJGt3qJnlXAi+PiI2HIxhJkiS1Rt0k7xTgSeC0YYhFkiRJLVIrycvM2cDbgF0i4oqImBoRKw5PaJIkSWpW3X7yFvf4943Vi4h+u8LLzLQzZEmSpBFUN/myY2NJkqSlQN0kb+qwRCFJkqSWqpXkZeavhysQSZIktY6PHJMkSepAQ0ryolg9Il7VqoAkSZI0dE0leRGxbUT8N/AE8BBwV6/pq0TEtyLimxGxfAvilCRJUr4wyfUAACAASURBVA21uzaJiPcC3wXG91UmM+dFxIaUGzVmAdObDVCSJEn11arJi4gtgO9QErwzgSnA3D6Kn0PpcmWPoQQoSWqd2bNn8+EPf5itttqKcePG0dXV9ZIymckpp5zCeuutx+67784b3vAGbrnllpEPVtKQ1K3JOxaYAHwjM4+Gl3SQ3NOMarhdk7FJklrs1ltv5bLLLmPHHXfk2WefbVjm1FNP5eSTT+b0009n0aJFzJgxg1133ZU//elPrLXWWiMcsaRm1b0mbyqQDOLZtZn5APAUsF4TcUmShsHee+/NvffeywUXXMCWW275kulPP/00p556Kp/97Gf56Ec/ynbbbccFF1xARHDWWWe1IWJJzaqb5K0DPJmZ9w2y/ELAGy8kaZRYZpn+P/Z/+9vfMn/+fN75zne+MG7FFVdk77335he/+MVwhyepheomeYuACTHAw2oBIuJlwMrA480EJkkaebfffjvjxo1j4403XmL85ptvzu23396mqCQ1o26SdxflpotNBlF2d2AccGvdoCRJ7TFv3jwmTpzIuHHjlhi/yiqrsHDhQp555pk2RSaprrpJ3mWUO2aP7q9QRLwcOJVy/d7PmgtNkiRJzaqb5H2N0gHyYRFxckSs3HNiRCwfEfsCvwM2Ax4Evt2SSCVJw26VVVZhwYIFLF68ZMcJ8+bNY4UVVmDChAltikxSXbWSvMycCxwAPA18jvK0i9UBIuIBSgJ4AbApsADYPzOfrBtUROwfEf8REb+JiPkRkRHxwwHm2SkiLouIxyLiqYj4Q0QcHRHj+ptPkvSizTbbjMWLFzN79uwlxt9+++1sttlmbYpKUjNqP9YsM68EdqQ8yWI85bq7ANai9LsX1bTXZeZ1TcZ1HPBRYBvg/oEKR8Q+wNXAG4CLgLMo/fn9Oz5tQ5IGbaeddmKllVbiggsueGHcwoULufTSS9ljD/u2l5YmtR9rBpCZfwTeFBHrA6+ndK0yjtI8e21mzu5v/kE4BrgPmA3sAszsq2BErER5CsdioCszb6jGfwG4Ctg/It6VmSZ7ksa8hQsXctlllwFw//33M3/+fC688EIA9txzT1ZYYQU+85nPcPLJJ7PKKqvw9NNPc/rpp/P8889z5JFHtjN0STU1leR1y8x7gHtaFEvP5b6Q1A2it5b9gTWAc7sTvGoZT0fEcZQnbxyBNXqSxMMPP8wBBxywxLju/++++24mT57MZz7zGZ5//nm+/OUv88gjj7DDDjtwxRVXsOaaa7YjZElNqvvs2ucj4rmI2Gi4AmrCG6vhLxtMu5rSIfNOVb99kjSmTZ48mcxs+Jo8eTJQflx//vOf57777uPyyy/nN7/5Da997WvbG7ik2upek/cUsKAFzbGttGk1vLP3hMx8DribUmO5wUgGJUmS1E51k7z7KDdbjCaTquETfUzvHr9yH9MlSZI6Tt1r8n4OHBURu2Tmr4cjoHaIiMOAwwDWXHNNZs2a1d6AOsiCBQvcnx3M47v0ufPRlzR69GvVcavy7Z8u2d3pQRMPGlIMnjOjh+/hzlY3yfsy8G7gPyPiTZn592GIqa7umrpJfUzvHt/nM3Qz89tUnTZPmTIlu7q6WhbcWDdr1izcn53L47v0OfGcE2uVP2jiQZy34LyWxjBzvz47TNAI8z3c2eomeZsDn6f0P/fniPgv4FrgYUoXJg1l5tVNRziwO4AplOfp3thzQkQsC7waeI7y3F1JkqQxoW6SN4vyPNpuH6le/ckm1lPHVcA/A28Bev/cfAOwAnB1Zi4axhgkSZJGldpPvKA80aLOq5l11HEhMBd4V0RMeSHIiOWAL1X//ucwxyBJkjSq1Kphy8zhTtgAiIi3A2+v/l2rGr4uIqZVf8/NzE9UMc2PiA9Rkr1ZETEdeAx4G6V7lQuB80cibkmSpNFiOJtRh2Ib4OBe4zbgxb7u7gE+0T0hMy+OiF0o1wvuByxHeSTascCZmZlIkiSNIaMyycvME4ATas5zLbDncMQjSZK0tBmR5ldJkiSNrFo1eRFxfDMrycyTmplPkiRJzanbXHsCS3ahMpCoypvkSZIkjaC6Sd7V9J/kTaJ0mPwyYB7whybjkiRJ0hDU7UKla6AyETER+CTlTtdLM/OM5kKTJElSs1p+d21mLgC+GBHjga9ExE2ZOavV65EkSVLfhvPu2q9Srsn75DCuQ5IkSQ0MW5KXmY8CjwM7DNc6JEmS1NiwdYYcES8HVgYWDdc6JEmS1NhwNtd+nNJce/cwrkOSJEkN1O0M+Q0DFFkOWI/y/NjdKd2tnNdcaJIkSWpW3ebaWQyuM+SohjOB02uuQ5KkpdrFF1/M8ccfzx133ME666zDkUceybHHHtvusDTGNNNcG/28ngceBWYAHwTenJlekydJGjOuvfZa9t13X3bYYQcuvfRSDj30UD796U/zta99rd2haYyp2xnycF7DJ0nSUu+kk07i9a9/Pd/97ncB2G233Xj88cc56aST+Jd/+RcmTJjQ5gg1Vpi0SZLUQrfccgtvfvOblxi32267MW/ePK677ro2RaWxyCRPkqQWevrpp19SW9f9/2233daOkDRG1b279uXAVOAfmTlzgLJvBCYCV1WPOpMkqeNttNFG/P73v19i3O9+9zsAHnvssXaEpDGqbk3ePwMXAXsMouwBVdkD6wYlSdLS6vDDD+fiiy/mO9/5DvPmzePyyy/njDPOAGCZZWxA08ipe7a9oxoOpu+771PuuN2v5jokSVpqHXrooRxxxBEcccQRrLrqquy777584QtfAGCttdZqc3QaS+omeZsAzwK3DKLsjVXZTesGJUnS0mrcuHGcddZZPPLII/zhD3/goYceYscddwR4YSiNhLqdIa8FzM/MATtEzsznI2J+NY8kSWPKKquswiqrrALA2WefzU477cRmm23W5qg0ltRN8hYCkyJiXGYu7q9gRCwLrFTNI0nSmHD99ddzzTXXsM022zB//nzOO+88Lr/8cq655pp2h6Yxpm5z7V8oieGbBlH2TcB44K91g5IkaWk1fvx4zj//fN7+9rdzyCGHsHDhQq699lq22mqrdoemMaZuTd5lwA7A6RFxXWb+o1GhiJhIeWZtVvNIkjQmbLfddi/pQkVqh7o1eWcB84DXAL+PiHdExPLdEyNi+YjYF7ihKvME8PVWBStJkqTBqfvs2sci4iDgYsqdthcCiyNiblVkdWAcpeuUp4F3ZuajLYxXkiRJg1C3uZbM/FVEvB44E3h9tYzed9BeDRyVmf839BAlSRqdpp4zdcjLmHlwvw+QkppWO8kDyMybgZ0jYiNgJ0qSl8CDwG8z05stJEmS2qipJK9bZs4GZrcoFkmSJLWID9GTJEnqQLWSvIiYHBFnRMRRgyj78arses2HJ0mSpGbUrcl7L3AU5e7ZgaxQlX1P3aAkSZI0NHWTvD2q4cWDKPsjSjK4V811SJIkaYjqJnmTgYWZOWeggpl5F+W5tevXD0uSJElDUTfJWxVYVKP808AaNdchSZKkIaqb5D0OrBwRLx+oYFVmZWB+M4FJkiSpeXWTvJsp19kdMIiyB1bL/2PdoCRJkjQ0dZO8n1KSvK9ExFZ9FYqIrYHTKE/B+Enz4UmSJKkZdZ94cQ6lW5Qtgesj4jvA/wD3VNPXB/YGPggsB9wKfK81oUqSJGmwaiV5mflsRLwNuBzYCPho9eotgL8Ae2fmc0OOUpIkSbXUfqxZZt4NbAf8K/B3SkLX8/UAcBKw3WC6WpEkSVLr1W2uBSAz/wF8AfhCRLwKWIty/d2DmXlvC+OTJElSE5pK8nrKzL8Bf2tBLJIkSWqR2s21kiRJGv1q1eRFxKrAocBU4DXAatWkRyl30l4F/CAzH21lkJIkSapn0EleRHwe+AywQveoHpNXANYFdgdOiIhTM/NLLYtSkiRJtQzYXBsRy0TETyl3zK5ISe4eA2YA51evGdW4oCR8J0bExRFhc7CkjjV9+nS23XZbJk6cyCtf+Ure97738cADD7Q7LEkCBndN3qnAOygJ3Axgl8xcIzPfnJkHVa83Z+YalGbcq6qyewNfGa7AJamdfvazn3HQQQex0047cckll3Daaadx9dVXs9dee/H888+3OzxJ6r+5NiI2Ao6mdI/ypcz8Yn/lM/PXwK8j4iTgOOBjEfGtzPxLqwKWpNHgxz/+Mdtuuy1nnXXWC+NWWmkl9tlnH+644w4233zzNkYnSQPX5H2Akgj+fKAEr6fMPB74OTCOcqOGJHWUZ599lkmTJi0xbuWVVwYgM9sRkiQtYaAkb1dKLd6pTSz7VEqz7a5NzCtJo9qhhx7Kb37zG84991zmz5/PnXfeyXHHHccb3/hGtthii3aHJ0kDJnnrA4uB65pY9m+B56plSFJH2WuvvZg2bRqHHXYYkyZNYtNNN2Xx4sX89Kc/bXdokgQMnORNAuZnE20P1TzzgZWaCUySRrOZM2dy+OGHc9RRRzFz5kymT5/OY489xjve8Q4WL17c7vAkacB+8h4D1oiI8Zn5bJ0FR8R4YGXgkWaDk6TR6uMf/zhve9vbOO20014Yt80227DZZptxySWXsO+++7YxOkkauCZvNuW6uqlNLPuN1fJnNzFvUyJir4j4VUTcFxFPRcRdEXFBRLxupGKQNDbcfvvtbLPNNkuM23TTTVl++eX561//2qaoJOlFAyV5v6IkeV+MiBig7AuqTpC/SLlp41fNhzd4EXEa8D/AtsAvga8DNwH7ANdGxHtGIg5JY8P666/PTTfdtMS42267jaeeeorJkye3JyhJ6mGg5trvAJ8GdgSmRcSHMvOZ/maommm/W82zsFrGsIqItYBPAA8BW2Xmwz2mdXfQfBLww+GORdLYcPjhh3PMMcewzjrrsMcee/DQQw9x0kknMXnyZPbcc892hydJ/dfkZeaDlBq5AN4D3BwR74mIib3LRsTLI+J9wC1V2QROrJYx3NanbMv/9kzwADJzJvAPYI0RiEPSGPGxj32Mb3zjG1xxxRXss88+fOpTn2KbbbZhxowZrLjiiu0OT5IGrMkjM78aEesDHwU2A84Bno+Iu4BHq2KrARtQEq3uZt2zM/P01ofc0F+AZ4AdImL1zJzbPSEi3gC8HLh4hGKRNAZEBEcccQRHHHFEu0ORpIYGTPIAMvNjEXEDcAqwDuVJFhsDG1VFel6v9yDwucyc1sI4B4rvsYj4NHAG8OeIuJiSgG4IvA24AvjwSMUjSZLUboNK8gAy89yIOA94B+XO2S0pNXhQEqo/AzOAiwe6bm84ZObXImIO8H3gQz0mzQam9W7GlSRJ6mTRKc9YjIhPUWoazwTOotQobgZ8GdgNOD0zP9XHvIcBhwGsueaa202fPn1EYh4LFixYwMSJL7mEUx1irBzfOx+9c8jL2GS1TVoQydDV3ZZVx63KY4sfa2kMo2VftMLSfm6MlfdwJ5s6deqNmTml0bSOSPIioguYCVyUmfv2mrYCcCewNrBxZt7V37KmTJmSN9xww3CFOubMmjWLrq6udoehYTJWju/Uc5rpKnRJMw+e2YJIhq7uthw08SDOW3BeS2MYLfuiFZb2c2OsvIc7WUT0meQN1E/e0uKt1fAl75TMXAj8jrKtrx3JoCRJktqlU5K8l1XDvrpJ6R4/4tcKSpIktUOnJHm/qYaHRcQre06IiD2A1wNPA78d6cAkSZLaYdB3145yFwJXArsCt0XERZQbLzanNOUG8JnMfLTvRUiSJHWOjkjyMvP5iNgT+AjwLko3LysAjwGXAWdm5og8Q1eSJGk06IgkDyAznwW+Vr0kSZLGtD6vyYuIrSJi85EMRpIkSa3R340Xt1Cuc3tBRHw/Is4Y3pAkSZI0VAPdXRu9/j+Ecs2bJEmSRrH+kryngZVGKhBJkiS1Tn9J3hxg+YjYb4RikSRJUov0d3ftT4HPAz+JiEeBBdX4NSKi3+e/9pKZuWGzAUqSJKm+/pK8U4BtgL2A1asXwDhgco11ZFORSZIkqWl9JnmZ+RSwd0RsCvw/YEXgB8ATwNEjE54kSZKaMWBnyJl5B3AHQET8AHgqM88Z7sAkSZLUvLpPvDiRF6/NkyRJ0ihVK8nLzBOHKxBJkiS1zkCdIfcrIraMiIMj4pPV6+CI2KJVwUmSxoauri4iouHruuuuG7FlSJ2kbnMtABGxO/AV4DV9TP8j8KnM/NUQYpMkjRFnn3028+fPX2Lc8ccfz80338z2228/YsuQOkntJC8iPgp8jfLIswAWA3OryatVy9wK+EVEfCwzv9GiWCVJHWqLLZZsBHrmmWe44YYbOPDAA1l22cF9VbViGVInqdVcGxFbUxK8ZYDfAXsCEzNz7cxcG3h5Ne46SgL4tYjYqrUhS5I63S9/+UvmzZvHQQcd1NZlSEuzutfkHVvNcynwT5n5y8xc1D0xMxdl5i+BN1RlxgHHtCpYSdLYMH36dNZdd1123nnnti5DWprVTfJ2oTzB4qjMXNxXoWpad4fJU5uMTZI0Bi1cuJCf/exnvPOd7yQi2rYMaWlXN8lbE3giM+cMVDAz7wYer+aRJGlQLr30Up588skhNbO2YhnS0q5ukvcUsEJEDHgFa1VmhWoeSZIGZfr06Wy00UZMmTKlrcuQlnZ1k7zbgPHA/oMoewAwoZpHkqQBPfHEE/ziF78YUg1cK5YhdYK6Sd4FlLtmz46IN/VVKCJ2Bc6mXL/3k+bDkySNJRdddBGLFi0aUoLWimVInaBux0H/CXwA2BL4VURcB1wJ3F9NXxd4E/A6SjL4p2oeSZIGNH36dLbeems233zzti5D6gR1n127qHraxX8DOwA7URK6nrpvY/pfYL/MfGbIUUqSOt7cuXOZMWMGJ598cluXIXWK2l2AZ+YDEbET5bq8A4EpwCuqyQ8DNwDTgZ9m5vOtClSS1NlWX311nn322bYvQ+oUTT3npUrefoLX20mSJI1KdW+8kCRJ0lLAJzZLkkbU1HNGx4OQZh48s90hSMPKmjxJkqQOZJInSZLUgUzyJEmSOpBJniRJUgcyyZMkSepAJnmSJEkdqFYXKhFxfPXnDzLz3mGIR5IkSS1Qt5+8LwKLgS8PQyySJElqkbpJ3lxgXGb6YEBJkqRRrO41ef8HrBwRqw1HMJIkSWqNuknet6p5jh2GWCRJktQitZK8zPwpcAbwmYj4SkSsPjxhSZIkaSjq3l17VfXnk8DHgWMiYjbwMOWGjEYyM9/UfIiSJEmqq+6NF129/h8HbFq9+pI11yFJkqQhqpvknTgsUUiSJKmlaiV5mWmSJ0mStBTwsWaSJEkdyCRPkiSpA9W9Jg+AiFgB+CCwO7A+sHxmbthj+iRgL8qdtee1IlBJkiQNXu0kLyK2AS4B1gWiGt37Dtr5wHHAphHxUGZehSRJkkZMreba6nFmPwfWA24CPkFJ6JaQmQl8j5IEvm3oYUqSJKmOutfkHQOsDcwA/r/MPAN4qo+yP6+Gr2syNkmSJDWpbpK3N6Vp9lOZ+fwAZe8AngU2HKCcJEmSWqxukrcB8Axwy0AFqybb+cBKTcQlSZKkIaib5C0DPFclcP2KiAAmUp5zK0mSpBFUN8m7H1ghIl4xiLLbAy8D7q4dlSRJkoakbpI3qxq+fxBlv0i5fu+KmuuQJEnSENVN8r5OSdw+FxG7NioQEWtGxI+APSjX731jaCHWExFvioiLIuLBiFgUEQ9ExOURsedIxiFJktROtTpDzsxbI+JzwKnA5RFxMzAJICJ+THn6xXbA+GqWozLzby2Mt18R8RXgk8B9wM+AucAaVUxdwGUjFYskSVI71X7iRWZ+JSIeBf4N2LbHpAN58QkYjwNHZ+a5Qw9xcCLiQ5QE7xzgsMx8ptf08Q1nlCRJ6kBNPbs2M78XEecD+wGvB9YBxgEPAtcCF2TmEy2LcgAR8TLgX4G/0SDBq2J+dqTikSRJaremkjyAzFxAqTU7p3XhNO3NlGbZrwHPR8RewGuAp4HfZeZ17QxOkiRppDWd5I0y21fDp4GbKQneCyLiamD/zHxkpAOTJElqhyEleRGxJTAF6O4372Hghsy8daiB1dS9/k8CfwZ2pjyV49WUawd3Ay6g3HwhSZLU8WIQD6946UwRbwVOAbbso8itwHGZ+bMhxFYnnm8BhwGLgM0yc06PaStQnqO7LrBTo6bbiDismp8111xzu+nTp49E2GPCggULmDhxYrvD0DAZK8f3zkfvHPIyNlltkxZEMnR1t2XVcavy2OLHhima9mrFMVnaz42x8h7uZFOnTr0xM6c0mlY7yYuI4ykdHXffSfsc8Gj192q8WDuYwMmZeULdgOuKiNOATwHXZ+brGkz/LvAByh2/X+9vWVOmTMkbbrhheAIdg2bNmkVXV1e7w9AwGSvHd+o5U4e8jJkHz2xBJENXd1sOmngQ5y04b5iiaa9WHJOl/dwYK+/hThYRfSZ5tTpDjoi3ACdQEryrKc2gL8/MtTNzbcqzanejPBkjgC9ExO7Nhz5od1TDx/uYPq8aLj8CsUiSJLVd3SdeHFsNLwCmZuaVmbmoe2JmPpOZVwJvqspEj3mG0wxKzeEWEdFom7pvxPA5upIkaUyom+RNoSRTx2Y/7bzVtI9X/27fV7lWycx7gEuBVwFH9ZwWEbsBu1Nq+X453LFIkiSNBnXvrp0APJ6Z9w9UMDPvi4h51Twj4SPAa4Ezqn7ybqbcXft2YDHwwZHsoFmSJKmd6tbk3QVMjIgBE7fqKRQTq3mGXWbeR3lG7VnAxpQavS5KDd/rM/OnIxGHJEnSaFA3yfsxMB543yDKvrcq++O6QTUrMx/JzCMzc/3MnJCZq2fmOzLzdyMVgyRJ0mhQN8n7KnANcGZEHNxXoYh4H3Am8JtqHkmSJI2gPq/Jq/rDa+Q3wP8Dvh8RJ1K6S+m+Ru+VwC6UGyCeqMp+DjipRfFKkiRpEPq78eIEyp20fQlKMvfeBuMBJgGfrf42yZMkSRpB/SV5V9N/kidJkqRRqs8kLzO7RjAOSZIktVDdGy8kSZK0FDDJkyRJ6kAmeZIkSR2o7mPNAIiINwLvArYCVqF0etyXzMwNm1mPJEmSmlMryYuIZYFzgQO7Rw1iNu/QlSRJGmF1a/I+TanBg9LR8eXAQ8BzrQxKkiRJQ1P3mryDKTVzX8rMXTLzlMz8Xmae099rGOKWNEZNmzaNiHjJ65vf/Ga7Q6utFdvSSftjNHB/qpPUrclbj5LknToMsUjSoF111VUsv/zyL/y/wQYbtDGaoWnFtnTS/hgN3J/qBHWTvEeAiZm5cDiCkaTB2n777Zk4cWK7w2iJVmxLJ+2P0cD9qU5Qt7n2amBSRKw7HMFIkiSpNeomeacCTwGnDUMskjRoG264Icsuuyybbrop3/rWt9odzpC0Yls6aX+MBu5PdYJazbWZ+aeIOBD4YUT8gpLs/T4znxyW6CSpl7XXXpuTTz6ZHXbYgcWLFzN9+nQOP/xwFi5cyDHHHNPu8GppxbZ00v4YDdyf6iSRWa8bu4gYB3wROI7B9YGXmdlUp8vtMGXKlLzhhhvaHUbHmDVrFl1dXe0OQ8NktBzfAw88kCuvvJJHHnmEZZZp/YN8pp4zdcjLmHnwzEGVa8W29LeMutty0MSDOG/BeU3FMdq14piM5LkxHEbLe1jNi4gbM3NKo2m1PkEiYgVgBvD57lGDfEnSsNl///157LHHmDNnTrtDGbJWbEsn7Y/RwP2ppVXdGrbPAm8AFgM/xs6QJY0CEbHEcGnWim3ppP0xGrg/tbSqm+QdRGmiPTozvzEM8UhSbRdeeCGrr74666+/frtDGbJWbEsn7Y/RwP2ppVXdJO+VlFq87w5DLJI0oP32248ddtiBrbbaisWLF3P++edz/vnnc+aZZw7L9XjDqRXb0kn7YzRwf6qT1E3yHgZWysxFwxGMJA1k00035fvf/z733nsvmckWW2zBueeey3vf+952h1ZbK7alk/bHaOD+VCepm+T9Cjg0IjbNzDuGIyBJ6s8pp5zCKaec0u4wWqIV29JJ+2M0cH+qk9Stez4ZeAI4MyLGD0M8kiRJaoFm+q/7APA94MaIOAP4PfCP/mbIzL81sR5JkiQ1qW6Sd3ePvydRkr2BZBPrkaSlvqPZnlqxLWotj4k6Xd3kq5lOguxYSJIkaYTVTfJePSxRSJIkqaVqJXmZec9wBSJJkqTWsWdHSZKkDmSSJ0mS1IFqNddGxPuaWUlmntvMfJIkSWpO3RsvplG6RKkjAZM8SZKkEVQ3yfsb/Sd5k4CVq7+fBOY2E5QkSZKGpu7dtZMHKhMRGwPHAQcAn87MnzQXmiRJkprV8idRZOZfgIMj4lng3Ii4MzNvafV6JEmS1LfhvLv2BGAC8NlhXIckSZIaGLYkLzPvAx4HdhmudUiSJKmxljfXdouI5YCVgGeHax2SJElqbDiba99fLf/+YVyHJEmSGqjbGfKrBiiyHLAesB9wKKW7lYuaC02SJEnNqttce3eNsgHcCnyp5jokSZI0RHWba2OQr7soyd2OmflEy6KVJEnSoNStyXv1ANOfA+Zl5sIm45EkSVIL1H3ixT3DFYgkSZJaZzjvrpWkft1///1MnDiRiGDBggXtDkeSOsr/396dR8tVlvke/z4mCIQQEhImBUlAAcH2CkRl1IRJYAEqmIZwlUGUQRlF8IoCQbDVZQMyyqKRQaEBQYRWI3CFBBBQDLRtCx0mkyuDiBkhJCQQ3vvH3oecnNSZ69Sues/3s9ZeldrDW0/VW7vyO3s05EmqzGmnncbw4cOrLkOSstTl7tqI+Fg9XiSldH892pGUj/vvv58777yTM844g9NOO63qciQpO90dkzed4lp3/ZF68DqSBpHly5dzwgkncNZZZzFy5Miqy5GkLPVkd21PL5vS1SBJb7viiitYunQpX/7yl6suRZKy1d0Wtu4umVLLesCZwH4Y8CR1MHfuXM4880yuv/56VltttarLkoFoOwAAHUlJREFUkaRsdRnyenPJlIgYBpxaDmtTBLyZwBn9KVBSXr7xjW+www47sO+++1ZdiiRlrd/HykXEEOBY4JvA+hTh7nlgCnBtSumt/r6GpDw8/vjjXH311dx///0sWLAAgMWLi2unL1y4kCFDhrDmmmtWWaIkZaNfIS8iDgHOBTajCHfzge8Cl6SUXu9/ef0TEZ8FflI+/WJK6aoq65EGu6effpo33niDHXfccZVpG2+8MUcddRRXXeVqKkn10KeQFxF7Ad8BPkQR7pYAlwDfTSktqF95fRcRmwCXAosAL8QlNYFddtmFadOmrTTuzjvv5Hvf+x5Tp05ls802q6gyScpPr0JeRIyn2FI3kSLcLQeuAaaklF6sf3l9ExFBUddc4Dbgq9VWJAlgzJgxTJgwYaVxs2fPBmDXXXf1wsiSVEc9uuNFRLw3In4K/B7YjSLg/Rz4QErp6GYKeKUTKeo8Enit4lokSZIarsuQFxEbRsQPgceBz1CEu/uAHVNKB6WUnmxAjb0SEe+n2Np4kXfakJrfEUccQUrJrXiSVGfd7a59FliDItz9F/D1lNKdA15VH0XEUIoTLf6Kl26RJEmDWHchb02K25IlYBRweXG4W6+klNLmfaitL84CtgV2SSktadBrSpIkNZ1IqfNb00ZEPa5xl1JKQ+rQTpci4qPAg8AFKaXT242fApxNF5dQiYijgaMBNthgg+1vuummgS530Fi0aJG74TLWVf8+NfepBldT2xajt+h3G83yXqqw7pB1mbd8XtVlZK0e39G+8je69U2cOPHRlNL4WtO625J3zgDUU3flbtofA09R3FKtV1JKVwJXAowfPz51PPtPfTd9+vRVzqZUPrrq33Oua46fj2kHTet+pm40y3upwuThk7lx0Y1Vl5G1enxH+8rf6Lx1d1uzVvllGw60/Sn0eie7lP8tIv6N4oSMkxtWmSRJUgX6fVuzJrEU+FEn07ajOE7vt8CTwMONKkqSJKkqWYS88iSLL9SaVh6Tty1wnbc1kyRJg0WPLoYsSZKk1mLIkyRJylD2IS+lNCWlFO6qlSRJg0n2IU+SJGkwMuRJkiRlyJAnSZKUIUOeJElShgx5kiRJGTLkSZIkZciQJ0mSlCFDniRJUoYMeZIkSRky5EmSJGXIkCdJkpQhQ54kSU3m1ltvZaeddmL06NGsscYabLnllpx33nksW7as6tLUQoZWXYAkSVrZ3Llz2W233TjttNMYOXIkjzzyCFOmTOGll17i0ksvrbo8tQhDniRJTeaYY45Z6fnEiRN55ZVXuOyyy7jkkkuIiIoqUytxd60kSS1g9OjR7q5Vr7glT5KkJrV8+XKWLl3KY489xsUXX8xxxx3nVjz1mCFPkqQmtdZaa7F06VIADjvsML7//e9XXJFaibtrJUlqUg899BAPPPAA559/PnfccQfHH3981SWphbglT5KkJrXddtsBsMsuuzBmzBgOP/xwTj31VDbffPOKK1MrcEueJEktoC3wzZo1q+JK1CoMeZIktYAHH3wQgHHjxlVciVqFu2slSWoye++9N3vssQfbbLMNQ4YM4cEHH+T888/n4IMPdletesyQJ0lSk/nwhz/Mtddey+zZsxk6dCibbbYZ3/nOdzj22GOrLk0txJAnSVKTOffcczn33HOrLkMtzmPyJEmSMmTIkyRJypC7ayVJqtDE6yb2u41ph0+rQyXKjVvyJEmSMmTIkyRJypAhT5IkKUOGPEmSpAwZ8iRJkjJkyJMkScqQIU+SJClDhjxJkqQMGfIkSZIyZMiTJEnKkCFPkiQpQ4Y8SZKkDBnyJEmSMmTIkyRJypAhT5IkKUOGPEmSpAwZ8iRJkjJkyJMkScqQIU+SJClDhjxJg84tt9zCAQccwLvf/W6GDx/O9ttvz4033lh1WVKW6rG+9beNZqihCkOrLkCSGu2CCy5g3LhxXHjhhYwZM4apU6dy6KGHMmfOHE444YSqy5OyUo/1rb9tNEMNVYiUUtU1NJXx48enGTNmVF1GNqZPn86ECROqLkMDpKv+nXjdxMYW04lph09bZdycOXMYM2bMSuMOPfRQHn74YWbNmrXK/M3yXqowefhkblzU3FsrVPt73hON+I3u7fo2EG00Qw0DJSIeTSmNrzXN3bWSBp2OP9QA2267LS+++GIF1Uh5q8f61t82mqGGKhjyJAl4+OGH2WKLLaouQxoU6rG+9beNZqhhoHlMnqRB75577uH222/n6quvrroUKXv1WN/620Yz1NAIbsmTNKjNnj2bQw89lE9+8pMcccQRVZcjZa0e61t/22iGGhrFkCdp0Jo3bx777LMPm266KTfccEPV5UhZq8f61t82mqGGRsom5EXE6Ij4QkT8PCKeiYglEbEwIn4bEUdFRDbvVVL/LV68mP32249ly5bxy1/+kmHDhlVdkpSteqxv/W2jGWpotJyOyZsE/BD4GzAN+CuwAXAgcBWwT0RMSl4zRhr03nzzTSZNmsTTTz/NQw89xPrrr191SVK26rG+9beNZqihCjmFvKeAA4BfpZTeahsZEWcAjwAHUQS+n1VTnqRm8aUvfYmpU6dy0UUXMXfuXObOnfv2tG233ZbVV1+9wuqkvNRjfetvG81QQxWyCXkppXs7Gf9SRFwBfBuYgCFPGvTuvvtuAE466aRVps2aNYuxY8c2uCIpX/VY3/rbRjPUUIVsQl433igf36y0CklNYfbs2VWXIA0a9Vjf+ttGM9RQhexPRoiIocBh5dM7q6xFkiSpUbIPecB3gQ8AU1NKd1VdjCRJUiNEziebRsSJwEXATGDnlNK8TuY7GjgaYIMNNtj+pptualyRmVu0aBHDhw+vugwNkK7696m5TzW4Gg2EdYesy7zlNX861US2GN23W2vV+zfa9X6FvvZJb02cOPHRlNL4WtOyDXkRcTxwCfAEsHtK6aWeLDd+/Pg0Y8aMAa1tMJk+fToTJkyougwNkK76d+J1ExtbjAbE5OGTuXHRjVWXoW5MO3xan5ar92+06/0Kfe2T3oqITkNelrtrI+JkioD3Z2BiTwOeJElSLrILeRHxNeBC4I8UAe/likuSJElquKxCXkScSXGixaMUu2jnVFySJElSJbK5Tl5EHA58C1gOPACcGBEdZ5udUrq2waVJkiQ1XDYhDxhXPg4BTu5knvuAaxtSjSRJUoWy2V2bUpqSUopuhglV1ylJktQI2YQ8SZIkrWDIkyRJypAhT5IkKUOGPEmSpAwZ8iRJkjJkyJMkScqQIU+SJClDhjxJkqQMGfIkSZIyZMiTJEnKkCFPkiQpQ4a8JvLMM89wzDHH8MEPfpAhQ4YwYcKEqktqafX4PJuljWbR8b2cfPLJVZckqRP+fmlo1QVohccff5ypU6eyww478MYbb1RdTsurx+fZLG00i5zei5Q7f7/klrwmsv/++/Pcc89xyy23sM0221RdTsurx+fZLG00i5zei5Q7f79kyGsi73iH3VFP9fg8m6WNZpHTe5Fy5++X7D1JkqQMGfIkSZIyZMiTJEnKkCFPkiQpQ4Y8SZKkDBnyJEmSMuTFkJvI4sWLmTp1KgAvvPACr7zyCrfeeisA++67L8OGDauyvJZTj8+zWdpoFh3fy4IFC1r2vUi58/dLkVKquoamMn78+DRjxoxKXnv27NmMGzeu5rRZs2YxduzYxhZUB9OnT6/sNjj1+DybpY1m0Zv3MvG6iQ2qSgNp8vDJ3LjoxqrLUDemHT5tlXE9WV+7+43u7e+X6/0KtfpkIETEoyml8bWmuSWviYwdOxZDd/3U4/NsljaaRcf3UmWIl9Q1f7/kMXmSJEkZMuRJkiRlyN21FajHMQuN2tffKnI5DqRZ+rWnn+fk4ZM557pzBrgaSd3p62+g63De3JInSZKUIUOeJElShgx5kiRJGTLkSZIkZciQJ0mSlCFDniRJUoYMeZIkSRky5EmSJGXIkCdJkpQhQ54kSVKGDHmSJEkZMuRJkiRlyJAnSZKUIUOeJElShgx5kiRJGTLkSZIkZciQJ0mSlCFDniRJUoYMeZIkSRky5GXmiSeeYPfdd2fYsGG8613v4qyzzmL58uWVtbH33ntXWkcumqVfJUmtY2jVBah+5s+fzx577MHWW2/NHXfcwbPPPsupp57KW2+9xXnnnVdJG+eddx7Dhw+vpI5cNEu/SpJaiyEvI1dccQVLlizhtttuY8SIEey555688sorTJkyhdNPP50RI0Y0vI3HHnuMCRMmVFJHLpqlXyVJrcXdtRn59a9/zSc+8YmV/sM+5JBDWLJkCffdd9+gayMXfp6SpL4w5GVk5syZbLXVViuNe8973sOwYcOYOXPmoGsjF36ekqS+MORlZP78+YwcOXKV8aNGjWL+/PmDro1c+HlKkvrCkCdJkpQhQ15GRo0axcKFC1cZP3/+fEaNGjXo2siFn6ckqS+yCnkRsXFEXB0RL0bE0oiYHRE/iIhB8b/YVltttcrxVc899xyLFy9e5XiswdBGLvw8JUl9kU3Ii4jNgUeBI4FHgAuBvwAnAQ9HxOgKy2uIffbZh7vuuotXX3317XE333wza665Jh//+McHXRu58POUJPVFNiEPuBxYHzgxpfSplNL/SSntRhH2tgS+XWl1DXDsscey+uqrc+CBB/Kb3/yGK6+8kilTpvCVr3ylx9dBq3cbjz76aGV15KJZ+lWS1FqyCHnlVry9gNnAZR0mnw28BnwuItZqcGkNNWrUKO655x6WL1/O/vvvz9lnn80pp5zCOeecU1kbZ5xxRmV15KJZ+lWS1FpyuePFxPLx7pTSW+0npJRejYgHKULgDsA9jS6ukbbeemvuvffepmlj+vTpTJgwobI6ctEs/SpJah1ZbMmj2B0L8FQn058uH7doQC2SJEmVyyXkrVM+rnqNiJXHr3o1WEmSpAzlsru2XyLiaODo8umiiHiyynp6Io6IqkvoqTHAnKqLaBUt1K8ATGe6/Zs5+zhv9u/AaeDv+aadTcgl5LVtqVunk+lt4xfUmphSuhK4st5FCSJiRkppfNV1aGDYv/mzj/Nm/+Ytl921bVveOjvm7n3lY2fH7EmSJGUll5A3rXzcKyJWek8RsTawM7AY+F2jC5MkSapCFiEvpfQscDcwFvhyh8nnAGsBP0kpvdbg0uRu8NzZv/mzj/Nm/2YsUkpV11AX5QWRH6K468UdwP8AH6W4ht5TwE4ppbnVVShJktQ42YQ8gIjYBPgWsDcwGvgb8HPgnJTS/CprkyRJaqSsQp4kSZIKWRyTp+pFxFURkcrhvZ3MMyQiTomIP0XEkoiYFxFTI2KnLtpdMyLOiYgnI+L1iHg5In4aEe8fuHejiHhfRHwtIu6NiOciYllE/D0i7oiIid0se3hEPBIRiyJiYURMj4j9upi/198LDZyI2Dgiro6IFyNiaUTMjogfRMSoqmvTChExOiK+EBE/j4hnynVnYUT8NiKO6ngSYrvldirXr3nlMn+KiJMjYkgXr7VfuR4vLNfr30fE4QP37lQvbslTv0XE/sB/AIuA4cD7UkrPdJgngJ8Cn6G45M0vgHWBg4E1gINSSnd0WGZ1insN7wzMAO4FNgEmAcuA3VJKvx+4dzZ4RcRNFH3zBPBbYB7F7QMPAIYAJ6WULq6x3L8CpwLPA7cC7wQOoejrE1JKl3aYv9ffCw2cGsc2zwQ+QnFs85PAzh7b3Bwi4ljghxSHJU0D/gpsABxIcW3YnwGTUrv/5CPik+X414GbKdbr/SnW7VtTSpNqvM7xwCXA3HKZZRTr68bA+Smlrw7QW1Q9pJQcHPo8AOsBLwE3AdOBBLy3xnyTy2kPAmu0G/9hYCnwMrB2h2W+Xi5zC/COduM/WY5/vP14h7r26xHAtjXGf5ziR34psFGHaTuV/fIMMKrd+LEU/0G8Dozt7/fCYUD7/a6yP07oMP6CcvwVVdfo8Haf7EYR0N7RYfyGFIEvUfyR1DZ+RLk+LQXGtxu/BkWwT8AhHdoaW663c9uvu8Cocj1PwI5VfxYOnQ/urlV/tZ1+3/HSNR0dVz5+M6X0etvIlNIfKP46XI/ir0Pg7S08x5ZPT08pvdVumTuAB4CtKUKH6iyldG1K6T9rjL+PIsy/kyLUtdfWX99O7U50SinNBi4DVgeO7LBMr74XGjjlVry9gNkU/dXe2cBrwOciYq0Gl6YaUkr3ppR+0f63sRz/EnBF+XRCu0mfoVifbkopzWg3/+vAN8unx7Gyz1Ost5eW63HbMvOBfymfHoualiFPfRYRRwCfAo5JXezCiYg1KALBYopw1tGvy8fd2o3bHHgP8FRKaVYPl1FjvFE+vtlhfFtf3FljmVX6q4/fCw2ctmMt764RHF6l2No6DNih0YWp12qto12tn/dTrIc7lYfJ9GQZ188WYMhTn0TEpsBFwPWp+2OmNqc4jusvKaWOwQDg6fKx/W3ptiwfO7sVXa1lNMDKft+d4j+E+9uNXwt4N7AopfS3GovW6q++fC80cFznMhARQ4HDyqftw1mn/Vuuf7Mo7me/WQ+X+RvF1t2NI2JYP8vWADHkqdfKs7auozjR4sQeLLJO+biwk+lt40f2cxkNoPIv/Bsodt9MSStfe9I+bn32Rx6+C3wAmJpSuqvd+IFcR9fpZLoqZsgbpMrLIqReDNe3W/wUimPhvpi8yHTT6mcfd2xrCPATijOdbwb+tVHvQ1LPRMSJFGe3zwQ+V3E5agJDqy5AlXmW4qypnnoRICK2AL4NXJNSmtrDZbv7a69t/IJ+LqOV9amPOyoD3vUUl675KfDZlFLHay/Zx63P/mhh5aVOLqK47NHuKaV5HWbp6zo6ppxW67jr7rb0qWKGvEEqpbR7HxfdmvIsyYjoeKZkm6eLk2P5dErpdoqwsRzYLCKG1jj+6n3lY/vjPp4sHzs7/qfWMmqnH338tohYjWIX7STg34HDUkrLa7zWaxHxAvDuiNioxnF5tfqrL98LDRzXuRYVEScDFwJ/pgh4L9eY7UlgPEX/Ptph+aHAOIoTNf7SYZkx5TIPd1hmI2At4PmU0uL6vBPVm7tr1VuzgR91MrxUznNL+Xw2vH2K/kMUZ+btWqPNfcrHe9uNe5biWk9bRMS4Hi6jOoqId1L05STgx8DnagW8dtr6Yu8a01bprz5+LzRwppWPe3W8W0JErE2xq34x8LtGF6bORcTXKALeH4GJnQQ86Hr9/BjFevhQSmlpD5dx/WwFVV+ozyGfgf5fDHlEh2W8GHJ1fbk68Kvyc76qJ58zA3cx5BH1eE8OPep3L4bcQgNwZtkvM4B1u5l3BPAPencx5HF4MeSWHrytmeomIqZTnJDR3W3NZlLcvmo03d/W7F6K8DCD4hZn78Hbmg24iLiG4q4Xc4DLKX7MO5qeUpreYbnzga+w8m3NDqbo6+5ua9aj74UGTo3bmv0P8FGKa+g9BeyUvK1ZUyjvHXstxSEPl1D7uLjZKaVr2y3zKYr18nWKuxTNo7hV4Zbl+H9OHUJBRJwAXIy3NWtNVadMh3wGutiSV04fSnFm7n8DS4D5wFSK/zg6a3MY8C2Ka3QtpfhL9BZg66rfb85Du77sapjSybJHAH+guIbWq8B9wH5dvFavvxcOA9r3mwDXUNwTdRnw/4Af0G7rrEP1AzClB+vo9BrL7VyuX/PL9e2/y/VvSBevtX+5Hr9artd/AA6v+jNw6H5wS54kSVKGPPFCkiQpQ4Y8SZKkDBnyJEmSMmTIkyRJypAhT5IkKUOGPEmSpAwZ8iRJkjJkyJOkJhMRIyMilcOHqq5HUmsy5ElqGRExLiJeLcPPBd3Mu1FEzC3n/VEP2z+qXbj6QC/quqFc5sWIGNLT5SRpIBnyJLWMlNIs4LTy6UkRsWsXs18FrEtxW65TevgSNwOLyn9/vicLRMQ6wKfLp9ellJb38LUkaUAZ8iS1lJTSFcD/pfj9ujYi1uo4T0R8EdiX4v6dn08pvdLDthcBPy2ffjYiVuvBYpOBNct/X92T15GkRjDkSWpFRwGvAJsB328/ISLGAueXTy9PKd3by7bbdu2uB+zXg/nbtvg9kFJ6upevJUkDxpAnqeWklJ4DTi6fHhsRewJERADXAmsDzwCn96Hth4CZ5dMud9lGxDbAh8unP+owbUhE7B0Rl0fEI+XxessiYk5E3BMRR/bl+L2I+FR5/N+CLubp9sSNiBhaHoN4d0S8XNb2ckRMjYgDe1uXpOZjyJPUklJK1wC/AgL4UUSMoAh+HwfeAo5IKS3uY/NtgW3viNiwi/naQuCrwC0dpv0T8GvgOIoguBawBBgN7Eaxa/eXPdwlXFcRsRHwMMVxi3sCY4DFFFsv9wF+Vp5M4kkkUgsz5ElqZV8E5gObADcB3y7HX5BSerAf7f4YeAMYChxWa4YynH22fHpTjUC5FLiGIjSNSimtk1JaBxgJHAvMAfYGvt6POnstIt4J/AIYD/wO2AsYllIaCYwoa5sPHAp8s5G1SaqvSClVXYMk9VlEHArc0G7UE8B2KaWl/Wz3NoqzZmemlN5fY/qngdvKpzuklH7fy/b3oDiB5B/ABqndj3FEjKQIWgDbppT+2G7ap4CfAwvLYFar7a6WPwG4GHgU2DWltKTG8hOBeynONN6oPCFFUotxS56kVncL8Pd2z0/pb8Arte2y3Soidqwx/cjy8fHeBrzSPRRb+9YDtuzD8n11VPl4ca2AB5BSmgY8DwwHdm5UYZLqy5AnqdV9A9ig3fMv1KndO4EXy3+vdAJGeZzePuXTTi+bEhHDIuKk8kSLlyJiadsJERTHDa5ezrpxnWruUkSsDXywfHpBWVPNAWg7FnHTRtQmqf4MeZJaVkRsRxHyAKaWj5MiYlIn81/aSah5vuO85UWNryufHhwRw9pNPozieL03gJ908lqbAP8F/IDiRIsNgOUUu2f/Xg5tu2hXudbfANmQ4kQVKE4A2aCLYWg53zAktSRDnqSWFBGrU4SwocDvgQOAG8vJl0XEejUWG0ntQNPZGbRtW+nWBj7TbnzbrtpfpJT+0cmyVwDvBf4G/G9g/ZTSsJTS+imlDVNKGwKvtb2dTt9ofbU/W3bHlFL0YPhBg2qTVGeGPEmt6hzgA8DrwOHllrcTKLaQrQdc3nGBlNJnOwkyQzvOW87/DHBf+fTzAOXxeVuV42reE7c88WHvtuVSSv/eMQyWd+oY3vO3+7Y3y8c1uphnnU7Gtz920d2wUuYMeZJaTkTsAHy1fHpGSulJgJTSXOCYcvxnIuKf6/BybUHuYxGxOSuOz3sBuKuTZd7Fit/X/+xknj36WE/bWbOrd7K1EuCjtUamlOZTnH0MsH8fX19SizDkSWopEbEmxW7aIcADwEXtp6eU7mDFJVUui4j1+/mStwILKXapfgloC47XlVsPa1nY7t//q+PEiFiDYktkX/yZ4qQNgINqtL0aXd/p48ry8eCI2LWrF4qIdftUoaSmYMiT1Gr+BdiC4ni2I1NKb9WY50SKY+HGUGO3bW+UlxlpO9bvZIoLBie6OKs2pfQCRRgDuDwidipvuUZEbA/8BtgcWNaHehay4iST8yJi/4gYWrb9T+W0cV00cQXFMYxDgTsj4qvtg3BErB0Re0bEVRQnjkhqUYY8SS0jIj4GnFQ+PT2l9Gyt+VJK81ix2/agiDi4ny/dtsu27Tfzvs5eu53jKa6DtznwILA4Il4FZlDcbeJzFLc564uvAHMpzpD9D+C1su0/AdsDnb7f8hqC+1Fc7HgY8H3g7xGxoLwf7kLgborr6XV13J+kJmfIk9QSyhMVrqHYbXoP8MOu5k8p/YIVlze5tD+7bVNKMygCVJtOt+K1W+Y+YEfgdmAexe7lBcD1wEdSSrf3o56ngY9Q3H7tJYrPZAHFvWg/RBEku1p+DsUxgW137XieItCtATxHsTXwq8B2fa1RUvW8rZkkSVKG3JInSZKUIUOeJElShgx5kiRJGTLkSZIkZciQJ0mSlCFDniRJUoYMeZIkSRky5EmSJGXIkCdJkpQhQ54kSVKGDHmSJEkZ+v+IxIYoO4SogAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "n, bins, patches = plt.hist(Store_Optimal_X[:,0], 30, facecolor='g', alpha=0.75)\n",
    "def autolabel(rects):\n",
    "    \"\"\"\n",
    "    Attach a text label above each bar displaying its height\n",
    "    \"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        plt.text(rect.get_x() + rect.get_width()/2., 1.0*height,\n",
    "                '%d' % int(height),\n",
    "                ha='center', va='bottom',fontsize=15)\n",
    "plt.xlabel('X-Value',fontsize=25)\n",
    "plt.ylabel('Number of Occurence',fontsize=25)\n",
    "plt.title('Hyperopt Optimal Objective: Iterations 20',fontsize=20)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.grid(True)\n",
    "autolabel(patches)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "n, bins, patches = plt.hist(Store_Optimal_X[:,1], 30, facecolor='g', alpha=0.75)\n",
    "def autolabel(rects):\n",
    "    \"\"\"\n",
    "    Attach a text label above each bar displaying its height\n",
    "    \"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        plt.text(rect.get_x() + rect.get_width()/2., 1.0*height,\n",
    "                '%d' % int(height),\n",
    "                ha='center', va='bottom',fontsize=15)\n",
    "plt.xlabel('X-Value',fontsize=25)\n",
    "plt.ylabel('Number of Occurence',fontsize=25)\n",
    "plt.title('Optimal Objective: Iterations 30',fontsize=20)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.grid(True)\n",
    "autolabel(patches)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "n, bins, patches = plt.hist(Store_Optimal_X[:,2], 30, facecolor='g', alpha=0.75)\n",
    "def autolabel(rects):\n",
    "    \"\"\"\n",
    "    Attach a text label above each bar displaying its height\n",
    "    \"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        plt.text(rect.get_x() + rect.get_width()/2., 1.0*height,\n",
    "                '%d' % int(height),\n",
    "                ha='center', va='bottom',fontsize=15)\n",
    "plt.xlabel('X-Value',fontsize=25)\n",
    "plt.ylabel('Number of Occurence',fontsize=25)\n",
    "plt.title('Optimal Objective: Iterations 40',fontsize=20)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.grid(True)\n",
    "autolabel(patches)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "n, bins, patches = plt.hist(Store_Optimal_X[:,3], 30, facecolor='g', alpha=0.75)\n",
    "def autolabel(rects):\n",
    "    \"\"\"\n",
    "    Attach a text label above each bar displaying its height\n",
    "    \"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        plt.text(rect.get_x() + rect.get_width()/2., 1.0*height,\n",
    "                '%d' % int(height),\n",
    "                ha='center', va='bottom',fontsize=15)\n",
    "plt.xlabel('X-Value',fontsize=25)\n",
    "plt.ylabel('Number of Occurence',fontsize=25)\n",
    "plt.title('Optimal Objective: Iterations 60',fontsize=20)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.grid(True)\n",
    "autolabel(patches)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mango_env",
   "language": "python",
   "name": "mango_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}