NMTF-link_Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of NMTF-link run and outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NMTF-link\n",
    "This tool is designed for computing link predictions using the Non Negative Matrix Tri-Factorization (NMTF) method. This is generalization for already pre-existing code in [DEIB-GECO/NMTF-DrugRepositioning](https://github.com/DEIB-GECO/NMTF-DrugRepositioning) allowing the use of networks of any topology.\n",
    "\n",
    "Make sure to install the required libraries in the requirements file to run this example: [Requirements](https://github.com/DEIB-GECO/NMTF-link/blob/master/requirements.txt)\n",
    "\n",
    "Try this example online: [NMTF-link_Example](https://colab.research.google.com/drive/1JWuYjppKcUiNm0bJsHTjQzYoSK6MJ7Pm?usp=sharing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## On this Jupyter notebook we can run the NMTF-link algorithm as below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please specify the directory of your data and setting file: case_study_1\n",
      "Please specify the name of your setting file: graph_topology.tsv\n",
      "\n",
      "Evaluation metric is \u001b[1mAPS\u001b[0m\n",
      "Maximum number of iteration is \u001b[1m200\u001b[0m\n",
      "Initialization strategy is \u001b[1mrandom\u001b[0m\n",
      "\n",
      "All specified nodes' categories: \u001b[1m['USERS', 'GENRES', 'MOVIES', 'ACTORS']\u001b[0m\n",
      "\n",
      "Association matrix filename: C:\\Users\\gaiac\\OneDrive\\Documenti\\GitHub\\NMTF-link\\case_study_1\\UsersToGenres.txt\n",
      "Used parameters: \u001b[1m k₁ = 100 and k₂ = 100\u001b[0m\n",
      "Non-zero elements of the association matrix = \u001b[1m30\u001b[0m\n",
      "USERS GENRES (10, 7)\n",
      "Shape Factor Matrix left (10, 100)\n",
      "Shape Factor Matrix right (7, 100)\n",
      "\n",
      "Association matrix filename: C:\\Users\\gaiac\\OneDrive\\Documenti\\GitHub\\NMTF-link\\case_study_1\\UsersToMovies.txt\n",
      "Used parameters: \u001b[1m k₁ = 100 and k₂ = 100\u001b[0m\n",
      "Non-zero elements of the association matrix = \u001b[1m57\u001b[0m\n",
      "USERS MOVIES (10, 26)\n",
      "Shape Factor Matrix left (10, 100)\n",
      "Shape Factor Matrix right (26, 100)\n",
      "\n",
      "Association matrix filename: C:\\Users\\gaiac\\OneDrive\\Documenti\\GitHub\\NMTF-link\\case_study_1\\MoviesToActors.txt\n",
      "Used parameters: \u001b[1m k₁ = 100 and k₂ = 100\u001b[0m\n",
      "Non-zero elements of the association matrix = \u001b[1m28\u001b[0m\n",
      "MOVIES ACTORS (26, 7)\n",
      "Shape Factor Matrix left (26, 100)\n",
      "Shape Factor Matrix right (7, 100)\n",
      "\n",
      "Association matrix filename: C:\\Users\\gaiac\\OneDrive\\Documenti\\GitHub\\NMTF-link\\case_study_1\\MoviesToGenres.txt\n",
      "Used parameters: \u001b[1m k₁ = 100 and k₂ = 100\u001b[0m\n",
      "Non-zero elements of the association matrix = \u001b[1m38\u001b[0m\n",
      "MOVIES GENRES (26, 7)\n",
      "Shape Factor Matrix left (26, 100)\n",
      "Shape Factor Matrix right (7, 100)\n",
      "\n",
      "\u001b[1mRun number 1 of the algorithm\u001b[0m\n",
      "initial error: 511439693300.35583\n",
      "iteration 1, relative error = 0\n",
      "iteration 2, relative error = 0\n",
      "iteration 3, relative error = 0.922005815280027\n",
      "iteration 4, relative error = 0.4161276290145675\n",
      "iteration 5, relative error = 0.12539766017643575\n",
      "iteration 6, relative error = 0.21411268506378875\n",
      "iteration 7, relative error = 3.8709892787858973\n",
      "iteration 8, relative error = 0.00665056499995448\n",
      "iteration 9, relative error = 0.7394443364425399\n",
      "iteration 10, relative error = 0.34131713351174625\n",
      "iteration 11, relative error = 0.02964395792222637\n",
      "iteration 12, relative error = 0.0005586942633883769\n",
      "iteration 13, relative error = 0.005078462419960846\n",
      "iteration 14, relative error = 0.006228995145104061\n",
      "iteration 15, relative error = 0.009144737897218022\n",
      "iteration 16, relative error = 0.01663771190599636\n",
      "iteration 17, relative error = 0.019728070344940996\n",
      "iteration 18, relative error = 0.015478246001436095\n",
      "iteration 19, relative error = 0.009829767959229754\n",
      "iteration 20, relative error = 0.006835905388126325\n",
      "iteration 21, relative error = 0.005238481064679228\n",
      "iteration 22, relative error = 0.004286041523503855\n",
      "iteration 23, relative error = 0.00373544620629693\n",
      "iteration 24, relative error = 0.0034153911787310766\n",
      "iteration 25, relative error = 0.003255273669168578\n",
      "iteration 26, relative error = 0.003236197510530785\n",
      "iteration 27, relative error = 0.003345366903121587\n",
      "iteration 28, relative error = 0.003557902564780034\n",
      "iteration 29, relative error = 0.003837444831676431\n",
      "iteration 30, relative error = 0.004142559242268935\n",
      "iteration 31, relative error = 0.004436176809791104\n",
      "iteration 32, relative error = 0.004696285503512188\n",
      "iteration 33, relative error = 0.004926178197587179\n",
      "iteration 34, relative error = 0.0051662003935611656\n",
      "iteration 35, relative error = 0.005472080519808967\n",
      "iteration 36, relative error = 0.005759701525034187\n",
      "iteration 37, relative error = 0.00585242490950802\n",
      "iteration 38, relative error = 0.00583544830058957\n",
      "iteration 39, relative error = 0.005857083638220629\n",
      "iteration 40, relative error = 0.0059349779984946335\n",
      "iteration 41, relative error = 0.006036212076900752\n",
      "iteration 42, relative error = 0.006138503505324442\n",
      "iteration 43, relative error = 0.006232827947375602\n",
      "iteration 44, relative error = 0.006300089679532273\n",
      "iteration 45, relative error = 0.006290083930899633\n",
      "iteration 46, relative error = 0.006131675028440001\n",
      "iteration 47, relative error = 0.005772861370413875\n",
      "iteration 48, relative error = 0.005217719657539303\n",
      "iteration 49, relative error = 0.004541070412242426\n",
      "iteration 50, relative error = 0.0038624772973508614\n",
      "iteration 51, relative error = 0.003270237952922575\n",
      "iteration 52, relative error = 0.002775511366410156\n",
      "iteration 53, relative error = 0.0023545647002955984\n",
      "iteration 54, relative error = 0.0020021020333903052\n",
      "iteration 55, relative error = 0.001728500981949302\n",
      "iteration 56, relative error = 0.0015369465253501037\n",
      "iteration 57, relative error = 0.0014208760404607929\n",
      "iteration 58, relative error = 0.001374162138762546\n",
      "iteration 59, relative error = 0.0013965072502600596\n",
      "iteration 60, relative error = 0.001486550397166157\n",
      "iteration 61, relative error = 0.0016224396891161025\n",
      "iteration 62, relative error = 0.0017479797413867508\n",
      "iteration 63, relative error = 0.0018056967846862028\n",
      "iteration 64, relative error = 0.0018046438806575227\n",
      "iteration 65, relative error = 0.001808475627627371\n",
      "iteration 66, relative error = 0.0018316986927174623\n",
      "iteration 67, relative error = 0.0018032881187957353\n",
      "iteration 68, relative error = 0.0016562760386012035\n",
      "iteration 69, relative error = 0.0014107767007242795\n",
      "iteration 70, relative error = 0.0011444142093559556\n",
      "iteration 71, relative error = 0.0009204934747890169\n",
      "iteration 72, relative error = 0.00076017535314188\n",
      "iteration 73, relative error = 0.0006551245078134899\n",
      "iteration 74, relative error = 0.0005880405820349007\n",
      "iteration 75, relative error = 0.00054411611385003\n",
      "iteration 76, relative error = 0.0005136494242462628\n",
      "iteration 77, relative error = 0.0004910007733958804\n",
      "iteration 78, relative error = 0.00047307324474840954\n",
      "iteration 79, relative error = 0.00045826265403108886\n",
      "iteration 80, relative error = 0.00044591654593061677\n",
      "iteration 81, relative error = 0.0004361834640643258\n",
      "iteration 82, relative error = 0.000430172740302984\n",
      "iteration 83, relative error = 0.0004302610053171064\n",
      "iteration 84, relative error = 0.0004397057255314557\n",
      "iteration 85, relative error = 0.0004591230252757382\n",
      "iteration 86, relative error = 0.0004787729766383108\n",
      "iteration 87, relative error = 0.0004789078365862233\n",
      "iteration 88, relative error = 0.00045107241776198343\n",
      "iteration 89, relative error = 0.0004095338712939136\n",
      "iteration 90, relative error = 0.0003723843921693611\n",
      "iteration 91, relative error = 0.0003476902028657423\n",
      "iteration 92, relative error = 0.00033787587174573556\n",
      "iteration 93, relative error = 0.0003459434864934752\n",
      "iteration 94, relative error = 0.0003710852223742739\n",
      "iteration 95, relative error = 0.0003946364587296997\n",
      "iteration 96, relative error = 0.00039202092085615926\n",
      "iteration 97, relative error = 0.0003699396690249365\n",
      "iteration 98, relative error = 0.00035365688377364594\n",
      "iteration 99, relative error = 0.0003650414333334994\n",
      "iteration 100, relative error = 0.00044559429838111057\n",
      "iteration 101, relative error = 0.0006873129826281206\n",
      "iteration 102, relative error = 0.001224033898419131\n",
      "iteration 103, relative error = 0.0019681900596421904\n",
      "iteration 104, relative error = 0.0019897384567588773\n",
      "iteration 105, relative error = 0.0003223684499163438\n",
      "iteration 106, relative error = 0.0018688003086152326\n",
      "iteration 107, relative error = 0.0027292816305146315\n",
      "iteration 108, relative error = 0.002060388223330519\n",
      "iteration 109, relative error = 0.0007216185926753391\n",
      "iteration 110, relative error = 0.0005632705676007057\n",
      "iteration 111, relative error = 0.001521816034275071\n",
      "iteration 112, relative error = 0.0021607162499682497\n",
      "iteration 113, relative error = 0.002516669456211931\n",
      "iteration 114, relative error = 0.0025401107945528874\n",
      "iteration 115, relative error = 0.0022441775711592627\n",
      "iteration 116, relative error = 0.001826423002713868\n",
      "iteration 117, relative error = 0.0014826755438798658\n",
      "iteration 118, relative error = 0.0012684862225252086\n",
      "iteration 119, relative error = 0.0011496883569993108\n",
      "iteration 120, relative error = 0.0010750111685627897\n",
      "iteration 121, relative error = 0.0010069114298699138\n",
      "iteration 122, relative error = 0.0009271317396835296\n",
      "iteration 123, relative error = 0.0008328923165779326\n",
      "iteration 124, relative error = 0.0007302574944414859\n",
      "iteration 125, relative error = 0.0006281051024220493\n",
      "iteration 126, relative error = 0.0005343228634958523\n",
      "iteration 127, relative error = 0.00045416479445527317\n",
      "iteration 128, relative error = 0.00039002713343382745\n",
      "iteration 129, relative error = 0.000341990753063054\n",
      "iteration 130, relative error = 0.0003086641770408097\n",
      "iteration 131, relative error = 0.00028798896464357095\n",
      "iteration 132, relative error = 0.0002778110006069788\n",
      "iteration 133, relative error = 0.00027616723414261807\n",
      "iteration 134, relative error = 0.00028133189968109287\n",
      "iteration 135, relative error = 0.0002916975322804622\n",
      "iteration 136, relative error = 0.00030556497506989514\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 137, relative error = 0.0003209193311920788\n",
      "iteration 138, relative error = 0.0003352923089151641\n",
      "iteration 139, relative error = 0.0003458395658783871\n",
      "iteration 140, relative error = 0.00034974114220806914\n",
      "iteration 141, relative error = 0.00034490281819481085\n",
      "iteration 142, relative error = 0.0003307101041931821\n",
      "iteration 143, relative error = 0.0003084215810880345\n",
      "iteration 144, relative error = 0.0002808912410280189\n",
      "iteration 145, relative error = 0.0002516877058683758\n",
      "iteration 146, relative error = 0.00022404484357195088\n",
      "iteration 147, relative error = 0.0002001274688770712\n",
      "iteration 148, relative error = 0.00018083019890158784\n",
      "iteration 149, relative error = 0.00016601132039310187\n",
      "iteration 150, relative error = 0.00015491568899726301\n",
      "iteration 151, relative error = 0.0001465750213059179\n",
      "iteration 152, relative error = 0.00014008354828494066\n",
      "iteration 153, relative error = 0.00013473918986158258\n",
      "iteration 154, relative error = 0.00013008499907527543\n",
      "iteration 155, relative error = 0.00012589339465488597\n",
      "iteration 156, relative error = 0.0001221266619657427\n",
      "iteration 157, relative error = 0.00011889516695229658\n",
      "iteration 158, relative error = 0.00011642561860236273\n",
      "iteration 159, relative error = 0.0001150467174493392\n",
      "iteration 160, relative error = 0.00011519807094262438\n",
      "iteration 161, relative error = 0.0001174696189096181\n",
      "iteration 162, relative error = 0.00012268265551899023\n",
      "iteration 163, relative error = 0.00013202979284728314\n",
      "iteration 164, relative error = 0.00014729926663316448\n",
      "iteration 165, relative error = 0.0001712149537182543\n",
      "iteration 166, relative error = 0.00020791152385683806\n",
      "iteration 167, relative error = 0.0002634794137325851\n",
      "iteration 168, relative error = 0.0003461836734760513\n",
      "iteration 169, relative error = 0.00046489951711372625\n",
      "iteration 170, relative error = 0.0006215140978410754\n",
      "iteration 171, relative error = 0.0007886313278922812\n",
      "iteration 172, relative error = 0.0008715983574344062\n",
      "iteration 173, relative error = 0.0007243469490823757\n",
      "iteration 174, relative error = 0.00036687599014276575\n",
      "iteration 175, relative error = 0.00013764605227758843\n",
      "iteration 176, relative error = 0.00020861667681374937\n",
      "iteration 177, relative error = 0.00033756539467954123\n",
      "iteration 178, relative error = 0.00035399896650613143\n",
      "iteration 179, relative error = 0.0002938059174547838\n",
      "iteration 180, relative error = 0.00022020122474539786\n",
      "iteration 181, relative error = 0.00015891882671426437\n",
      "iteration 182, relative error = 0.00011346729215422509\n",
      "iteration 183, relative error = 8.100097598743523e-05\n",
      "iteration 184, relative error = 5.812943459643399e-05\n",
      "iteration 185, relative error = 4.218686854078182e-05\n",
      "iteration 186, relative error = 3.124039122208755e-05\n",
      "iteration 187, relative error = 2.3881349056693083e-05\n",
      "iteration 188, relative error = 1.9045366803008473e-05\n",
      "iteration 189, relative error = 1.5895786081364067e-05\n",
      "iteration 190, relative error = 1.3759460550627401e-05\n",
      "iteration 191, relative error = 1.209727543897209e-05\n",
      "iteration 192, relative error = 1.0493603997270709e-05\n",
      "iteration 193, relative error = 8.652578958240344e-06\n",
      "iteration 194, relative error = 6.393102825018804e-06\n",
      "iteration 195, relative error = 3.6384169692849184e-06\n",
      "iteration 196, relative error = 3.9937086308144785e-07\n",
      "iteration 197, relative error = 3.247077613987726e-06\n",
      "iteration 198, relative error = 7.181326825913244e-06\n",
      "iteration 199, relative error = 1.1263547502765549e-05\n",
      "iteration 200, relative error = 1.5352624665606132e-05\n",
      "\u001b[1mRun number 2 of the algorithm\u001b[0m\n",
      "initial error: 544960556475.3605\n",
      "iteration 1, relative error = 0\n",
      "iteration 2, relative error = 0\n",
      "iteration 3, relative error = 0.9240562316433962\n",
      "iteration 4, relative error = 0.42526129671018004\n",
      "iteration 5, relative error = 0.12865941149046745\n",
      "iteration 6, relative error = 0.22339949076635546\n",
      "iteration 7, relative error = 3.5348510670215907\n",
      "iteration 8, relative error = 0.056931309096176726\n",
      "iteration 9, relative error = 0.7259668461853923\n",
      "iteration 10, relative error = 0.3721020484160556\n",
      "iteration 11, relative error = 0.03339943001983131\n",
      "iteration 12, relative error = 0.007914479060642722\n",
      "iteration 13, relative error = 0.0006413737937612947\n",
      "iteration 14, relative error = 0.004539091070089851\n",
      "iteration 15, relative error = 0.002678784957343642\n",
      "iteration 16, relative error = 0.00032101077661669423\n",
      "iteration 17, relative error = 0.000600313250281631\n",
      "iteration 18, relative error = 0.0020648670271837524\n",
      "iteration 19, relative error = 0.00316469525021875\n",
      "iteration 20, relative error = 0.0009534322552904673\n",
      "iteration 21, relative error = 0.0002003790530617657\n",
      "iteration 22, relative error = 0.0066085065798649175\n",
      "iteration 23, relative error = 0.006835856890331521\n",
      "iteration 24, relative error = 0.00443578327682615\n",
      "iteration 25, relative error = 0.01638688635769128\n",
      "iteration 26, relative error = 0.015019287300054592\n",
      "iteration 27, relative error = 0.011829294454433146\n",
      "iteration 28, relative error = 0.010359478078122972\n",
      "iteration 29, relative error = 0.009199873666173346\n",
      "iteration 30, relative error = 0.007946862471585455\n",
      "iteration 31, relative error = 0.006640792200503887\n",
      "iteration 32, relative error = 0.005417703779912375\n",
      "iteration 33, relative error = 0.004401784211720002\n",
      "iteration 34, relative error = 0.003682967537955353\n",
      "iteration 35, relative error = 0.0032601657248941983\n",
      "iteration 36, relative error = 0.0030944247118006914\n",
      "iteration 37, relative error = 0.0031873837323638868\n",
      "iteration 38, relative error = 0.003511208333623661\n",
      "iteration 39, relative error = 0.0037304245770639786\n",
      "iteration 40, relative error = 0.003535104517314386\n",
      "iteration 41, relative error = 0.003178580647254426\n",
      "iteration 42, relative error = 0.002939443688737745\n",
      "iteration 43, relative error = 0.0028441761292154408\n",
      "iteration 44, relative error = 0.0028381523662195304\n",
      "iteration 45, relative error = 0.0028802647557437974\n",
      "iteration 46, relative error = 0.0029489484277655633\n",
      "iteration 47, relative error = 0.0030328300305834635\n",
      "iteration 48, relative error = 0.003124969961167978\n",
      "iteration 49, relative error = 0.0032203897731302787\n",
      "iteration 50, relative error = 0.003314742889133719\n",
      "iteration 51, relative error = 0.003403073466580719\n",
      "iteration 52, relative error = 0.003478852448356357\n",
      "iteration 53, relative error = 0.00353436590735081\n",
      "iteration 54, relative error = 0.0035633547426997896\n",
      "iteration 55, relative error = 0.0035652488512562626\n",
      "iteration 56, relative error = 0.0035492310398700217\n",
      "iteration 57, relative error = 0.0035412092404964257\n",
      "iteration 58, relative error = 0.0036098126422399205\n",
      "iteration 59, relative error = 0.003828939627357717\n",
      "iteration 60, relative error = 0.0039647213357385976\n",
      "iteration 61, relative error = 0.003817244579720713\n",
      "iteration 62, relative error = 0.003578452678507615\n",
      "iteration 63, relative error = 0.003351885326008095\n",
      "iteration 64, relative error = 0.0031143506308130902\n",
      "iteration 65, relative error = 0.0028468008289516257\n",
      "iteration 66, relative error = 0.0025602811801149383\n",
      "iteration 67, relative error = 0.00228018457082404\n",
      "iteration 68, relative error = 0.0020300708798492192\n",
      "iteration 69, relative error = 0.0018232891343929192\n",
      "iteration 70, relative error = 0.0016618400110238849\n",
      "iteration 71, relative error = 0.0015395488226403538\n",
      "iteration 72, relative error = 0.00144647725264513\n",
      "iteration 73, relative error = 0.0013724284886947062\n",
      "iteration 74, relative error = 0.0013087023586757698\n",
      "iteration 75, relative error = 0.0012484322497190114\n",
      "iteration 76, relative error = 0.0011863859126522234\n",
      "iteration 77, relative error = 0.001118890870668851\n",
      "iteration 78, relative error = 0.0010439686190815121\n",
      "iteration 79, relative error = 0.0009613969965511765\n",
      "iteration 80, relative error = 0.0008724680360326024\n",
      "iteration 81, relative error = 0.0007794604315737679\n",
      "iteration 82, relative error = 0.0006850166374150823\n",
      "iteration 83, relative error = 0.000591612200536363\n",
      "iteration 84, relative error = 0.0005012113428127264\n",
      "iteration 85, relative error = 0.0004151163422857249\n",
      "iteration 86, relative error = 0.0003339716123771441\n",
      "iteration 87, relative error = 0.0002578669528019979\n",
      "iteration 88, relative error = 0.00018648542761534373\n",
      "iteration 89, relative error = 0.00011925301682640324\n",
      "iteration 90, relative error = 5.546398008349621e-05\n",
      "iteration 91, relative error = 5.628170630228575e-06\n",
      "iteration 92, relative error = 6.475300619348645e-05\n",
      "iteration 93, relative error = 0.0001225921175938705\n",
      "iteration 94, relative error = 0.00017976653749361444\n",
      "iteration 95, relative error = 0.00023683540745779975\n",
      "iteration 96, relative error = 0.0002943003038605955\n",
      "iteration 97, relative error = 0.0003526111894547777\n",
      "iteration 98, relative error = 0.0004121710547613877\n",
      "iteration 99, relative error = 0.00047333689683224105\n",
      "iteration 100, relative error = 0.0005364148655970844\n",
      "iteration 101, relative error = 0.0006016474559494239\n",
      "iteration 102, relative error = 0.0006691910091746883\n",
      "iteration 103, relative error = 0.0007390830693498783\n",
      "iteration 104, relative error = 0.0008112008748150225\n",
      "iteration 105, relative error = 0.0008852094967550066\n",
      "iteration 106, relative error = 0.0009604766994381971\n",
      "iteration 107, relative error = 0.001035879787276851\n",
      "iteration 108, relative error = 0.0011094146384130544\n",
      "iteration 109, relative error = 0.001177842828869004\n",
      "iteration 110, relative error = 0.001237828801825096\n",
      "iteration 111, relative error = 0.0012917335697260274\n",
      "iteration 112, relative error = 0.0013566554770333102\n",
      "iteration 113, relative error = 0.0014525068530943828\n",
      "iteration 114, relative error = 0.001549697251001259\n",
      "iteration 115, relative error = 0.0015567386694597758\n",
      "iteration 116, relative error = 0.0014038922055340105\n",
      "iteration 117, relative error = 0.001098006971998988\n",
      "iteration 118, relative error = 0.0006908683368143285\n",
      "iteration 119, relative error = 0.00023737793218446781\n",
      "iteration 120, relative error = 0.00021740514985354323\n",
      "iteration 121, relative error = 0.0006365387487628774\n",
      "iteration 122, relative error = 0.0009926703505155272\n",
      "iteration 123, relative error = 0.0012823663255834585\n",
      "iteration 124, relative error = 0.0015475284122156566\n",
      "iteration 125, relative error = 0.0018281231976991809\n",
      "iteration 126, relative error = 0.002018444424250001\n",
      "iteration 127, relative error = 0.002025022101583334\n",
      "iteration 128, relative error = 0.0019737974087576326\n",
      "iteration 129, relative error = 0.001949371154849419\n",
      "iteration 130, relative error = 0.0018722646956110104\n",
      "iteration 131, relative error = 0.0016374655530486447\n",
      "iteration 132, relative error = 0.001235456338622345\n",
      "iteration 133, relative error = 0.0007544889421317954\n",
      "iteration 134, relative error = 0.00030706416795005205\n",
      "iteration 135, relative error = 3.494149460710478e-05\n",
      "iteration 136, relative error = 0.00025572350000576776\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 137, relative error = 0.0003763183638164705\n",
      "iteration 138, relative error = 0.00042944253440881493\n",
      "iteration 139, relative error = 0.00044352748803333936\n",
      "iteration 140, relative error = 0.00043733984043685365\n",
      "iteration 141, relative error = 0.00042106732134496915\n",
      "iteration 142, relative error = 0.0003995795548093047\n",
      "iteration 143, relative error = 0.00037526725003858466\n",
      "iteration 144, relative error = 0.0003496251675095049\n",
      "iteration 145, relative error = 0.0003238157017505413\n",
      "iteration 146, relative error = 0.00029874649202889616\n",
      "iteration 147, relative error = 0.0002750430077442775\n",
      "iteration 148, relative error = 0.0002530615708470781\n",
      "iteration 149, relative error = 0.0002329489744436865\n",
      "iteration 150, relative error = 0.0002147198091946453\n",
      "iteration 151, relative error = 0.00019834538582172182\n",
      "iteration 152, relative error = 0.00018393090293605563\n",
      "iteration 153, relative error = 0.00017243900687703812\n",
      "iteration 154, relative error = 0.00016940636252918645\n",
      "iteration 155, relative error = 0.00020144901307694843\n",
      "iteration 156, relative error = 0.00035410578942314313\n",
      "iteration 157, relative error = 0.000720988745860182\n",
      "iteration 158, relative error = 0.0011364978817134724\n",
      "iteration 159, relative error = 0.00129173263176138\n",
      "iteration 160, relative error = 0.0011813676265882583\n",
      "iteration 161, relative error = 0.0009740477953603487\n",
      "iteration 162, relative error = 0.0007731845846553993\n",
      "iteration 163, relative error = 0.0006056716774725316\n",
      "iteration 164, relative error = 0.00046888253708041454\n",
      "iteration 165, relative error = 0.00035556950826198173\n",
      "iteration 166, relative error = 0.0002605453660894486\n",
      "iteration 167, relative error = 0.00018090347280426842\n",
      "iteration 168, relative error = 0.00011510234003642378\n",
      "iteration 169, relative error = 6.226812121934569e-05\n",
      "iteration 170, relative error = 2.1763931594300435e-05\n",
      "iteration 171, relative error = 7.1211859201483725e-06\n",
      "iteration 172, relative error = 2.5422052481509306e-05\n",
      "iteration 173, relative error = 3.465002592872113e-05\n",
      "iteration 174, relative error = 3.6785567547889654e-05\n",
      "iteration 175, relative error = 3.409130425974266e-05\n",
      "iteration 176, relative error = 2.8804286827392482e-05\n",
      "iteration 177, relative error = 2.2822324707751558e-05\n",
      "iteration 178, relative error = 1.7494762494243908e-05\n",
      "iteration 179, relative error = 1.3569674255365433e-05\n",
      "iteration 180, relative error = 1.1278751141944886e-05\n",
      "iteration 181, relative error = 1.049777319366939e-05\n",
      "iteration 182, relative error = 1.0916512584020056e-05\n",
      "iteration 183, relative error = 1.2174090245553205e-05\n",
      "iteration 184, relative error = 1.3943854632480496e-05\n",
      "iteration 185, relative error = 1.597226871169443e-05\n",
      "iteration 186, relative error = 1.808554773563787e-05\n",
      "iteration 187, relative error = 2.0178568458286644e-05\n",
      "iteration 188, relative error = 2.2197150536992886e-05\n",
      "iteration 189, relative error = 2.4120472435631895e-05\n",
      "iteration 190, relative error = 2.5946808036153815e-05\n",
      "iteration 191, relative error = 2.76834338535331e-05\n",
      "iteration 192, relative error = 2.934031675838921e-05\n",
      "iteration 193, relative error = 3.092670723282596e-05\n",
      "iteration 194, relative error = 3.244970998184294e-05\n",
      "iteration 195, relative error = 3.391405014777504e-05\n",
      "iteration 196, relative error = 3.5322459451485224e-05\n",
      "iteration 197, relative error = 3.667630118998713e-05\n",
      "iteration 198, relative error = 3.797620800638024e-05\n",
      "iteration 199, relative error = 3.9222616853655305e-05\n",
      "iteration 200, relative error = 4.041615740040465e-05\n",
      "\u001b[1mRun number 3 of the algorithm\u001b[0m\n",
      "initial error: 590721600864.2605\n",
      "iteration 1, relative error = 0\n",
      "iteration 2, relative error = 0\n",
      "iteration 3, relative error = 0.9231366915253898\n",
      "iteration 4, relative error = 0.41883845587553026\n",
      "iteration 5, relative error = 0.13155106294244442\n",
      "iteration 6, relative error = 0.3834486374233482\n",
      "iteration 7, relative error = 4.059354536780985\n",
      "iteration 8, relative error = 0.5232369716988391\n",
      "iteration 9, relative error = 0.6359665246312591\n",
      "iteration 10, relative error = 0.18393360178776713\n",
      "iteration 11, relative error = 0.014589415730124903\n",
      "iteration 12, relative error = 0.003829294233683571\n",
      "iteration 13, relative error = 0.009123308003814523\n",
      "iteration 14, relative error = 0.009325997746596837\n",
      "iteration 15, relative error = 0.007898176311128217\n",
      "iteration 16, relative error = 0.008486509901993698\n",
      "iteration 17, relative error = 0.00927889336209043\n",
      "iteration 18, relative error = 0.008045499960853329\n",
      "iteration 19, relative error = 0.005497395205558399\n",
      "iteration 20, relative error = 0.003734709321253112\n",
      "iteration 21, relative error = 0.0034615901534495626\n",
      "iteration 22, relative error = 0.004034446987710503\n",
      "iteration 23, relative error = 0.004345966421144519\n",
      "iteration 24, relative error = 0.0041029708175854\n",
      "iteration 25, relative error = 0.003743408900422372\n",
      "iteration 26, relative error = 0.0034982722995489626\n",
      "iteration 27, relative error = 0.00337158821398139\n",
      "iteration 28, relative error = 0.0033191188128745656\n",
      "iteration 29, relative error = 0.003277925416967615\n",
      "iteration 30, relative error = 0.003165476251375044\n",
      "iteration 31, relative error = 0.0029535437154025366\n",
      "iteration 32, relative error = 0.002712122879734868\n",
      "iteration 33, relative error = 0.0025529346872836868\n",
      "iteration 34, relative error = 0.0026268297307257705\n",
      "iteration 35, relative error = 0.0029541207866908017\n",
      "iteration 36, relative error = 0.003121427510708249\n",
      "iteration 37, relative error = 0.0030165427521648227\n",
      "iteration 38, relative error = 0.0028352779226331307\n",
      "iteration 39, relative error = 0.0026298801264172454\n",
      "iteration 40, relative error = 0.0023757526555690752\n",
      "iteration 41, relative error = 0.0020833318569240585\n",
      "iteration 42, relative error = 0.0018108860596904213\n",
      "iteration 43, relative error = 0.0016261908032155178\n",
      "iteration 44, relative error = 0.0015650501432478488\n",
      "iteration 45, relative error = 0.0016192343174221211\n",
      "iteration 46, relative error = 0.0017529141903726054\n",
      "iteration 47, relative error = 0.0019257104324979866\n",
      "iteration 48, relative error = 0.0021066136250457263\n",
      "iteration 49, relative error = 0.0022771492704519378\n",
      "iteration 50, relative error = 0.002428684676329599\n",
      "iteration 51, relative error = 0.0025582428235623343\n",
      "iteration 52, relative error = 0.0026649837855815756\n",
      "iteration 53, relative error = 0.002748180445471191\n",
      "iteration 54, relative error = 0.0028068677424601244\n",
      "iteration 55, relative error = 0.002840711418179355\n",
      "iteration 56, relative error = 0.0028510902894556592\n",
      "iteration 57, relative error = 0.002841447054400096\n",
      "iteration 58, relative error = 0.002816717339155124\n",
      "iteration 59, relative error = 0.0027824516008818335\n",
      "iteration 60, relative error = 0.0027444231889663033\n",
      "iteration 61, relative error = 0.002709025102589725\n",
      "iteration 62, relative error = 0.0026840648451884673\n",
      "iteration 63, relative error = 0.0026791444693745415\n",
      "iteration 64, relative error = 0.0027048602726148564\n",
      "iteration 65, relative error = 0.0027704805949053057\n",
      "iteration 66, relative error = 0.0028803046190380753\n",
      "iteration 67, relative error = 0.0030294251171042656\n",
      "iteration 68, relative error = 0.0032002930591923657\n",
      "iteration 69, relative error = 0.0033624924990945664\n",
      "iteration 70, relative error = 0.003478674629954818\n",
      "iteration 71, relative error = 0.0035173689489060673\n",
      "iteration 72, relative error = 0.0034670327025457756\n",
      "iteration 73, relative error = 0.003340003349663473\n",
      "iteration 74, relative error = 0.003159876605593836\n",
      "iteration 75, relative error = 0.002941850437582777\n",
      "iteration 76, relative error = 0.0026854495879988414\n",
      "iteration 77, relative error = 0.002387410458940225\n",
      "iteration 78, relative error = 0.002060045975933341\n",
      "iteration 79, relative error = 0.0017343714405385526\n",
      "iteration 80, relative error = 0.0014458337302454165\n",
      "iteration 81, relative error = 0.0012187259606536744\n",
      "iteration 82, relative error = 0.0010617357792373923\n",
      "iteration 83, relative error = 0.0009727994560710207\n",
      "iteration 84, relative error = 0.0009452063646420981\n",
      "iteration 85, relative error = 0.0009695856792008756\n",
      "iteration 86, relative error = 0.001030873379944337\n",
      "iteration 87, relative error = 0.0011035750645068969\n",
      "iteration 88, relative error = 0.0011532051972585964\n",
      "iteration 89, relative error = 0.0011508804653560427\n",
      "iteration 90, relative error = 0.0010934258992094908\n",
      "iteration 91, relative error = 0.0010072833010618685\n",
      "iteration 92, relative error = 0.0009290033033919721\n",
      "iteration 93, relative error = 0.0008818478825620919\n",
      "iteration 94, relative error = 0.000867508733990743\n",
      "iteration 95, relative error = 0.0008736030905486449\n",
      "iteration 96, relative error = 0.0008881574920631752\n",
      "iteration 97, relative error = 0.0009099069390210815\n",
      "iteration 98, relative error = 0.0009451845336998242\n",
      "iteration 99, relative error = 0.000995467822666245\n",
      "iteration 100, relative error = 0.001052799016773603\n",
      "iteration 101, relative error = 0.0011104058444860638\n",
      "iteration 102, relative error = 0.0011693554826328395\n",
      "iteration 103, relative error = 0.0012241324782251593\n",
      "iteration 104, relative error = 0.001251611127468181\n",
      "iteration 105, relative error = 0.0012357536988707994\n",
      "iteration 106, relative error = 0.0011996694809165898\n",
      "iteration 107, relative error = 0.0011904450021389392\n",
      "iteration 108, relative error = 0.0012310906583057365\n",
      "iteration 109, relative error = 0.0012975783990405987\n",
      "iteration 110, relative error = 0.001338674648586337\n",
      "iteration 111, relative error = 0.0013114814681561208\n",
      "iteration 112, relative error = 0.001208607374613424\n",
      "iteration 113, relative error = 0.0010671265157030261\n",
      "iteration 114, relative error = 0.000941677067185945\n",
      "iteration 115, relative error = 0.0008572233957741926\n",
      "iteration 116, relative error = 0.0007937529636400523\n",
      "iteration 117, relative error = 0.0007204824092691758\n",
      "iteration 118, relative error = 0.0006346876104665902\n",
      "iteration 119, relative error = 0.0005566106332289511\n",
      "iteration 120, relative error = 0.0005019246876701976\n",
      "iteration 121, relative error = 0.0004721396592299652\n",
      "iteration 122, relative error = 0.00046103620528396594\n",
      "iteration 123, relative error = 0.00046118983055449853\n",
      "iteration 124, relative error = 0.0004660421457317161\n",
      "iteration 125, relative error = 0.0004695960178456473\n",
      "iteration 126, relative error = 0.00046600274579132234\n",
      "iteration 127, relative error = 0.00045054159327894886\n",
      "iteration 128, relative error = 0.00042301229032195556\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 129, relative error = 0.00039296256451553025\n",
      "iteration 130, relative error = 0.00038215719734091015\n",
      "iteration 131, relative error = 0.00041637033783824994\n",
      "iteration 132, relative error = 0.0005049672160817654\n",
      "iteration 133, relative error = 0.0006240852837927618\n",
      "iteration 134, relative error = 0.0007255581363166386\n",
      "iteration 135, relative error = 0.0007696057319520187\n",
      "iteration 136, relative error = 0.0007501978279182246\n",
      "iteration 137, relative error = 0.0006905462843151778\n",
      "iteration 138, relative error = 0.0006213736342967597\n",
      "iteration 139, relative error = 0.0005666114227979452\n",
      "iteration 140, relative error = 0.0005417078538971542\n",
      "iteration 141, relative error = 0.0005485864608989919\n",
      "iteration 142, relative error = 0.0005570480962831091\n",
      "iteration 143, relative error = 0.0005200379018522475\n",
      "iteration 144, relative error = 0.0004385660277604164\n",
      "iteration 145, relative error = 0.00035434411231657594\n",
      "iteration 146, relative error = 0.0002917831698117151\n",
      "iteration 147, relative error = 0.0002504779381858676\n",
      "iteration 148, relative error = 0.00022312873054181368\n",
      "iteration 149, relative error = 0.0002045829416967061\n",
      "iteration 150, relative error = 0.0001932214655564748\n",
      "iteration 151, relative error = 0.00019110573714951155\n",
      "iteration 152, relative error = 0.00020443634818883975\n",
      "iteration 153, relative error = 0.0002390045444111087\n",
      "iteration 154, relative error = 0.0002802524853416073\n",
      "iteration 155, relative error = 0.0002868556693634991\n",
      "iteration 156, relative error = 0.00024666121115020443\n",
      "iteration 157, relative error = 0.000194076076296262\n",
      "iteration 158, relative error = 0.00015554607880510004\n",
      "iteration 159, relative error = 0.0001331450136542503\n",
      "iteration 160, relative error = 0.00012068476531674417\n",
      "iteration 161, relative error = 0.00011320296133181753\n",
      "iteration 162, relative error = 0.00010809304623917045\n",
      "iteration 163, relative error = 0.000104160417223907\n",
      "iteration 164, relative error = 0.00010086886202095058\n",
      "iteration 165, relative error = 9.7967551853739e-05\n",
      "iteration 166, relative error = 9.533040972138687e-05\n",
      "iteration 167, relative error = 9.288878023036328e-05\n",
      "iteration 168, relative error = 9.060238277766034e-05\n",
      "iteration 169, relative error = 8.844614402322611e-05\n",
      "iteration 170, relative error = 8.640417211883235e-05\n",
      "iteration 171, relative error = 8.446788024075414e-05\n",
      "iteration 172, relative error = 8.263876350569373e-05\n",
      "iteration 173, relative error = 8.09416125599662e-05\n",
      "iteration 174, relative error = 7.946818787482122e-05\n",
      "iteration 175, relative error = 7.851427469808813e-05\n",
      "iteration 176, relative error = 7.89937447548347e-05\n",
      "iteration 177, relative error = 8.353957843257688e-05\n",
      "iteration 178, relative error = 9.816755851748077e-05\n",
      "iteration 179, relative error = 0.00012801119116526572\n",
      "iteration 180, relative error = 0.00015591264647086566\n",
      "iteration 181, relative error = 0.00015063576322550445\n",
      "iteration 182, relative error = 0.00012100513297230503\n",
      "iteration 183, relative error = 9.451076134945207e-05\n",
      "iteration 184, relative error = 7.939654163303265e-05\n",
      "iteration 185, relative error = 7.205194835377479e-05\n",
      "iteration 186, relative error = 6.842371156129698e-05\n",
      "iteration 187, relative error = 6.637200326486603e-05\n",
      "iteration 188, relative error = 6.498967362286021e-05\n",
      "iteration 189, relative error = 6.391592595802372e-05\n",
      "iteration 190, relative error = 6.300533142858074e-05\n",
      "iteration 191, relative error = 6.219606192689421e-05\n",
      "iteration 192, relative error = 6.146000087345639e-05\n",
      "iteration 193, relative error = 6.07834975307913e-05\n",
      "iteration 194, relative error = 6.0159515986976376e-05\n",
      "iteration 195, relative error = 5.9584202453243776e-05\n",
      "iteration 196, relative error = 5.905528345040446e-05\n",
      "iteration 197, relative error = 5.8571277740493004e-05\n",
      "iteration 198, relative error = 5.813109178032093e-05\n",
      "iteration 199, relative error = 5.7733803596389685e-05\n",
      "iteration 200, relative error = 5.7378541664349955e-05\n",
      "\u001b[1mRun number 4 of the algorithm\u001b[0m\n",
      "initial error: 540123886585.1584\n",
      "iteration 1, relative error = 0\n",
      "iteration 2, relative error = 0\n",
      "iteration 3, relative error = 0.9221874544289577\n",
      "iteration 4, relative error = 0.41356035688091686\n",
      "iteration 5, relative error = 0.1246969405783759\n",
      "iteration 6, relative error = 0.2782906795797402\n",
      "iteration 7, relative error = 3.4641438123496955\n",
      "iteration 8, relative error = 0.2965193724932496\n",
      "iteration 9, relative error = 0.6783184154875136\n",
      "iteration 10, relative error = 0.2296325899912541\n",
      "iteration 11, relative error = 0.017919863420639098\n",
      "iteration 12, relative error = 0.00039764292509720606\n",
      "iteration 13, relative error = 0.004460439087119273\n",
      "iteration 14, relative error = 0.004088034321204692\n",
      "iteration 15, relative error = 0.0018757878463201213\n",
      "iteration 16, relative error = 0.003310432926519305\n",
      "iteration 17, relative error = 0.008811697832773354\n",
      "iteration 18, relative error = 0.015279009863733376\n",
      "iteration 19, relative error = 0.016823777274487795\n",
      "iteration 20, relative error = 0.01032478486726528\n",
      "iteration 21, relative error = 0.002943162170499558\n",
      "iteration 22, relative error = 0.0011679019182676523\n",
      "iteration 23, relative error = 0.0032913039771795493\n",
      "iteration 24, relative error = 0.006004580722448779\n",
      "iteration 25, relative error = 0.007746422014051811\n",
      "iteration 26, relative error = 0.00816835798972651\n",
      "iteration 27, relative error = 0.0076703077718312035\n",
      "iteration 28, relative error = 0.006800135807732335\n",
      "iteration 29, relative error = 0.0059238217867321405\n",
      "iteration 30, relative error = 0.005293273968543362\n",
      "iteration 31, relative error = 0.004960850616528043\n",
      "iteration 32, relative error = 0.004784345655809552\n",
      "iteration 33, relative error = 0.0046996513583790105\n",
      "iteration 34, relative error = 0.0046965199287626255\n",
      "iteration 35, relative error = 0.0047285299266070354\n",
      "iteration 36, relative error = 0.00474919126116318\n",
      "iteration 37, relative error = 0.004739794964310361\n",
      "iteration 38, relative error = 0.004707523590801402\n",
      "iteration 39, relative error = 0.004675848711633602\n",
      "iteration 40, relative error = 0.004674551069427629\n",
      "iteration 41, relative error = 0.00472917747895707\n",
      "iteration 42, relative error = 0.004850755083284602\n",
      "iteration 43, relative error = 0.005030213341443466\n",
      "iteration 44, relative error = 0.0052443583849326346\n",
      "iteration 45, relative error = 0.005474831970743656\n",
      "iteration 46, relative error = 0.005722801110377935\n",
      "iteration 47, relative error = 0.0059867214443506175\n",
      "iteration 48, relative error = 0.006201142477211721\n",
      "iteration 49, relative error = 0.006222918174075655\n",
      "iteration 50, relative error = 0.005952743288206265\n",
      "iteration 51, relative error = 0.005456089616230437\n",
      "iteration 52, relative error = 0.004900583866961557\n",
      "iteration 53, relative error = 0.004419035822240279\n",
      "iteration 54, relative error = 0.0040411353156549045\n",
      "iteration 55, relative error = 0.003703145166979469\n",
      "iteration 56, relative error = 0.003325630677246434\n",
      "iteration 57, relative error = 0.0029009249960750727\n",
      "iteration 58, relative error = 0.0024948774846034433\n",
      "iteration 59, relative error = 0.002173888209655512\n",
      "iteration 60, relative error = 0.001960369307604359\n",
      "iteration 61, relative error = 0.001845519314533829\n",
      "iteration 62, relative error = 0.0018150425913446733\n",
      "iteration 63, relative error = 0.001858485832323728\n",
      "iteration 64, relative error = 0.0019609009336062793\n",
      "iteration 65, relative error = 0.0020888338694495853\n",
      "iteration 66, relative error = 0.0021947182110002776\n",
      "iteration 67, relative error = 0.0022500282187294576\n",
      "iteration 68, relative error = 0.00226564334154133\n",
      "iteration 69, relative error = 0.002262128422895898\n",
      "iteration 70, relative error = 0.0022289706257668756\n",
      "iteration 71, relative error = 0.0021314587052495807\n",
      "iteration 72, relative error = 0.0019576189643032995\n",
      "iteration 73, relative error = 0.0017426063520703194\n",
      "iteration 74, relative error = 0.001543404436134792\n",
      "iteration 75, relative error = 0.001399357749865789\n",
      "iteration 76, relative error = 0.001319622360766841\n",
      "iteration 77, relative error = 0.001296190758138202\n",
      "iteration 78, relative error = 0.0013189249968631688\n",
      "iteration 79, relative error = 0.0013816952715991183\n",
      "iteration 80, relative error = 0.0014810098860936626\n",
      "iteration 81, relative error = 0.0016095310470992808\n",
      "iteration 82, relative error = 0.0017459530018672045\n",
      "iteration 83, relative error = 0.0018477033791376783\n",
      "iteration 84, relative error = 0.0018649726060139358\n",
      "iteration 85, relative error = 0.001788994615017364\n",
      "iteration 86, relative error = 0.0016768133899366501\n",
      "iteration 87, relative error = 0.0015897641265383634\n",
      "iteration 88, relative error = 0.001536851752952999\n",
      "iteration 89, relative error = 0.0014841907780103695\n",
      "iteration 90, relative error = 0.0013908435903564975\n",
      "iteration 91, relative error = 0.0012567841222093999\n",
      "iteration 92, relative error = 0.0011193109942400346\n",
      "iteration 93, relative error = 0.0010051730328342686\n",
      "iteration 94, relative error = 0.000918337723321518\n",
      "iteration 95, relative error = 0.0008545931233375267\n",
      "iteration 96, relative error = 0.0008109583134535782\n",
      "iteration 97, relative error = 0.0007845636350730021\n",
      "iteration 98, relative error = 0.0007647918926904213\n",
      "iteration 99, relative error = 0.0007341855967452058\n",
      "iteration 100, relative error = 0.0006886968414838688\n",
      "iteration 101, relative error = 0.0006427150409712759\n",
      "iteration 102, relative error = 0.0006130167281499186\n",
      "iteration 103, relative error = 0.0006316772262389914\n",
      "iteration 104, relative error = 0.0008859971141670274\n",
      "iteration 105, relative error = 0.0020176276337554376\n",
      "iteration 106, relative error = 0.0019380963920301896\n",
      "iteration 107, relative error = 0.0011769792186588372\n",
      "iteration 108, relative error = 0.004367933108611079\n",
      "iteration 109, relative error = 0.001641037622584893\n",
      "iteration 110, relative error = 0.0008280777030797916\n",
      "iteration 111, relative error = 0.0006900233768133768\n",
      "iteration 112, relative error = 0.0007072133861965331\n",
      "iteration 113, relative error = 0.000793473617531336\n",
      "iteration 114, relative error = 0.0009507202065851511\n",
      "iteration 115, relative error = 0.0012003824330326237\n",
      "iteration 116, relative error = 0.0015506339608829206\n",
      "iteration 117, relative error = 0.0019450525350862792\n",
      "iteration 118, relative error = 0.0022346822211020915\n",
      "iteration 119, relative error = 0.0022922957693657145\n",
      "iteration 120, relative error = 0.0021725348024579532\n",
      "iteration 121, relative error = 0.002037038401857504\n",
      "iteration 122, relative error = 0.0019737302528661173\n",
      "iteration 123, relative error = 0.001963717243587817\n",
      "iteration 124, relative error = 0.0019477618928459663\n",
      "iteration 125, relative error = 0.001879890889543082\n",
      "iteration 126, relative error = 0.0017417066057806407\n",
      "iteration 127, relative error = 0.0015463984826808242\n",
      "iteration 128, relative error = 0.001334269313843152\n",
      "iteration 129, relative error = 0.0011455898321720605\n",
      "iteration 130, relative error = 0.0009982438041253024\n",
      "iteration 131, relative error = 0.000890447355985528\n",
      "iteration 132, relative error = 0.0008140552952003913\n",
      "iteration 133, relative error = 0.0007635415105097003\n",
      "iteration 134, relative error = 0.0007386561271459942\n",
      "iteration 135, relative error = 0.0007436992430422347\n",
      "iteration 136, relative error = 0.0007856050400453242\n",
      "iteration 137, relative error = 0.0008708297671294976\n",
      "iteration 138, relative error = 0.0009994742312495677\n",
      "iteration 139, relative error = 0.0011570614382906748\n",
      "iteration 140, relative error = 0.0013119612084916711\n",
      "iteration 141, relative error = 0.001429322940826532\n",
      "iteration 142, relative error = 0.001491461045145958\n",
      "iteration 143, relative error = 0.0014959343695524466\n",
      "iteration 144, relative error = 0.0014380043312372018\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 145, relative error = 0.0013155974958512803\n",
      "iteration 146, relative error = 0.0011486912058675286\n",
      "iteration 147, relative error = 0.0009736511535956875\n",
      "iteration 148, relative error = 0.0008193325610292259\n",
      "iteration 149, relative error = 0.0006963598162854567\n",
      "iteration 150, relative error = 0.0006026677334301383\n",
      "iteration 151, relative error = 0.0005318377929299166\n",
      "iteration 152, relative error = 0.0004776220774908782\n",
      "iteration 153, relative error = 0.0004352347073296116\n",
      "iteration 154, relative error = 0.00040129142186884706\n",
      "iteration 155, relative error = 0.00037344392640340586\n",
      "iteration 156, relative error = 0.0003500554253238565\n",
      "iteration 157, relative error = 0.00032997465681913854\n",
      "iteration 158, relative error = 0.0003123883151059688\n",
      "iteration 159, relative error = 0.0002967243179883641\n",
      "iteration 160, relative error = 0.00028258606416549007\n",
      "iteration 161, relative error = 0.00026970550862694283\n",
      "iteration 162, relative error = 0.0002579080849010023\n",
      "iteration 163, relative error = 0.00024708564421576843\n",
      "iteration 164, relative error = 0.00023717536890281897\n",
      "iteration 165, relative error = 0.00022814356326415766\n",
      "iteration 166, relative error = 0.00021997366459234574\n",
      "iteration 167, relative error = 0.00021265797356088804\n",
      "iteration 168, relative error = 0.00020619262787639116\n",
      "iteration 169, relative error = 0.00020057533206721366\n",
      "iteration 170, relative error = 0.00019580536011974372\n",
      "iteration 171, relative error = 0.00019188537957853127\n",
      "iteration 172, relative error = 0.00018882468943677794\n",
      "iteration 173, relative error = 0.00018664347987825536\n",
      "iteration 174, relative error = 0.00018537764682939113\n",
      "iteration 175, relative error = 0.00018508343957567605\n",
      "iteration 176, relative error = 0.00018584067024161438\n",
      "iteration 177, relative error = 0.00018775225080267115\n",
      "iteration 178, relative error = 0.00019093641241781708\n",
      "iteration 179, relative error = 0.00019550638027246293\n",
      "iteration 180, relative error = 0.00020153153821863605\n",
      "iteration 181, relative error = 0.00020897647774029439\n",
      "iteration 182, relative error = 0.00021762319300927367\n",
      "iteration 183, relative error = 0.00022699934834348234\n",
      "iteration 184, relative error = 0.00023635729234144318\n",
      "iteration 185, relative error = 0.00024475617911520075\n",
      "iteration 186, relative error = 0.00025126711466923716\n",
      "iteration 187, relative error = 0.00025524421642220097\n",
      "iteration 188, relative error = 0.0002565321296983987\n",
      "iteration 189, relative error = 0.0002554905736413036\n",
      "iteration 190, relative error = 0.00025282439173396424\n",
      "iteration 191, relative error = 0.0002493252578289122\n",
      "iteration 192, relative error = 0.00024565879827071293\n",
      "iteration 193, relative error = 0.0002422661944783722\n",
      "iteration 194, relative error = 0.00023936849319492975\n",
      "iteration 195, relative error = 0.00023702369714102137\n",
      "iteration 196, relative error = 0.00023519222673763947\n",
      "iteration 197, relative error = 0.00023378825194801664\n",
      "iteration 198, relative error = 0.00023271219117104312\n",
      "iteration 199, relative error = 0.0002318680610091253\n",
      "iteration 200, relative error = 0.00023117111284979476\n",
      "\u001b[1mRun number 5 of the algorithm\u001b[0m\n",
      "initial error: 529279956335.0408\n",
      "iteration 1, relative error = 0\n",
      "iteration 2, relative error = 0\n",
      "iteration 3, relative error = 0.9214296304030162\n",
      "iteration 4, relative error = 0.41774710154471695\n",
      "iteration 5, relative error = 0.13329578231090428\n",
      "iteration 6, relative error = 0.38100317087028557\n",
      "iteration 7, relative error = 3.5083887599385304\n",
      "iteration 8, relative error = 0.40559039664936497\n",
      "iteration 9, relative error = 0.6589577132427946\n",
      "iteration 10, relative error = 0.20718063294140546\n",
      "iteration 11, relative error = 0.011579837676694226\n",
      "iteration 12, relative error = 0.0034059479447577272\n",
      "iteration 13, relative error = 0.0015784571481547022\n",
      "iteration 14, relative error = 0.003915005473304453\n",
      "iteration 15, relative error = 0.004733815339268474\n",
      "iteration 16, relative error = 0.005017990174163953\n",
      "iteration 17, relative error = 0.004823781215015591\n",
      "iteration 18, relative error = 0.0050008989744835175\n",
      "iteration 19, relative error = 0.005887469528286532\n",
      "iteration 20, relative error = 0.007412564169136676\n",
      "iteration 21, relative error = 0.009348782315180356\n",
      "iteration 22, relative error = 0.01119359020673524\n",
      "iteration 23, relative error = 0.012122547338271146\n",
      "iteration 24, relative error = 0.011286343883600374\n",
      "iteration 25, relative error = 0.008689358278527036\n",
      "iteration 26, relative error = 0.005768485779053587\n",
      "iteration 27, relative error = 0.00420001445402995\n",
      "iteration 28, relative error = 0.004058485146796552\n",
      "iteration 29, relative error = 0.004368152447930612\n",
      "iteration 30, relative error = 0.004613003223833517\n",
      "iteration 31, relative error = 0.004789634207327237\n",
      "iteration 32, relative error = 0.004929168911745329\n",
      "iteration 33, relative error = 0.004942404553028793\n",
      "iteration 34, relative error = 0.0047385482060378235\n",
      "iteration 35, relative error = 0.004427294279837468\n",
      "iteration 36, relative error = 0.004209725188944742\n",
      "iteration 37, relative error = 0.004104067971085355\n",
      "iteration 38, relative error = 0.0040474813025023005\n",
      "iteration 39, relative error = 0.003928586094367917\n",
      "iteration 40, relative error = 0.003656164161532791\n",
      "iteration 41, relative error = 0.003268033749098615\n",
      "iteration 42, relative error = 0.0028786549178335635\n",
      "iteration 43, relative error = 0.0025797259287758246\n",
      "iteration 44, relative error = 0.0023973107713344314\n",
      "iteration 45, relative error = 0.0023064731136073907\n",
      "iteration 46, relative error = 0.0022657323364005284\n",
      "iteration 47, relative error = 0.0022416325140320213\n",
      "iteration 48, relative error = 0.0022155174047296744\n",
      "iteration 49, relative error = 0.002179323102010181\n",
      "iteration 50, relative error = 0.002130004717290128\n",
      "iteration 51, relative error = 0.002067317829618694\n",
      "iteration 52, relative error = 0.0019938213769190283\n",
      "iteration 53, relative error = 0.00191425299058265\n",
      "iteration 54, relative error = 0.0018336063611104503\n",
      "iteration 55, relative error = 0.001755306458150378\n",
      "iteration 56, relative error = 0.0016806643294721513\n",
      "iteration 57, relative error = 0.001609498844913592\n",
      "iteration 58, relative error = 0.0015411144584205677\n",
      "iteration 59, relative error = 0.001474996231358735\n",
      "iteration 60, relative error = 0.001411051108286228\n",
      "iteration 61, relative error = 0.0013495316135577497\n",
      "iteration 62, relative error = 0.0012908449639090986\n",
      "iteration 63, relative error = 0.0012353869797820465\n",
      "iteration 64, relative error = 0.0011834559942007655\n",
      "iteration 65, relative error = 0.0011352463146165083\n",
      "iteration 66, relative error = 0.0010908987745117365\n",
      "iteration 67, relative error = 0.0010505871162600471\n",
      "iteration 68, relative error = 0.001014633150440548\n",
      "iteration 69, relative error = 0.0009836657046677928\n",
      "iteration 70, relative error = 0.0009588692707504214\n",
      "iteration 71, relative error = 0.0009424161467575395\n",
      "iteration 72, relative error = 0.000938260116465726\n",
      "iteration 73, relative error = 0.0009536314744805343\n",
      "iteration 74, relative error = 0.0010018963701172662\n",
      "iteration 75, relative error = 0.0011080784534204242\n",
      "iteration 76, relative error = 0.0013194452162996664\n",
      "iteration 77, relative error = 0.0017246512928533778\n",
      "iteration 78, relative error = 0.002481292062296359\n",
      "iteration 79, relative error = 0.003820932795550957\n",
      "iteration 80, relative error = 0.00589109486593766\n",
      "iteration 81, relative error = 0.00824729007410205\n",
      "iteration 82, relative error = 0.009768916189265809\n",
      "iteration 83, relative error = 0.010369164526768513\n",
      "iteration 84, relative error = 0.011078730900874953\n",
      "iteration 85, relative error = 0.011151918028506196\n",
      "iteration 86, relative error = 0.008863858954033835\n",
      "iteration 87, relative error = 0.0053023377232078745\n",
      "iteration 88, relative error = 0.002685816739457702\n",
      "iteration 89, relative error = 0.0013992459641064333\n",
      "iteration 90, relative error = 0.0009306832716614823\n",
      "iteration 91, relative error = 0.0009330617435173606\n",
      "iteration 92, relative error = 0.0012992043916582392\n",
      "iteration 93, relative error = 0.001821733966330126\n",
      "iteration 94, relative error = 0.0021243305999207804\n",
      "iteration 95, relative error = 0.0021165731714059793\n",
      "iteration 96, relative error = 0.0018452252017209477\n",
      "iteration 97, relative error = 0.0013903484690407522\n",
      "iteration 98, relative error = 0.0009305020661003669\n",
      "iteration 99, relative error = 0.0007268172534098432\n",
      "iteration 100, relative error = 0.0008829390772307143\n",
      "iteration 101, relative error = 0.001131830616517565\n",
      "iteration 102, relative error = 0.0011241520070006525\n",
      "iteration 103, relative error = 0.0009353024981096879\n",
      "iteration 104, relative error = 0.0007911877396470597\n",
      "iteration 105, relative error = 0.000764958027368141\n",
      "iteration 106, relative error = 0.0008725671065245271\n",
      "iteration 107, relative error = 0.0011548724891702253\n",
      "iteration 108, relative error = 0.0015392435787832275\n",
      "iteration 109, relative error = 0.001720352106645656\n",
      "iteration 110, relative error = 0.0015627513730887613\n",
      "iteration 111, relative error = 0.0012428388547505245\n",
      "iteration 112, relative error = 0.0009580799840168493\n",
      "iteration 113, relative error = 0.0007814620543208584\n",
      "iteration 114, relative error = 0.0006899236955078473\n",
      "iteration 115, relative error = 0.0006447107973991316\n",
      "iteration 116, relative error = 0.0006251025362252323\n",
      "iteration 117, relative error = 0.0006315459669190669\n",
      "iteration 118, relative error = 0.0006844397038733563\n",
      "iteration 119, relative error = 0.0007977677605046801\n",
      "iteration 120, relative error = 0.0009043942165444143\n",
      "iteration 121, relative error = 0.0008974112343091238\n",
      "iteration 122, relative error = 0.0007907741255204089\n",
      "iteration 123, relative error = 0.0006848885078622459\n",
      "iteration 124, relative error = 0.0006431244012978632\n",
      "iteration 125, relative error = 0.0006808743374472757\n",
      "iteration 126, relative error = 0.0007648626515129673\n",
      "iteration 127, relative error = 0.0007932912575059622\n",
      "iteration 128, relative error = 0.0007333894639425216\n",
      "iteration 129, relative error = 0.0006562645583462157\n",
      "iteration 130, relative error = 0.000603973439228553\n",
      "iteration 131, relative error = 0.0005728036061981596\n",
      "iteration 132, relative error = 0.0005507588752315512\n",
      "iteration 133, relative error = 0.0005310933579567584\n",
      "iteration 134, relative error = 0.0005116127013204272\n",
      "iteration 135, relative error = 0.000492259780072619\n",
      "iteration 136, relative error = 0.0004736628509721727\n",
      "iteration 137, relative error = 0.00045646618218921184\n",
      "iteration 138, relative error = 0.0004410746399205348\n",
      "iteration 139, relative error = 0.0004275989992539317\n",
      "iteration 140, relative error = 0.00041589127344101933\n",
      "iteration 141, relative error = 0.0004056160917798024\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 142, relative error = 0.00039634456376948354\n",
      "iteration 143, relative error = 0.0003877082154560391\n",
      "iteration 144, relative error = 0.0003797786354509501\n",
      "iteration 145, relative error = 0.0003742267420335455\n",
      "iteration 146, relative error = 0.00037747450444172774\n",
      "iteration 147, relative error = 0.00040315200290184564\n",
      "iteration 148, relative error = 0.00045093455269629314\n",
      "iteration 149, relative error = 0.0004838715801448021\n",
      "iteration 150, relative error = 0.0004751975544113953\n",
      "iteration 151, relative error = 0.0004213806128026135\n",
      "iteration 152, relative error = 0.000356909596826295\n",
      "iteration 153, relative error = 0.00031384874834366826\n",
      "iteration 154, relative error = 0.00029377499078696827\n",
      "iteration 155, relative error = 0.0002883788521164993\n",
      "iteration 156, relative error = 0.00029176923692438304\n",
      "iteration 157, relative error = 0.0003009236764964172\n",
      "iteration 158, relative error = 0.00031410365532684946\n",
      "iteration 159, relative error = 0.00032994750639608753\n",
      "iteration 160, relative error = 0.0003471283845992251\n",
      "iteration 161, relative error = 0.00036424631597341094\n",
      "iteration 162, relative error = 0.0003797971412590175\n",
      "iteration 163, relative error = 0.000392180130528921\n",
      "iteration 164, relative error = 0.00039976360464460167\n",
      "iteration 165, relative error = 0.0004010447177431209\n",
      "iteration 166, relative error = 0.000394923233532593\n",
      "iteration 167, relative error = 0.000381086919996883\n",
      "iteration 168, relative error = 0.0003605985117824588\n",
      "iteration 169, relative error = 0.0003373408083071467\n",
      "iteration 170, relative error = 0.0003216850495330827\n",
      "iteration 171, relative error = 0.00032586369166467364\n",
      "iteration 172, relative error = 0.00032383436483286195\n",
      "iteration 173, relative error = 0.00028218717662965945\n",
      "iteration 174, relative error = 0.00022933824025572595\n",
      "iteration 175, relative error = 0.00019342634866278166\n",
      "iteration 176, relative error = 0.0001790757800734518\n",
      "iteration 177, relative error = 0.0001820310670159148\n",
      "iteration 178, relative error = 0.00018249409721763773\n",
      "iteration 179, relative error = 0.00016803428498330706\n",
      "iteration 180, relative error = 0.00015116586833181894\n",
      "iteration 181, relative error = 0.00014039070132764814\n",
      "iteration 182, relative error = 0.00013461470081592273\n",
      "iteration 183, relative error = 0.00013113113190092233\n",
      "iteration 184, relative error = 0.00012838222269263662\n",
      "iteration 185, relative error = 0.00012583832973162972\n",
      "iteration 186, relative error = 0.00012366905194682987\n",
      "iteration 187, relative error = 0.00012265071503841772\n",
      "iteration 188, relative error = 0.00012419511832515402\n",
      "iteration 189, relative error = 0.00013033009472331958\n",
      "iteration 190, relative error = 0.00014341080795578893\n",
      "iteration 191, relative error = 0.00016527134746117318\n",
      "iteration 192, relative error = 0.00019565573678379308\n",
      "iteration 193, relative error = 0.00023050261270956513\n",
      "iteration 194, relative error = 0.0002619685211242486\n",
      "iteration 195, relative error = 0.00028216984697597343\n",
      "iteration 196, relative error = 0.00028916128654546077\n",
      "iteration 197, relative error = 0.00028966232320143414\n",
      "iteration 198, relative error = 0.00029555655774170426\n",
      "iteration 199, relative error = 0.00031785205181765946\n",
      "iteration 200, relative error = 0.00036292837559986387\n",
      "\u001b[1mFinal run without masking, stop at iteration: 12\u001b[0m\n",
      "iteration 1, relative error = 0\n",
      "iteration 2, relative error = 0\n",
      "iteration 3, relative error = 0.9230852667716237\n",
      "iteration 4, relative error = 0.413915524636338\n",
      "iteration 5, relative error = 0.11975634310233506\n",
      "iteration 6, relative error = 0.16766522447197743\n",
      "iteration 7, relative error = 3.452715128712263\n",
      "iteration 8, relative error = 0.1356345870280921\n",
      "iteration 9, relative error = 0.734613919972452\n",
      "iteration 10, relative error = 0.3552867836222821\n",
      "iteration 11, relative error = 0.03750651460501734\n",
      "iteration 12, relative error = 0.0017207231052862556\n"
     ]
    }
   ],
   "source": [
    "# NMTF-link.py asks for the data and setting file directory and for the filename of the setting file.\n",
    "# Then, the NMTF-based algorithm computes the new link predictions in the specified network according to the parameters \n",
    "# selected in the setting file.\n",
    "\n",
    "# Here, this example uses the graph_topology.tsv file as the setting file in the folder case_study_1:\n",
    "%run NMTF-link.py\n",
    "\n",
    "# Remember that to run the algorithm from terminal the correct line is: python3 NMTF-link.py "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Used setting file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#parameters\t\n",
      "\n",
      "#integration.strategy\tintersection\n",
      "\n",
      "#initialization\trandom\n",
      "\n",
      "#metric\tAPS\n",
      "\n",
      "#number.of.iterations\t200\n",
      "\n",
      "#type.of.masking\tfully_random\n",
      "\n",
      "#stop.criterion\trelative_error\n",
      "\n",
      "#score.threshold\t0.5\n",
      "\n",
      "#nodes_left\tnodes_right\tfilename\tmain\n",
      "\n",
      "users\tgenres\tUsersToGenres.txt\t0\n",
      "\n",
      "users\tmovies\tUsersToMovies.txt\t1\n",
      "\n",
      "movies\tactors\tMoviesToActors.txt\t0\n",
      "\n",
      "movies\tgenres\tMoviesToGenres.txt\t0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"./case_study_1/graph_topology.tsv\") as f:\n",
    "    for line in f:\n",
    "        print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This means that:\n",
    "We use the random initialization strategy for the factor matrices, APS (Average Precision Score) as evaluation metric, 200 maximum iterations for each run of the algorithm, random masking strategy for the evaluation process and stop criterion based on the relative error of the loss function. The integration strategy of shared datasets is by intersection, i.e., only objects shared by all association matrices are considered. The new link predictions have an NMTF score above 0.5 (the score threshold)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network:\n",
    "The example contains 4 nodes' categories (**users, genres, actors and movies**). Users to genres links are reported in [UsersToGenres.txt](https://github.com/DEIB-GECO/NMTF-link/blob/master/case_study_1/UsersToGenres.txt), where each link represents the preference of movie genres for a specific user. Users to movies links are the list of watched movies for each user, reported in [UsersToMovies.txt](https://github.com/DEIB-GECO/NMTF-link/blob/master/case_study_1/UsersToMovies.txt). The [MoviesToActors.txt](https://github.com/DEIB-GECO/NMTF-link/blob/master/case_study_1/MoviesToActors.txt) file contains information on which actors worked on a specific movie, i.e., there is a link when an actor worked on that movie. Movies to genres links ([MoviesToGenres.txt](https://github.com/DEIB-GECO/NMTF-link/blob/master/case_study_1/MoviesToGenres.txt)) classify the genre of each movie in the network.\n",
    "The output file ([myOutFile.txt](https://github.com/DEIB-GECO/NMTF-link/blob/master/results/case_study_1/myOutFile_random_relative_error.txt)) contains new movie suggestions for the users, as selected by the element equal to 1 in the column **main** of the setting file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation plot\n",
    "The following figure is an example output of the NMTF-link tool using the toy example datasets in the case_study_1 folder and the graph_topology.tsv setting file displayed above:"
   ]
  },
  {
   "attachments": {
    "aps_random_relative_error.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![aps_random_relative_error.png](attachment:aps_random_relative_error.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "Each curve represents one run of the algorithm with a different random initialization of the factor matrices and a different mask. The y-axis reports the APS scores (i.e., the selected evaluation metric) and the x-axis represents the iterations performed for each run of the algorithm according to the update rules computed on the factor matrices. The dashed line indicates the selected stop iteration that is used for the final run of the algorithm, i.e., the run without masking where the new link predictions are computed. In this case this stop iteration is chosen by computing the average iteration across the 5 stop iterations detected, one for each run of the algorithm, i.e., the first iteration of each run with relative error ($RE$) $< 0.001$. The relative error is computed as follows: \n",
    "\n",
    "$RE = \\frac {(loss_{i+1} - loss_{i})}{loss_{i}}$\n",
    "\n",
    "where loss is the loss function of the algorithm, and i is the i-th iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New Link Predictions file\n",
    "The following table represents the output file, containing all new prendicted links with NMTF score above the score threshold specified in the [graph_topology.tsv](https://github.com/DEIB-GECO/NMTF-link/tree/master/case_study_1/graph_topology.tsv) setting file (**#score.threshold** = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anacleto  Ma  0.5733413382743112\n",
      "\n",
      "Anacleto  The Lighthouse  0.6337179475861378\n",
      "\n",
      "Serena  The Lighthouse  0.5200780445464313\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"./results/case_study_1/myOutFile_random_relative_error.txt\") as f:\n",
    "    for line in f:\n",
    "        print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "Each line contains the first object (**user**), the second object (**movie**) and the NMTF score of their link.\n",
    "Thus, the file [myOutFile.txt](https://github.com/DEIB-GECO/NMTF-link/tree/master/results/case_study_1/myOutFile_random_relative_error.txt) contains new movie suggestions (Ma and The Lighthouse) for two specific users (Anacleto and Serena)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparisons \n",
    "Here, we report the comparisons of different evaluations of the NMTF-link tool for different values of the **#initialization** and **#stop.criterion** parameters in the used graph_topology.tsv setting file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation plots\n",
    "The following figures are examples of applying the NMTF-link tool on the toy datasets in the **case_study_1** folder using different initialization strategies (**kmeans** and **spherical-kmeans**, respectively) and choosing the **#stop.criterion = relative_error**:"
   ]
  },
  {
   "attachments": {
    "aps_kmeans_relative_error.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![aps_kmeans_relative_error.png](attachment:aps_kmeans_relative_error.png)"
   ]
  },
  {
   "attachments": {
    "aps_skmeans_relative_error.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![aps_skmeans_relative_error.png](attachment:aps_skmeans_relative_error.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following figures are examples of applying the NMTF-link tool on the toy datasets in the **case_study_1** folder using different initialization strategies (**random**, **kmeans** and **spherical-kmeans**, respectively) and choosing the **#stop.criterion = maximum_metric** (which runs the algorithm 5 times with masking and chooses the stop iteration with best average evaluation metric):"
   ]
  },
  {
   "attachments": {
    "aps_random_maximum_metric.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xl4k1XaBvA7SZMm3QvdSxeQAsUiIKhTEAVlK44C6oiC4IJwKahg9RNwQ0VgmHEcGBUVRXBjhpFFFBFBtgGKSguIspS1tJSWLtAtXbKd74+0r5S22JamJ2nu33X1avLmzZunaNMn55znOSohhAARERERuQ217ACIiIiIqHUxASQiIiJyM0wAiYiIiNwME0AiIiIiN8MEkIiIiMjNMAEkIiIicjNMAImIiIjcDBNAIiIiIjfDBJCIiIjIzTABJCIiInIzTACJiIiI3AwTQCIiIiI3wwSQiIiIyM0wASQiIiJyM0wAiYiIiNwME0AiIiIiN8MEkIiIiMjNMAEkIiIicjNMAImIiIjcDBNAIiIiIjfDBJCIiIjIzTABJCIiInIzTACJiIiI3AwTQCIiIiI3wwSQiIiIyM0wASQiIiJyM0wAiYiIiNwME0AiIiIiN8MEkIiIiMjNMAEkIiIicjNMAImIiIjcDBNAIiIiIjfDBJCIiIjIzTABJCIiInIzTACJiIiI3AwTQCIiIiI3wwSQiIiIyM0wASQiIiJyM0wAiYiIiNwME0AiIiIiN8MEkIiIiMjNMAEkIiIicjNMAImIiIjcDBNAIiIiIjfDBJCIiIjIzTABJCIiInIzTACJiIiI3AwTQCIiIiI3wwSQiIiIyM0wASQiIiJyM0wAiYiIiNwME0AiIiIiN8MEkIiIiMjNMAEkIiIicjNMAImIiIjcDBNAIiIiIjfjITsAV2az2XDu3Dn4+vpCpVLJDoeIiIgaQQiB0tJSREREQK12z7EwJoBX4dy5c4iKipIdBhERETVDVlYWOnToIDsMKZgAXgVfX18A9v+B/Pz8JEdDROR8jEYjIiIiANg/NHt7e0uOiAgoKSlBVFSU8nfcHTEBvAo1075+fn5MAImI6qHRaJTbfn5+TADJqbjz8i33nPgmIiIicmMcASQiIofx8PDAQw89pNwmIufA30YiInIYT09PLF++XHYYRHQZJoAOJoSAxWKB1WqVHQqRU9NoNPDw8HDrNTlERK2FCaADmUwm5OTkoLy8XHYoRC7By8sL4eHh0Ol0skOhFiKEUN4Dvby8mOATOQkmgA5is9lw+vRpaDQaREREQKfT8Y2PqAFCCJhMJuTn5+P06dOIi4tz2+asbU15eTl8fHwAAGVlZawCJnISTAAdxGQywWazISoqCl5eXrLDIXJ6BoMBWq0WZ86cgclkgl6vlx0SEVGbxY/YDsZRDKLG4+8LEVHr4LstERERkZtpMwng//73P9x5552IiIiASqXCV1999YfP2bFjB/r06QO9Xo9OnTrh/fffb4VI266MjAyoVCocOHBAdih1OHNsl3v11VfRq1cv2WHUMXDgQEyfPl12GERE1ALaTAJoNBrRs2dPvPPOO406//Tp0xgxYgQGDBiA/fv344UXXsDTTz+N1atXOzhSaixnStpaM/l57rnnsGXLFuX+ww8/jFGjRrXKawPA9u3boVKpUFRUVOv4mjVrMGfOnFaLg4iIHKfNFIEkJSUhKSmp0ee///77iI6OxsKFCwEA8fHxSE1NxZtvvol77rnHUWES/SEfHx+larIlmUymq2qv0q5duxaMhoiIZGozI4BNtWfPHgwdOrTWsWHDhiE1NRVms7ne51RVVaGkpKTWV1uzatUq9OjRAwaDAe3bt8fgwYNhNBqVx5ctW4b4+Hjo9Xp069YNixcvvuL1Dh8+jBEjRsDHxwehoaEYP348CgoKlMdtNhsWLFiAzp07w9PTE9HR0Zg7dy4AoGPHjgCA3r17Q6VSYeDAgY2O4+eff0bv3r2h1+vRt29f7N+//w9/9sWLFyMuLg56vR6hoaG49957AdhH4Hbs2IFFixZBpVJBpVIhIyMDgH0ZwY033ghPT0+Eh4dj5syZsFgsyjUHDhyIJ598Ek8++SQCAgLQvn17vPTSSxBCNBjHpVPAr776Kj755BOsW7dOee3t27cDALKzszFmzBgEBgaiffv2GDlypBJXTdyjRo3C/PnzERERgS5dugAAPv/8c/Tt2xe+vr4ICwvD2LFjkZeXB8A+6jpo0CAAQGBgIFQqFR5++GHlZ7l0FPTixYuYMGECAgMD4eXlhaSkJBw/flx5fPny5QgICMD333+P+Ph4+Pj4YPjw4cjJyfnD/xbUdmg0Gtx777249957odFoZIdDLqRo7VfIfvY5lHy/SXYobZNogwCItWvXXvGcuLg4MXfu3FrHdu/eLQCIc+fO1fuc2bNnCwB1voqLi+ucW1FRIQ4fPiwqKirqPFZWVtbg1+XnX+nc8vLyPzy3Kc6dOyc8PDzEW2+9JU6fPi0OHjwo3n33XVFaWiqEEGLJkiUiPDxcrF69Wpw6dUqsXr1atGvXTixfvlwIIcTp06cFALF//37lekFBQWLWrFniyJEjYt++fWLIkCFi0KBByms+//zzIjAwUCxfvlycOHFC7Ny5U3z44YdCCCF+/vlnAUD88MMPIicnRxQWFjYqjrKyMhEcHCzGjBkjfvvtN/HNN9+ITp061Yrtcnv37hUajUasWLFCZGRkiH379olFixYJIYQoKioSiYmJYtKkSSInJ0fk5OQIi8Uizp49K7y8vMSUKVPEkSNHxNq1a0VQUJCYPXu2ct1bb71V+Pj4iGnTpomjR4+Kzz//XHh5eYklS5Y0+N9h9uzZomfPnkIIIUpLS8V9990nhg8frrx2VVWVMBqNIi4uTjz66KPi4MGD4vDhw2Ls2LGia9euoqqqSgghxEMPPSR8fHzE+PHjxW+//SZ+/fVXIYQQS5cuFRs2bBAnT54Ue/bsEX/6059EUlKSEEIIi8UiVq9eLQCI9PR0kZOTI4qKipSfZdq0aUqcd911l4iPjxf/+9//xIEDB8SwYcNE586dhclkEkIIsWzZMqHVasXgwYPF3r17RVpamoiPjxdjx45t8Ge/0u8NEbmX7BdeEIe7dhN577zT4tcuLi5u8O+3u3DrBHDevHm1ju3atUsAEDk5OfU+p7KyUhQXFytfWVlZzUoA60sia75GjBhR61wvL68Gz7311ltrnRsUFFTnnKZIS0sTAERGRka9j0dFRYkVK1bUOjZnzhyRmJgohKibAL788sti6NChtc6v+TdLT08XJSUlwtPTU0n4Lnf59RobxwcffCDatWsnjEaj8vh77713xQRw9erVws/PT5SUlNT7+OXJjxBCvPDCC6Jr167CZrMpx959913h4+MjrFar8rz4+Pha58yYMUPEx8fX+zpC1E4AhbAnciNHjqx1ztKlS+u8dlVVlTAYDOL7779XnhcaGqokhA2pSbRrEv1t27YJAOLixYsN/hscO3ZMABC7d+9WHi8oKBAGg0H897//FULYE0AA4sSJE7X+fUJDQxuMhQkgEdXImPCQONy1m7j4B3/Pm4MJoBBtZg1gU4WFhSE3N7fWsby8PHh4eKB9+/b1PsfT0xOenp6tEZ4UPXv2xO23344ePXpg2LBhGDp0KO69914EBgYiPz8fWVlZmDhxIiZNmqQ8x2KxwN/fv97rpaWlYdu2bfWuZzt58iSKiopQVVWF22+/vdExNiaOI0eOoGfPnrUacCcmJl7xukOGDEFMTAw6deqE4cOHY/jw4Rg9evQVm3gfOXIEiYmJtXZ46d+/P8rKynD27FlER0cDAP70pz/VOicxMRH/+Mc/YLVamz0llpaWhhMnTsDX17fW8crKSpw8eVK536NHjzrr/vbv349XX30VBw4cwIULF2Cz2QAAmZmZ6N69e6Ne/8iRI/Dw8MBNN92kHGvfvj26du2KI0eOKMe8vLxwzTXXKPfDw8OV6WYioisxZ2cDAHQdOkiOpG1y2wQwMTER33zzTa1jmzZtQt++faHVah362mVlZQ0+dnlCcKU/lpc3zb10/VdzaDQabN68GSkpKdi0aRPefvttvPjii/jpp5+UROjDDz+s9Ue/vphr2Gw23HnnnViwYEGdx8LDw3Hq1Kkmx1iTrFwpDnGF9XUN8fX1xb59+7B9+3Zs2rQJr7zyCl599VXs3bsXAQEB9T5HCFFne7+a13b0tn82mw19+vTBF198Ueex4OBg5fbl224ZjUYMHToUQ4cOxeeff47g4GBkZmZi2LBhMJlMjX79hv6NL/83ufx3SaVSNeu/D7kuo9HIreCoyYTFAnP1emEtE0CHaDMJYFlZGU6cOKHcP336NA4cOIB27dohOjoas2bNQnZ2Nj799FMAwOOPP4533nkHycnJmDRpEvbs2YOlS5fi3//+t8NjbcoboKPObYhKpUL//v3Rv39/vPLKK4iJicHatWuRnJyMyMhInDp1CuPGjWvUta6//nqsXr0asbGx8PCo+79aXFwcDAYDtmzZgscee6zO4zUjV1arVTkWGhr6h3F0794dn332GSoqKmAwGAAAP/744x/G6+HhgcGDB2Pw4MGYPXs2AgICsHXrVtx9993Q6XS14qh5ndWrV9dKelJSUuDr64vIyEjlvMtf+8cff0RcXFyjR//qe+3rr78eK1euREhICPz8/Bp1HQA4evQoCgoK8Ne//hVRUVEAgNTU1DqvB6DOa16qe/fusFgs+Omnn9CvXz8AQGFhIY4dO4b4+PhGx0NEVB/L+fOA1QpotfC45EMttZw2UwWcmpqK3r17o3fv3gCA5ORk9O7dG6+88goAICcnB5mZmcr5HTt2xIYNG7B9+3b06tULc+bMwb/+9S+3bgHz008/Yd68eUhNTUVmZibWrFmD/Px85Q/6q6++ivnz52PRokU4duwYfv31VyxbtgxvvfVWvdebOnUqLly4gAceeAA///wzTp06hU2bNuHRRx+F1WqFXq/HjBkz8Pzzz+PTTz/FyZMn8eOPP2Lp0qUAgJCQEBgMBmzcuBHnz59HcXFxo+IYO3Ys1Go1Jk6ciMOHD2PDhg148803r/izr1+/Hv/6179w4MABnDlzBp9++ilsNhu6du0KAIiNjcVPP/2EjIwMFBQUwGazYcqUKcjKysJTTz2Fo0ePYt26dZg9ezaSk5Nrjc5mZWUhOTkZ6enp+Pe//423334b06ZNa/R/l9jYWBw8eBDp6ekoKCiA2WzGuHHjEBQUhJEjR2Lnzp04ffo0duzYgWnTpuHs2bMNXis6Oho6nQ5vv/02Tp06ha+//rpOb7+YmBioVCqsX78e+fn59Y5Yx8XFYeTIkZg0aRJ27dqFX375BQ8++CAiIyMxcuTIRv9sRET1MZ21T/9qI8KhYvW4Y0hcf+jyrrSI1BUXsx8+fFgMGzZMBAcHC09PT9GlSxfx9ttv1zrniy++EL169RI6nU4EBgaKW265RaxZs0YIUX/RxrFjx8To0aNFQECAMBgMolu3bmL69OlK8YLVahVvvPGGiImJEVqtVkRHR9cqzvnwww9FVFSUUKvVtYperhSHEELs2bNH9OzZU+h0OtGrVy+lsrWhIpCdO3eKW2+9VQQGBgqDwSCuu+46sXLlSuXx9PR08ac//UkYDAYBQJw+fVoIIcT27dvFDTfcIHQ6nQgLCxMzZswQZrNZed6tt94qpkyZIh5//HHh5+cnAgMDxcyZM2sVb1zu8iKQvLw8MWTIEOHj4yMAiG3btgkhhMjJyRETJkwQQUFBwtPTU3Tq1ElMmjRJ+f+xvuIRIYRYsWKFiI2NFZ6eniIxMVF8/fXXdf5tXn/9dREWFiZUKpV46KGHlJ/l0kKYCxcuiPHjxwt/f39hMBjEsGHDxLFjx5THly1bJvz9/Wu99tq1a69YnOSKvzd0ZWVlZUpRWlM7E5D7urh6jTjctZs488gjDrk+i0CEUAnBBTnNVVJSAn9/fxQXF9eZhqusrMTp06fRsWNH6PV6SRGSbAMHDkSvXr2UhuN0Zfy9aXu4BpCaI/9fb6Ng8WIE/OUvCJ/zeotf/0p/v91Fm5kCJiIiorahpgJYe8l6ampZTACJiIjIqZiy7WuZWQHsOG2mCpjIGdVs20bkrjQaDUaMGKHcJmoMc/Y5AIA2MkJyJG0XE0AiInIYvV6Pb7/9VnYY5EKEyQRL9UYNbALtOJwCJiIiIqdhzs0FhIDK0xOaoCDZ4bRZTAAdjEXWRI3H3xciMlf3MtVGRjp8VyV3xgTQQWq2wCovL5ccCZHrqPl9cfR2jNR6jEYjvL294e3tDaPRKDsccgGmmgSwAyuAHYlrAB1Eo9EgICBA2cvXy8uLn2SIGiCEQHl5OfLy8hAQEMBigTaGH4SpKX4vAGEC6EhMAB0oLCwMAJQkkIiuLCAgQPm9ISL3VDMFzAIQx2IC6EAqlQrh4eEICQmB2WyWHQ6RU9NqtRz5IyI2gW4lTABbgUaj4R82IiKiRlCaQEdyBNCRWARCRERETsFWWQlrfgEAFoE4GhNAIiIicgrmc/YCELWXFzQBAZKjads4BUxERA6jVqtx6623KreJruTS9X/snOFYTACJiMhhDAYD98SmRlOaQLMC2OH4cYyIiIicgjICyATQ4ZgAEhERkVMwna2ZAo6QHEnbxwSQiIgcxmg0Ijg4GMHBwdwKjv4Qm0C3Hq4BJCIihyooKJAdArkINoFuPRwBJCIiIulsRiOsFy8CYALYGpgAEhERkXSm6tE/tZ8fNH5+kqNp+5gAEhERkXTmmgIQ7gDSKpgAEhERkXQ16/90nP5tFUwAiYiISDqlCXQkK4BbA6uAiYjIYdRqNfr27avcJmqI+RybQLcmJoBEROQwBoMBe/fulR0GuQA2gW5d/DhGRERE0ilrADkC2CqYABIREZFU1pIS2EpKAADaCI4AtgYmgERE5DDl5eWIjY1FbGwsysvLZYdDTqqmAETTrh3U3t6So3EPXANIREQOI4TAmTNnlNtE9TFxC7hWxxFAIiIikopNoFsfE0AiIiKSik2gWx8TQCIiIpJKaQLNCuBWwwSQiIiIpDIrawCZALYWJoBEREQkjRCCRSASsAqYiIgcRqVSoXv37sptostZi4ogqlsEcReQ1sMEkIiIHMbLywuHDh2SHQY5sZr1fx7BwVB7ekqOxn1wCpiIiIikUdb/sQCkVTEBJCIiImmUCmCu/2tVTACJiMhhysvLce211+Laa6/lVnBUL5PSAoYJYGviGkAiInIYIQQOHz6s3Ca6nDn7HACOALY2jgASERGRNDVTwDquAWxVTACJiIhICiEEzOc4AigDE0AiIiKSwpKfD1FVBajV0IaHyw7HrTABJCIiIilqWsB4hIVCpdVKjsa9MAEkIiIiKcxn7QmgLoLTv62NVcBEROQwKpUKMTExym2iS7EJtDxMAImIyGG8vLyQkZEhOwxyUuZsNoGWhVPAREREJAVHAOVhAkhERERSmKrXAGojIyRH4n6YABIRkcNUVFTghhtuwA033ICKigrZ4ZATEVYrzDk5ANgEWgauASQiIoex2WxITU1VbhPVsOTlAWYz4OEBj9BQ2eG4HY4AEhERUaur2QJOGx4OlUYjORr3wwSQiIiIWp1JKQBhBbAMTACJiIio1ZmVAhAmgDIwASQiIqJWV9MChgUgcjABJCIiolanrAHkCKAU0quAzWYzcnNzUV5ejuDgYLRr1052SERE1IKCgoJkh0BOSGkCHckRQBmkjACWlZXhgw8+wMCBA+Hv74/Y2Fh0794dwcHBiImJwaRJk7B3714ZoRERUQvy9vZGfn4+8vPz4e3tLTscchLCbIY5NxcARwBlafUE8J///CdiY2Px4Ycf4rbbbsOaNWtw4MABpKenY8+ePZg9ezYsFguGDBmC4cOH4/jx460dIhERETmQ+fx5wGaDSqeDRzBHiGVo9SnglJQUbNu2DT169Kj38RtvvBGPPvoo3n//fSxduhQ7duxAXFxcK0dJREREjqKs/4uIgErNcgQZWj0B/PLLLxt1nqenJ6ZMmeLgaIiIyJEqKiqQlJQEAPjuu+9gMBgkR0TOQEkAWQEsjfQikEudOXMGRqMR3bp1g5qfCIiIXJ7NZsOOHTuU20QAm0A7AylZ1ieffIKFCxfWOjZ58mR06tQJPXr0QEJCArKyspp83cWLF6Njx47Q6/Xo06cPdu7cecXzFy5ciK5du8JgMCAqKgrPPPMMKisrm/y6RERE1HhsAi2flATw/fffh7+/v3J/48aNWLZsGT799FPs3bsXAQEBeO2115p0zZUrV2L69Ol48cUXsX//fgwYMABJSUnIzMys9/wvvvgCM2fOxOzZs3HkyBEsXboUK1euxKxZs67qZyMiIqIrYxNo+aQkgMeOHUPfvn2V++vWrcNdd92FcePG4frrr8e8efOwZcuWJl3zrbfewsSJE/HYY48hPj4eCxcuRFRUFN577716z9+zZw/69++PsWPHIjY2FkOHDsUDDzyA1NTUq/rZiIiI6MrYBFo+KQlgRUUF/Pz8lPspKSm45ZZblPudOnVCbnV/oMYwmUxIS0vD0KFDax0fOnQoUlJS6n3OzTffjLS0NPz8888AgFOnTmHDhg244447GnydqqoqlJSU1PoiIiKixrOZTLDk5QFgEYhMUopAYmJikJaWhpiYGBQUFODQoUO4+eablcdzc3NrTRH/kYKCAlitVoSGhtY6Hhoa2mAief/99yM/Px8333wzhBCwWCx44oknMHPmzAZfZ/78+U2emiYiIqLf1Uz/qgwGaAIDJUfjvqQkgBMmTMDUqVNx6NAhbN26Fd26dUOfPn2Ux1NSUpCQkNDk66pUqlr3hRB1jtXYvn075s6di8WLF+Omm27CiRMnMG3aNISHh+Pll1+u9zmzZs1CcnKycr+kpARRUVFNjpOIyJ14eXnJDoGciDn7HABA1yGywb/R5HhSEsAZM2agvLwca9asQVhYWJ3egLt378YDDzzQ6OsFBQVBo9HUGe3Ly8urMypY4+WXX8b48ePx2GOPAQB69OgBo9GIyZMn48UXX6y3DY2npyc8PT0bHRcRkbvz9vaG0WiUHQY5kd+bQHP9n0xSEkC1Wo05c+Zgzpw59T7e2GbRNXQ6Hfr06YPNmzdj9OjRyvHNmzdj5MiR9T6nvLy8TpKn0WgghIAQokmvT0RERI1jVnoAcv2fTNIaQX/55Zf46quvYDabMXjwYEyePPmqrpecnIzx48ejb9++SExMxJIlS5CZmYnHH38cgH3aOTIyEvPnzwcA3HnnnXjrrbfQu3dvZQr45Zdfxl133QWNRnPVPx8RERHVZc5mBbAzkJIALlmyBI8//jji4uKg1+uxevVqnD59WknOmmPMmDEoLCzE66+/jpycHCQkJGDDhg2IiYkBAGRmZtYa8XvppZegUqnw0ksvITs7G8HBwbjzzjsxd+7cq/75iIjIrrKyEvfccw8AYPXq1dDr9ZIjItlMZ7kLiDNQCQnznT169MCoUaOUKeDly5fjqaeeQmlpaWuHclVKSkrg7++P4uLiWm1tiIjIzmg0wsfHBwBQVlYGb29vyRGRbMf63wxrYSE6rlkNfffuUmLg329JI4CnTp3CI488otwfP348Jk+ejNzcXISFhckIiYiICABgttpwusCIo7mlSM8tQUZBOSwO3MdYr9UgLsQHXUJ90S3MDx0CDVCrnbM61mYTyLpYjvTcUqTnluJEfhkqzdZGP9/DVIknCwsBAM/vykdV2h9vvvDn6yJwZ8+IZsdM9ZOSAFZUVCifCAF78YWnpyfKy8tlhENERG5ICIHsogqk55biaG4pjp23JzUn88tgtsorBvTWaRAX6ouuob7oGuaLbmH27+19WrcLRUFZlfJvk55bgvTzZTh+vhTlpsYnfJeLLrF36yjz0OPrU2UAyv7wOfHh7jlC52jSikA++uijWkmgxWLB8uXLERQUpBx7+umnZYRGRERtzEWjCenVCV5NQnPsfBnKqiz1nu+t06BLdfJ1TbAP9FrHFQeWVJpxLLcU6efLcDKvDEaTFQeyinAgq6jWeUE+OnQN80XXUD90C/NFlzBfdAn1gZfu6v6UG6ssOHbengAfrR7ZO3a+FAVlpnrP12nU6Bzig65hvugS6gtffeNf3++AEdgKqMMj8MaoxvX7va5D4zeGoMaTsgYwNjb2D5s/qlQqnDp1qpUiah6uISAiurLWXgNYabbi+PkyHM0tqZXQ5JVW1Xu+h1qFa4LtyYw9ubJ/7xBokNKk2Gy1IaN6+vnS+DMv1D9DplIB0e28lLhrRgxj23vDQ1O71Zml1tR2qZIQN+ba9lFIP3QN86n32o114YsvcH7OG/AZfDui3nmnWddoCfz7LWkEMCMjQ8bLEhFRNYvVhrMXK5BRaERGgRHnS6vgiOEAU+XvycU/NqVDp2/5XUFsQiCzsBzHzpcio9AIWwM/R2SAQZlOtSdKfugY5A2dR/OSGUfQatSIC/VFXKhvrePGKguO55UhPbekzijdmcJynCksx6bD55XzdRo1rgnxQbcwXwghlNFFk7X+tYxBPp7oGuajjC52DfNFXAuMLl7OXF0BrGMLGOmkTQFfSWFhIT777DNMnz5ddihERC7LYrUhu6gCpwuMOFNYjtMFRmQU2m9nXSiHpaFMqQXZTJXK7Y92noZa5/g2MIFeWiXB61I9MtYl1Ae+eq3DX9tRvD090CsqAL2iAmodv3Sd3rHcUhw9b/9eYbbiSE4JjuSU1DrfS6epLjapPeLZWusLlSbQkWwCLZvTJIBCCGzatAlLly7FunXr4OfnxwSQiOgP1JfknSk0IqMRSZ6nhxqx7b0R094LEQEGaBxVeXrbIcdc9xLh/nolsQn29XSbPWaDfDwR1NkT/Tv/vn7eZhM4e7FCmQYHgK5h9pG9yAC5FcbKNnDsASid9AQwIyMDH3/8MZYvX47s7GyMGzcO3377LQYNGiQ7NCIip5FbXIn086XIKDA2O8nrGOSNmPbeiA3yQmx7b4T56Z223Qg1n1qtQnR7L0S398LQa52rtRpHAJ2HlASwqqoKa9aswUcffYSUlBQkJSXhrbfewgMPPIDdkw6dAAAgAElEQVSZM2eiu6TGkEREzmjDrzl4+t/7G0z0mOSRK7CWlcFaXAyA28A5AykJYGRkJLp3744HH3wQq1atQmBgIADggQcekBEOEZHTMlZZ8OrXh2CxCXQM8kZciI9LJXmVlZUYP348AOCzzz7jVnBurGb6VxMQAI0Pd4SRTUoCaLVaoVKpoFKpoNE4rrcSEZGre2/7SeSVViGmvRc2Th8ATw/Xes+0Wq1YtWoVAPu2n+S+lOnfDpz+dQZSat9zcnIwefJk/Pvf/0ZYWBjuuecerF271m0W7RIRNUbWhXIs2Wnvh/rCiHiXS/6ILqUUgHD61ylISQD1ej3GjRuHrVu34tdff0V8fDyefvppWCwWzJ07F5s3b4bV2vytZoiI2oK/bjwKk8WGxE7tMbR7qOxwiK6KSRkBZALoDKR3v7zmmmvwxhtv4MyZM/j2229RVVWFP//5zwgN5ZsdEbmvn09fwLcHc6BWAa/c2Z0zJOTyappAcwTQOUhvA1NDrVYjKSkJSUlJyM/Px2effSY7JCIiKWw2gdfX23vnjbkhGvHh7rlVFbUtNWsAdVwD6BSkjwDWJzg4GMnJyQDsDaKJiNzJqn1n8Vt2CXw9PfDs0C6ywyG6akIIrgF0Mq2eAMbHx2PFihUwmUxXPO/48eN44oknsGDBglaKjIhIvrIqC/7+fToA4KnbOyOolbboInIkW3ExbEYjACaAzqLVp4DfffddzJgxA1OnTsXQoUPRt29fREREQK/X4+LFizh8+DB27dqFw4cP48knn8SUKVNaO0QiImkWbzuB/NIqxLb3wsP9OsoO56p5eXmhrKxMuU3uyVS9/k8TFAQ1e0E6hVZPAG+77Tbs3bsXKSkpWLlyJVasWIGMjAxUVFQgKCgIvXv3xoQJE/Dggw8iICDgjy9IRNRGZF0ox0e7TgOwt33ReTjlKp0mUalU8PZm0193p6z/4+if05BWBNKvXz/069dP1ssTETmdv35nb/vSv3N7DGHbF2pDlPV/LABxGq7/8ZKIqA346VQhvv3V3vbl5T+3nbYvVVVVePjhh/Hwww+jqqpKdjgkiTmbBSDOhgkgEZFkVpvA6+sPAwAeuDEa3cLaTtsXi8WCTz75BJ988gksFovscEgSNoF2PkwAiYgkW512FofO2du+JA9h2xdqe9gE2vkwASQikqisyoK/Vbd9efr2OLRn2xdqY4QQbALthJgAEhFJ9O62Eygos7d9eahfrOxwiFqctbAQorISUKngER4uOxyqJj0BPHnyJF566SU88MADyMvLAwBs3LgRhw4dkhwZEZFjZV0ox9Kd9rYvL97RvU20fSG6XM3on0doKNQ6neRoqIbUd5sdO3agR48e+Omnn7BmzRqlWejBgwcxe/ZsmaERETncvA1HYLLacHPnIAyOD5EdDpFDmLgFnFOSmgDOnDkTb7zxBjZv3gzdJZ8KBg0ahD179kiMjIjIsX48VYjvfsuFWgW89Of4NtP2hehy5uxzAAAdK4CdirRG0ADw66+/YsWKFXWOBwcHo7CwUEJERESOZ7UJvP6Nve3L2JvaVtuXy3l5eSnLe7gVnHtSmkBHsgDEmUgdAQwICEBOTk6d4/v370ckh4qJqI1alZaFwzkl8NV74JnBbbvti0qlQnBwMIKDgznK6aZq1gByCti5SE0Ax44dixkzZiA3NxcqlQo2mw27d+/Gc889hwkTJsgMjYjIIUorzfh7dduXaWz7Qm6A28A5J6kJ4Ny5cxEdHY3IyEiUlZWhe/fuuOWWW9CvXz+89NJLMkMjInKId7edREGZCZ2CvDEhMVZ2OA5XVVWFqVOnYurUqdwKzg0Jmw3mc/Y1gBwBdC4qIYSQHcSpU6ewb98+2Gw29O7dG3FxcbJDapSSkhL4+/ujuLgYfn5tdw0PEbWMzMJyDH5rB0xWG5Y+1Be3x4fKDsnhjEYjfHx8AABlZWXw9vaWHBG1JvP58zhx60BAo0G3Xw5A5SG19EDBv98Si0DMZjO6du2K9evXo3v37ujUqZOsUIiIWkVN25cBcUG4rRvbvlDbp0z/hoU5TfJHdtKmgLVaLaqqqrgomIjcwp6Thdh4qLrtyx3d+d5HbkEpAOH6P6cjdQ3gU089hQULFsBiscgMg4jIoaw2gdfX29u+jLspBl3DfCVHRNQ62ATaeUkdj/3pp5+wZcsWbNq0CT169KizNmTNmjWSIiMiajlfpmbhSE4J/PQeeGZI2277QnSp30cAmQA6G6kJYEBAAO655x6ZIRAROVRppRlvbqpu+zK4C9p5cy9Uch/ms/YEUMcpYKcjNQFctmyZzJcnInK4d7adUNq+jP9TjOxwiFoVm0A7L6coycnPz0d6ejpUKhW6dOmC4OBg2SEREV21M4VGLNuVAcC+36/OQ+qyaykMBgNOnz6t3Cb3ISwWmKt3+2IRiPOR+m5kNBrx6KOPIjw8HLfccgsGDBiAiIgITJw4EeXl5TJDIyK6ape2fRnU1T3bvqjVasTGxiI2NhZqtfslwO7Mcv48YLVCpdXCgwM7Tkfqb2NycjJ27NiBb775BkVFRSgqKsK6deuwY8cOPPvsszJDIyK6KiknC/D9ofPQqFV4+c9s+0Lux1S9/k8bEQEVk3+nI3UKePXq1Vi1ahUGDhyoHBsxYgQMBgPuu+8+vPfee/KCIyJqJqtN4PVvatq+RKNLqPu2fTGZTHjxxRcB2Lf/1OlYBOMuzGwB49SkpuTl5eUIDa27FVJISAingInIZa3cm4WjuaX2ti+D3bvti9lsxptvvok333wTZrNZdjjUitgE2rlJTQATExMxe/ZsVFZWKscqKirw2muvITExUWJkRETNU1Jpxj+q275MH9wFgWz7Qm7KnM0RQGcmdQp40aJFGD58ODp06ICePXtCpVLhwIED0Ov1+P7772WGRkTULO9sPYFCowmdgr0xPpFtX8h9mdgE2qlJTQATEhJw/PhxfP755zh69CiEELj//vsxbtw4tgsgIpeTUWDEst32licv39EdWg0XvpP7UppAcwTQKUnvA2gwGDBp0iTZYRARXbV5G47AbBW4pUswBnZl2wtyX8JksreBAdcAOiupH0/nz5+Pjz/+uM7xjz/+GAsWLJAQERFR86ScKMCmw9VtX+6IZ9sXcmvmnBxACKj0emjat5cdDtVDagL4wQcfoFu3bnWOX3vttXj//fclRERE1HRWm8Dr6+1tXx68KRpxbtz2hQiovQUcPww5J6lTwLm5uQgPD69zPDg4GDnV28cQETm7/ZkXcTS3FL6eHpju5m1fLmcwGPDbb78pt8k9mGp6ALIAxGlJTQCjoqKwe/dudOzYsdbx3bt3IyIiQlJURERNk3rmIgCgX+f2bPtyGbVajWuvvVZ2GNTKzNnnALAAxJlJTQAfe+wxTJ8+HWazGbfddhsAYMuWLXj++ee5FRwRuYy06gSwb0w7yZEQOYffdwFhAYizkpoAPv/887hw4QKmTJkCk8kEANDr9ZgxYwZmzZolMzQiokYRQmBfdQJ4fUyg5Gicj8lkwrx58wAAL7zwAreCcxPcBs75qYQQQnYQZWVlOHLkCAwGA+Li4uDp6Sk7pEYpKSmBv78/iouL4efnJzscIpLgdIERg97cDp2HGr++OhSeHhrZITkVo9EIHx8fAPb3em9vb8kRUWs4NmAArPkFiF21CoYE51sCwL/fkquAa/j4+OCGG26Ar68vTp48CZvNJjskIqJGSc24AAC4LtKfyR8RAFtlJaz5BQAAbSTX8zsrKQngJ598goULF9Y6NnnyZHTq1Ak9evRAQkICsrKyZIRGRNQkNev/+sRy+pcIAMzn7AUgam9vaAICJEdDDZGSAL7//vvw9/dX7m/cuBHLli3Dp59+ir179yIgIACvvfaajNCIiJqEBSBEtSnr/zp0YA9AJyalCOTYsWPo27evcn/dunW46667MG7cOADAvHnz8Mgjj8gIjYio0YrKTTieVwYAuD6aIx1EQO0m0OS8pIwAVlRU1Fp0mZKSgltuuUW536lTJ+Tm5soIjYio0fZnFgEAOgV5o72PaxSvETkam0C7BikJYExMDNLS0gAABQUFOHToEG6++Wbl8dzc3FpTxEREzij1jL0AhO1fiH7HJtCuQcoU8IQJEzB16lQcOnQIW7duRbdu3dCnTx/l8ZSUFCQkJMgIjYio0VIzatb/MQFsiF6vx88//6zcprbv0jWA5LykJIAzZsxAeXk51qxZg7CwMHz55Ze1Ht+9ezceeOABGaERETWK2WrDL2ftU8B9mAA2SKPR4IYbbpAdBrUirgF0DVKmgNVqNebMmYP9+/fju+++Q3x8fK3Hv/zyS0ycOLHJ1128eDE6duwIvV6PPn36YOfOnVc8v6ioCFOnTkV4eDj0ej3i4+OxYcOGJr8uEbmfw+dKUGm2wd+gxTXBPrLDIXIKNqMR1ov2kXEmgM5N6lZwLWnlypWYPn06Fi9ejP79++ODDz5AUlISDh8+jOjo6Drnm0wmDBkyBCEhIVi1ahU6dOiArKws+Pr6SoieiFyN0v8vJhBqNVtdNMRkMmHRokUAgGnTpnEruDbOVD36p/b3h4Z/T51am0kA33rrLUycOBGPPfYYAGDhwoX4/vvv8d5772H+/Pl1zv/4449x4cIFpKSkQKvVArAXpxARNcalCSA1zGw24/nnnwcATJkyhQlgG2c+a08AWQDi/JxiK7irZTKZkJaWhqFDh9Y6PnToUKSkpNT7nK+//hqJiYmYOnUqQkNDkZCQgHnz5sFqtbZGyETkwoQQSgUwE0Ci37EAxHW0iRHAgoICWK1WhIaG1joeGhraYD/BU6dOYevWrRg3bhw2bNiA48ePY+rUqbBYLHjllVfqfU5VVRWqqqqU+yUlJS33QxCRy8guqsD5kip4qFXo2YENoIlqsADEdbSJEcAal285I4RocBsam82GkJAQLFmyBH369MH999+PF198Ee+9916D158/fz78/f2Vr6ioqBaNn4hcQ83077URfjDoNJKjIXIepmw2gXYVUkcArVYrli9fji1btiAvLw82m63W41u3bm3UdYKCgqDRaOqM9uXl5dUZFawRHh4OrVYLjeb3N+/4+Hjk5ubCZDLVu05l1qxZSE5OVu6XlJQwCSRyQ7+v/+P+v0SXqmkCzRFA5yc1AZw2bRqWL1+OO+64AwkJCc3eNFqn06FPnz7YvHkzRo8erRzfvHkzRo4cWe9z+vfvjxUrVsBms0Gttg+EHjt2DOHh4Q0uUvb09ISnJ7d7InJ3NQ2guf6PqLaaNYA6rgF0elITwP/85z/473//ixEjRlz1tZKTkzF+/Hj07dsXiYmJWLJkCTIzM/H4448DsO8+EhkZqVQEP/HEE3j77bcxbdo0PPXUUzh+/DjmzZuHp59++qpjIaK2q6zKgqO59vW/fWOZABLVsJaUwFZaCgDQRkRIjob+iNQEUKfToXPnzi1yrTFjxqCwsBCvv/46cnJykJCQgA0bNiitXTIzM5WRPgCIiorCpk2b8Mwzz+C6665DZGQkpk2bhhkzZrRIPETUNh3ILIJNAJEBBoT6cWuzP6LX67Ft2zblNrVdNaN/mvbtofbykhwN/RGVEELIevF//OMfOHXqFN55551mT//KVFJSAn9/fxQXF8PPz092OETUChb+cAwLfziOkb0isOj+3rLDIXIaJZs3I/upp6G/7jp0/O9K2eFcEf9+Sx4B3LVrF7Zt24bvvvsO1157rdKQucaaNWskRUZEVL+aApC+XP9HVIvSBJoVwC5BagIYEBBQq2iDiMiZWW0C+zOLAADXMwFsFLPZjCVLlgAAJk+eXOeDPrUd7AHoWqQmgMuWLZP58kRETXLsfCnKqizw1mnQLcw9p42aymQy4cknnwQAPPzww0wA2zBlF5BIVgC7AqfYCSQ/Px/p6elQqVTo0qULgoODZYdERFRHavX0b+/oQGjUrrdumciRzNncBs6VSN0JxGg04tFHH0V4eDhuueUWDBgwABEREZg4cSLKy8tlhkZEVMe+M+z/R1QfIQRMShNotoBxBVITwOTkZOzYsQPffPMNioqKUFRUhHXr1mHHjh149tlnZYZGRFRH6pkLAJgAEl3OevEiRPXADdcAugapU8CrV6/GqlWrMHDgQOXYiBEjYDAYcN99911xX14iotaUV1KJrAsVUKuA3tEBssMhcio1BSAeISFQN7CbFjkXqSOA5eXl9e7VGxISwilgInIqNe1fuob5wVfPQgaiSykFIFz/5zKkJoCJiYmYPXs2KisrlWMVFRV47bXXkJiYKDEyIqLaUpX1fxz9I7ocW8C4HqlTwIsWLcLw4cPRoUMH9OzZEyqVCgcOHIBer8f3338vMzQiolp+bwDdTnIkrsXT0xPr169XblPbZFJGAJkAugqpCWBCQgKOHz+Ozz//HEePHoUQAvfffz/GjRsHg8EgMzQiIkWl2YpD54oBsACkqTw8PHDHHXfIDoMczFxdAazjCKDLkN4H0GAwYNKkSbLDICJq0MGzxTBbBUJ8PdEhkB9OiS7HNYCup9UTwK+//hpJSUnQarX4+uuvr3juXXfd1UpRERE1rKb9S9/YQKhUbADdFGazGV988QUAYNy4cdwJpA0SQsB8rroHIBNAl9HqCeCoUaOQm5uLkJAQjBo1qsHzVCoVrFZrK0ZGRFS/tAz7+r/rozn921QmkwmPPPIIAOAvf/kLE8A2yJKfD1FVBajV0NbT2YOcU6sngDabrd7bRETOSAiBtMzqApBYFoAQXc58troCOCwMKib4LkNqG5j6FBUVyQ6BiEhxMt+IonIzPD3U6B7uJzscIqfDFjCuSWoCuGDBAqxcuVK5/5e//AXt2rVDZGQkfvnlF4mRERHZ1ez/2zMqADoPp/vMTCSdOZsFIK5I6rvZBx98gKioKADA5s2b8cMPP2Djxo1ISkrC//3f/8kMjYgIAPf/JfojHAF0TVLbwOTk5CgJ4Pr163Hfffdh6NChiI2NxU033SQzNCIiAL/vANKXCSBRvdgE2jVJHQEMDAxEVlYWAGDjxo0YPHgwAPuia1YAE5FsF4wmnMo3AmAFMFFD2ATaNUkdAbz77rsxduxYxMXFobCwEElJSQCAAwcOoHPnzjJDIyJS1v9dE+yNQG+d5Ghck6enJ/773/8qt6ltEVYrzDk5ALgG0NVITQD/+c9/IjY2FllZWfjb3/4GHx8fAPap4SlTpsgMjYjo9/Yv3P+32Tw8PPCXv/xFdhjkIJa8PMBsBrRaeISEyA6HmkBqAqjVavHcc8/VOT59+nQJ0RAR1VbTAJoFIET1U7aACw+HSqORHA01BbeCIyKqh8liwy9n7X1J+8QyAWwui8WCtWvXAgBGjx4NDw/pW9BTCzJVVwDrWADicrgVHBFRPQ6dK0aVxYZALy06BXnLDsdlVVVV4b777gMAlJWVMQFsY5RdQFgA4nK4FRwRUT3Szvw+/atSqSRHQ+SclCngSBaAuBq2tSciqsfvCSALQIgawibQrktqAvj000/jX//6V53j77zzDgtBiEgaIYTSAJoFIEQNM2WzCbSrkpoArl69Gv37969zvF+/fli1apWEiIiIgLMXK5BfWgWtRoXrOvjLDofIKQmzGZbc8wA4AuiKpCaAhYWF8Pev++bq5+eHgoICCRE5EasFMBllR0Hklmr2/02I9Idey9YWRPUx5+YCNhtUnp7wCA6WHQ41kdRyrM6dO2Pjxo148sknax3/7rvv0KlTJ0lROYFt84Bd/wRufgYY9ILsaIjcTmpN/z9u/3bVhBDK7aM33gQvNZeetxnVhZzaiAgWSrkgqQlgcnIynnzySeTn5+O2224DAGzZsgX/+Mc/sHDhQpmhyaX3B6wmIO+w7EiI3FJNAUhf9v+7arYjRzA3LAwAoLVYACYKbY73gJtlh0DNIDUBfPTRR1FVVYW5c+dizpw5AIDY2Fi89957mDBhgszQ5AruZv+ed1RuHERuqKTSjPTzpQCA61kActWMX3+D0f4B8BuRhJDnn5cdDrU0tRpabgHnkqR35HziiSfwxBNPID8/HwaDQdkP2K2FdLd/v3AKMFcCWr3ceIjcyIHMIggBRLfzQogvf/euhq28HCUbNgAAAsbcD231SCARySd9MYbFYsEPP/yANWvWKGtFzp07h7KyMsmRSeQbZp8GFlag8LjsaIjcCtu/tJzSzZthKivDLoMe2/LOw2KxyA6JiKpJHQE8c+YMhg8fjszMTFRVVWHIkCHw9fXF3/72N1RWVuL999+XGZ48KpV9FDBzj30aOKyH7IiI3MY+JoAtpmj1GpiEwOQDB4C77uJWcEROROoI4LRp09C3b19cvHgRBoNBOT569Ghs2bJFYmROQFkHyEIQotZisdqwP5MFIC3BlJWF8p9/ZtEHkZOS+lFs165d2L17N3Q6Xa3jMTExyK7eXsZthcTbv+ezEISotRzNLYXRZIWvpwfiQnxlh+PSiteuBQB433QTcCxdcjREdDmpI4A2mw1Wq7XO8bNnz8LX183ffGsSwLwjcuMgciP7qkf/escEQqPmyFVzCasVRWu/AgD4jbxLcjREVB+pCeCQIUNq9ftTqVQoKyvD7NmzMWLECImROYHg6gTwYgZgKpcaCpG7YAPolmH88UdYcnKg9vOD76BBssMhonpITQD/+c9/YseOHejevTsqKysxduxYxMbGIjs7GwsWLJAZmnw+wYBXEAABFHD6hKg1sAF0yyhevQYA4P/nP0Pt6Sk5GiKqj9Q1gBEREThw4AD+85//IC0tDTabDRMnTsS4ceNqFYW4rZB4IGOnfRo4orfsaIjatNziSmQXVUCtAnpFBcgOx2VZi4tR+sMPAAD/u++GTXI8RFQ/aQmg2WzG5MmT8fLLL+ORRx7BI488IisU5xXc7fcEkIgcqmb0Lz7cD96ebFXSXMXffgthMsGza1for+0Oi8WCd955BwDqFPwRkTzSpoC1Wi3WVleJUQNYCUzUalLPXAAA9GX/v6tSM/0bcM/dUKlU0Gq1mDp1KqZOnQqtVis5OiKqIXUN4OjRo/HVV1/JDMG5sRKYqNXUjABy/9/mq0xPR+WhQ4BWC78775QdDhFdgdR5js6dO2POnDlISUlBnz594O3tXevxp59+WlJkTqKmGXRxFlBZAuj95MZD1EaVmyw4dK4EANA3tp3kaFxX8Rr76J/voEHwCLQn0larFTt37gQADBgwABqNRlp8RPQ7qQngRx99hICAAKSlpSEtLa3WYyqVigmgVzvAJwwoywXy04GoG2RHRNQm/ZJVDKtNINxfj8gAFqA1hzCZULzuawCA/92jleOVlZUYVN0KpqysrM4HfSKSQ2oCePr0aZkv7xpCulUngEeYABI5SE0DaE7/Nl/ptu2wFhXBIzgYPjffLDscIvoD0tYAlpaWYvPmzfjuu+9QUFAgKwznF9Ld/j2PhSBEjpKawQKQq1Uz/es/ahRUHqyiJnJ2Un5LDx48iKSkJOTk5AAA/Pz8sGrVKgwePFhGOM6tZh1g3mG5cRC1UTabUApA+jABbBbz+TyUVa/zu3T6l4icl5QRwJkzZyI6Ohq7du1Camoqbr31Vjz55JMyQnF+bAVD5FAn88tQUmmBQatBfDgLrZqjeN06wGaD4frr4dmxo+xwiKgRpIwApqamYsOGDejbty8A4OOPP0ZISAjKysrg4+MjIyTnVTMCWJoDVFwEDByhIGpJqdWjfz2j/KHVSO2M5ZKEEMr0b8A9d0uOhogaS8q7XUFBAaKjo5X77du3h5eXF/Lz82WE49z0foBfB/ttrgMkanHK/r8xbP/SHBX798OUkQGVwQDfYcNlh0NEjSRlBFClUqG0tBR6vR6A/RNkzbGSkhLlPD8/TscAsE8Dl5y1VwLHJMqOhqhNUdb/xXJ0vTmKVq8GAPgNHw6NT90WL1qtFn/729+U20TkHKQkgEIIdOnSpc6x3r17K7dVKhWsVquM8JxPSDfgxGbuCELUwgrLqnC6wAgAuD6KCWBT2YxGlH63EUDD0786nQ7/93//15phEVEjSEkAt23bJuNlXVcwt4QjcoSa0b8uoT7w9+LoVFOVfL8JtvJyaGOiYejTR3Y4RNQEUhLAW2+9VcbLui7uCUzkEGz/cnWK1tinfwNG3w2VSlXvOVarFfv27QMAXH/99dwKjshJsFunKwjuav9eXgAYCwDvILnxELURvyeALABpKlNGBipS0wC1Gv6jRjZ4XmVlJW688UYA3AqOyJmw54Er0HkDgbH22xwFJGoRVRYrDmYXA+AOIM1RtPYrAIB3//7QhoVJjoaImooJoKvgOkCiFvVbdglMFhvae+sQ095LdjguRVitKP7KngCy9x+Ra2IC6CpCqhtC5zMBJGoJaWfs+//2iQlscP0a1c+YkgLL+fPQ+PvD57bbZIdDRM3gFAngiRMn8P3336OiogKAvQ0MXSaku/07m0ETtYjUDBaANFfRavvOH3533gm1Tic5GiJqDqkJYGFhIQYPHowuXbpgxIgRyMnJAQA89thjePbZZ2WG5nxqtoTLOwwwQSa6KkII7Mus3gGEDaCbxHLxIsq2bAHA6V8iVyY1AXzmmWfg4eGBzMxMeHn9vgZnzJgx2Lhxo8TInFBQF0ClBiqLgLLzsqMhcmlnCstRUGaCTqNGQqS/7HBcSsn6byHMZnjGx0MfHy87HCJqJqkJ4KZNm7BgwQJ06NCh1vG4uDicOXOmyddbvHgxOnbsCL1ejz59+mDnzp2Net5//vMfqFQqjBo1qsmv2Wq0eqBdJ/vtvMNyYyFycTXtX3p08IenB/vSNUXRGvv0b8DdjRv902q1mD17NmbPns2t4IiciNQ+gEajsdbIX42CggJ4eno26VorV67E9OnTsXjxYvTv3x8ffPABkpKScPjwYURHRzf4vDNnzuC5557DgAEDmhx/qwvuBhSesK8DvIYLr4maKxHCQ/MAACAASURBVLU6AWT7l6apPHwYVUeOQKXVwu/PdzTqOTqdDq+++qpjAyOiJpM6AnjLLbfg008/Ve6rVCrYbDb8/e9/x6BBg5p0rbfeegsTJ07EY489hvj4eCxcuBBRUVF47733GnyO1WrFuHHj8Nprr6FTp07N/jlaTU0hCCuBia7KvuoE8HomgE1StGYtAMDn9tvhEch/OyJXJnUE8O9//zsGDhyI1NRUmEwmPP/88zh06BAuXLiA3bt3N/o6JpMJaWlpmDlzZq3jQ4cORUpKSoPPe/311xEcHIyJEyc2arq4qqoKVVVVyv2SkpJGx9gialrBsBcgUbMVV5hxLK8UACuAm8JmMqHkm28ANK34w2az4cgR+3tWfHw81GqnaD5B5Pak/iZ2794dBw8exI033oghQ4bAaDTi7rvvxv79+3HNNdc0+joFBQWwWq0IDQ2tdTw0NBS5ubn1Pmf37t1YunQpPvzww0a/zvz58+Hv7698RUVFNfq5LeLSVjCsBCZqln2ZFyEEENveC0E+TVtq4s7Ktm6FtbgYHqGh8O7Xr9HPq6ioQEJCAhISEpRWX0Qkn/S9gMPCwvDaa6+1yLUub+YqhKi3wWtpaSkefPBBfPjhhwgKavy+urNmzUJycrJyv6SkpHWTwHbXAGoPwFQKFJ8FAlo5ASVqA/Zx/99mqen95z9qFFQaFs4QuTqpCeDBgwfrPa5SqaDX6xEdHd2oYpCgoCBoNJo6o315eXl1RgUB4OTJk8jIyMCdd96pHLPZbAAADw8PpKen1zsC6enp2eTilBbloQPadwbyj9q/mAASNVlNA2j2/2s8c24ujLt2AQAC7h4tORoiaglSE8BevXopI3Q1u39cOmKn1WoxZswYfPDBB9Dr9Q1eR6fToU+fPti8eTNGj/79zWnz5s0YOXJknfO7deuGX3/9tdaxl156CaWlpVi0aFHrT+02RUi8PfnLOwLEDZEdDZFLsVhtOJBVBIDr/5qi+Kt1gBAw9O0DXUyM7HCIqAVIXQO4du1axMXFYcmSJfjll19w4MABLFmyBF27dsWKFSuwdOlSbN26FS+99NIfXis5ORkfffQRPv74Yxw5cgTPPPMMMjMz8fjjjwMAJkyYgFmzZgEA9Hq9sial5isgIAC+vr5ISEiAzpm3NgqubrzKQhCiJjuSU4oKsxV+eg90DvaRHY5LEEJc0vvvHsnREFFLkToCOHfuXCxatAjDhg1Tjl133XXo0KEDXn75Zfz888/w9vbGs88+izfffPOK1xozZgwKCwvx+uuvIycnBwkJCdiwYQNiqj+tZmZmto3qs5DqBJCtYIiaLPXMBQD29i9qdd31wVRXRWoqzJmZUHt5wW/YUNnhEFELkZoA/vrrr0qCdqmYmBhlirZXr17KHsF/ZMqUKZgyZUq9j23fvv2Kz12+fHmjXkM6JQFMB2w2oC0ktUStJI0NoJuspvefb9JwqL29JUdDRC1FavbQrVs3/PWvf4XJZFKOmc1m/PWvf0W3bvaed9nZ2fUWcritwI6ARgeYy4Gipm+XR+TO0lgB3CTWMiNKqvdlD7inedO/Wq0Wzz33HJ577jluBUfkRKSOAL777ru466670KFDB1x33XVQqVQ4ePAgrFYr1q9fDwA4depUg6N6bknjAQR1Bc7/ai8GaddRdkRELuFcUQVyiiuhUavQM8pfdjguoXTjdxAVFdDFxsLQu3ezrqHT6fD3v/+9hSMjoqslNQHs168fMjIy8Pnnn+PYsf9v777DoyrTxo9/ZyaZ9AwJyaQREkLvCCiCFFfpIM2GKOqiropY0V14V1+x4iIiiOIiimB5xQb8VkGU0FGUvkJCCZBCSe9tkinn98fAwFADKWcmc3+uay7COWfm3E+ek5w7TzuHURSFO+64gwkTJhAUFATAxIkT1QzRNRnb2RPAnGRoO0ztaIRwC2ee/9sxOhh/vepLoLqFM92/hnHjLrqmqhDCfan+WzAwMNAxU1fU0JlxgDkH1Y1DCDfieP5vcxn/VxNVx1Kp3L0btFoMF1lOq6ZsNhsZGRkANG/evHFMxhOiEVA9AQRITk4mIyPDaSwgwKhRo1SKyMXJUjBCXJX0/HLWHcwGZAHomipeYW/9C+zXD+8I4zV/TmVlJS1a2IeqlJWVESATSYRwCaomgMeOHWPs2LHs27cPjUZzwWLQVqtVzfBcl9E+QYa8w2CzglYeyyTExVisNj7Zmsq7iYcxmW008femT8uaP/7RUykWC8UrVwL27l8hROOjagL49NNP06JFCxITE0lISGD79u3k5+fXaN0/j9YkHrz8wFIJBakQ1krtiJyYzFZyS6vUDsNjGYN98PGSPwqSThUz7ft97DtZDMBNrZry5tjOhAa48ELvLqJs61YsubnoQkII+svNaocjhKgHqiaA27ZtY/369YSHh6PVatFqtfTt25eZM2fy1FNPsWfPHjXDc11aLYS3hcy99okgLpIAVlRbWPJbGv/eeJQSk0XtcDxWaICeyTe35L4b4/D19rxE0GS2Mm9dCh9tPobVphDs68WLIztwZ49mMpGhhorPTP4YdRsaV34ykhDimqmaAFqtVgID7Y9jCgsL49SpU7Rt25a4uDgOHTqkZmiuz9jBngDmHgTUHStZbbHx1fYM5q8/Ql6ZveVPr9PKGtUqsNmgoLya11cd4JOtqTx9a2vu6NEML51nVMbvx/KZvnwfqXnlAIzoHMXLozpgDLr0s8SFM0tBAaUbNgDS/StEY6ZqAtipUyf+/PNPEhIS6NWrF7NmzUKv1/PRRx+RkJCgZmiu78w4wJxk1UKw2hRW7DnJ3MTDnCisBCA21I/nBrVhVNcYdPKorQZnttr4ftcJ5q1LIbPYxLTl+1i4+RjPDmrDyM5RjfbxZyUmMzNXH+Sr7fbZphHBPrw2uhODO0aqHJn7KfnhBzCb8e3YEd+2bdUORwhRT1RNAF988UXKy+1/qb/++uuMHDmSfv360bRpU77++ms1Q3N94eotBaMoCj8nZTH7l8McySkDwBjkw5O3tubunrHovTyjtckVeeu0jL+hOWOui+HLPzL4YMMRUvPKeeqrPXy48SgvDGnDX9oaG1VX6C9JWbz0//aTXWJvfZ7QqznThrUj2FeeOnG1FEWh6PvlABjGjVU5GiFEfVI1ARwyZIjj64SEBJKTkykoKCAkJKRR3aDqxZm1APNTwFINXvU/TkdRFLak5PH2z4ccA+ub+Hvz+ICW3N87Hj+95403c1W+3joe6tuCu6+PZfHWVBZtPsaBzBImLdlJj7gQXhjSlhsTmqodZq3klJqY8Z8kVu/LAqBFWAAzx3V2+3KpyZSUTNXhw2j0egwjRtTJZ3p5eTme5uTl5RIrjwkhAI1yZu2VBmaxWPD19WXv3r106tRJjRBqraSkBIPBQHFxMcHBwQ17ckWBmbFQXQqTfz+bENaTXekFzFpziD9SCwDw1+t4uG8LHu6fIC0tbqCwvJp/bz7Kkl/TqLLYAOjXOoy/D2lH52bu9Vg0RVH4dtcJ3lh1gOJKMzqthr/1T+DpW1t75KSXupT16qsU/t9XBA8fTsycd9QOR4h6o+r920Wo9ueYl5cXcXFxstbftdJo7OMAT+ywLwhdTwlg8qkSZv9yiPUHcwDQe2mZeGMcj9/ckrBAn3o5p6h7IQF6pg9rz6SbWjB/fQrLth9nS0oeW1K2MqxTJFMHt6GVMUjtMK8oI7+C6Sv+5Ncj+QB0ignmrXFd6BTjXkmsK7JVVVH84ypAJn8I4QlUHwM4ffp0vvjiC0JDQ9UMxT2Fn5MA1rHUvHLmrD3MD/89BYBOq+Guns148pbWRDfxq/PziYYREezL62M687d+LZmbeJgVe0/y0/4sfk7KYux1zXhmYGtiQ/3VDvMCFquNT39N4521hzCZbfh4aXluUBse6tvCY2Y417fSxERsJSV4RUUR0PvGOvtcRVHIy8sD7Ks9yPAeIVyDqgnge++9x5EjR4iOjiYuLu6CRwTt3r1bpcjcxJlWv9y6SwBPFVUyf30K3+w8gdVmHx1wW9donh3YmoTwwDo7j1BX86b+zLm7G48OaMmctYf4OSmb73ef4D//PcmEG5rzxC2tXGbplAOZJfzj+z/584R93GnvhKbMHNeZ+DB5pFhdKj49+aPJ2DFodHXXlV5RUYHRaH+UnDwKTgjXoWoCOGbMGDVP7/6MdTcTOL+sigUbj/L57+lUnx4jdks7I1MHt6FjtHSvNVZtI4NYOLEne48XMfvnQ2w9ksfSbel8s/MED94Uz2P9W2LwV2eMp8lsZf76FBZuOobFphDk68WLI9pzV89YaUWqY+ZTpyjftg0Aw1iZ/SuEJ1A1AXz55ZfVPL37O7MUTMFRMJvA++pbbEpMZj7efIxPtqZSXm0fj3lDi1D+PqQtPeOlW95TdIttwhcP9+K3I3nM+vkQe48X8eHGo3zxezqPDWjJg33iCfBpuF8X21MLmLb8T47l2peJGtoxkldHd8QY7Bqtko1N0cqVoCj433AD+thYtcMRQjQA1efkFxUV8d1333H06FFeeOEFQkND2b17NxEREcTExKgdnmsLigTfJmAqsi8HE9m5xm+trLby2bY0Ptx0lKIKMwCdYwy8MKQt/VrLOB1P1adVGCtaNiXxQA6zfz7EoexS3v75EJ/+msoTf2nFhF7N6/U5w6UmM2/9dJAv/7Av6Bwe5MNrozsytFNUvZ3T0ymKQskPPwLS+ieEJ1E1Afzzzz8ZOHAgBoOBtLQ0HnnkEUJDQ1mxYgXp6el89tlnaobn+jQaezdwxjb7RJAaJoDrDmQzffk+ckrtC+e2DA/g+cFtGdopUhI/gUajYVCHCG5tZ+SHP08xZ+1h0vMreOWHZD7ekkqnmPpbMuG/x4vJKjEBMP76WKYPb4/BT5YZqk9Vh1OoTk1Fo9cTNGig2uEIIRqIqgngc889x4MPPsisWbMICjq7BMWwYcOYMGGCipG5kfB2ZxPAGjiaW8aU/9tDpdlKTBM/nh3UhjHdomUmpbiAVqthdLcYhneO4tudJ5i37jAniyo5WVRZr+eNa+rPzHGd6dMyrF7PI+xK1vwEQEDfvugCZaKXEJ5C1QRwx44dLFy48ILtMTExZGVlqRCRGzJ2sP+be+WJINUWG88s20ul2Uqflk359K/X12t3nmgcvHVaJvRqzrjuMfyclEWpyVJv5wr08WJIx0h5qkwDURSF0jU/AxA8bKjK0QghGpKqCaCvry8lJSUXbD906BDh4eEqROSGjO3s/+YkX/HQd9baH+HWxN+bOXd1k+RPXBVfbx2ju8m43Mak6vBhR/dv4F/+Ui/n8PLy4oEHHnB8LYRwDar+NI4ePZpXX32Vb775BrCPPcrIyGDatGncfvvtaobmPs7MBC5Mh+oK0F98Ed/fjuTx0eZjALw1rguRBplNKYSnK1mzBqjf7l8fHx+WLFlSL58thLh2qg78mj17Nrm5uRiNRiorKxkwYACtWrUiKCiIN954Q83Q3EdgOPiHAQrkHbroIYXl1Tz7zV4UBe65IZahnSIbNkYhhMuR7l8hPJuqLYDBwcFs3bqV9evXs3v3bmw2G927d2fgQJmJdlWM7SFti30iSPR1TrsURWHa8j/JLqkiITyAl0Z2UClIIYQraYjuX7D/DqqoqADA399fVhoQwkWomgCmpaURHx/PLbfcwi233KJmKO7t3ATwPMt2HOfnpGy8dRreG38d/noZgyOEOKf7t1+/ep39W1FRQeDpz5dHwQnhOlTtAk5ISKBv374sXLiQgoICNUNxb+FnJoI4J4BHc8t49Qf75JAXhrSlU4w80k0Icbr79yd7Ahg8dIjK0Qgh1KBqArhz50569+7N66+/TnR0NKNHj+bbb7+lqqpKzbDcz5lnAp+zFEy1xcbTy+zr/fVtFcbDfRNUCk4I4WqqDh+mOi2t3rt/hRCuS9UEsHv37rz99ttkZGTw008/YTQaefTRRzEajUyaNEnN0NzLmRbA4uNgsi+r884vh9h/soQQf2/euasrWq2MuxFC2JX8dHrx53ru/hVCuC6XePyDRqPhL3/5C4sWLSIxMZGEhASWLl2qdljuwz8UAk/P7M09xNaUPBaeXvLlX7d3ISJYlnwRQtg5zf6V7l8hPJZLJIDHjx9n1qxZdOvWjeuvv56AgADef/99tcNyL6e7gctO7OO5b/YCMKFXcwZ3lCVfhBBnSfevEAJUngX80Ucf8eWXX/Lrr7/Stm1b7r33XlauXEl8fLyaYbknY3s4toFt27aSU3o7LcMDeGmELPkihHAm3b9CCFA5AXzttdcYP3488+bNo1u3bmqG4v5OjwP0KTyMt07DvPHXyfNUhRBOnLt/G2bxZ51Oxx133OH4WgjhGlRNADMyMi65KOjevXslKbwKx73jiAXaaE/w90HtZMkXIcQFqg4davDuX19fX7799tsGOZcQouZUHQN4fvJXXFzMggUL6N69Oz169FApKvdTZbHyzHr70jmRmkIe6hGickRCCFfkWPy5fz90gbIgsxCezCUmgaxfv5777ruPqKgo5s+fz/Dhw9m5c6faYbmN2T8fYleWhUzCANDmHbzCO4QQnsZp8ech8uxfITydal3AJ06cYMmSJSxevJjy8nLuuusuzGYz33//PR06yOSFmtqSksuiLakAeEd2gKzNkJMMcb1VjkwI4UqqDh2iOj29wWf/lpeXy6PghHBBqrQADh8+nA4dOpCcnMz8+fM5deoU8+fPVyMUt1ZQXs3Ub/4LwH03Nicsoat9R660AAohnEn3rxDiXKq0AP7yyy889dRTPP7447Ru3VqNENyeoij8/bs/ySmtopUxkH8O7wDJp1tOz3smsBDCs0n3rxDifKq0AG7ZsoXS0lJ69uxJr169eP/998nNzVUjFLf15R8ZJB7IRq/T8t6ZJV/OPBJOEkAhxDnU6v4VQrguVRLA3r17s2jRIjIzM3n00UdZtmwZMTEx2Gw21q5dS2lpqRphuY0jOaW8vioZgL8PbUuH6GD7jvC2gAYq8qBMEmohhF3JT9L9K4RwpuosYH9/fyZNmsTWrVvZt28fU6dO5a233sJoNDJq1Cg1Q3NZVRYrT361F5PZRr/WYUy6qcXZnfoACImzf50rrYBCiDOLP5/u/h06TOVohBCuwiWWgQFo27Yts2bN4sSJE3z11Vdqh+Oy3l5ziAOZJYQG6Hnnzq5otectpB1ufyYwOTIRRAgBVQcPnu3+vflmtcMRQrgIVZ8EcjE6nY4xY8YwZswYtUNxOZsP5/LxVvuSL2/f0QVjsO+FBxnbw+GfpAVQCAFAyelHvwUO6K9K969Op2P48OGOr4UQrsHlEkBxcfllVUz91r7ky/2947i1fcTFDzSeaQGUBFAIT3du92+QSrN/fX19WbVqlSrnFkJcmst0AYtLO7PkS25pFW0iAvmf4e0vffC5CaCiNEyAQgiX5Oj+9fGR7l8hhBNJAN3AF7+ns+5gDnovLfPGX4ev92W6UZq2Bo0WTEVQmtVwQQohXI6j+1dm/wohziNdwC7ucHYpr6+yd+dOG9qO9lHBl3+Dty+EJkD+Efs4wOCoBoiy4dgqK8md9x6W/Hy1Q7lm3pERNP3b39AFBakdimjEFEWhZM1PgHrdv2B/FJzRaAQgJydHHgUnhIuQBNCFmcxWnvpqD1UWGwPahPPXm+Jr9kZje3sCmHMQWt5SrzE2tLwFCyhYskTtMGqt9Je1xMx/D982bdQORTRSVQcPYk7PcInu34qKClXPL4S4kCSALmzWmkMczCqlaYCe2Xd2RaPRXPlNYF8K5sAPkJNcvwE2sKpjqeQvWQpA6EOT8AoPVzmia2C1UfDFF1Snp5M2/h6i33id4GGyNpuoe2cWf5buXyHExUgC6KI2Hsph8a+nl3y5swvhQT41f/OZiSC5jWctQEVRyH7zTTCbCRjQH+Pzz9c8IXYxhrFjODl1KhXbfufks89R+d8/MT4/FY2X/DiKuqEoCiU/n579O1Se/SuEuJBMAnFBGYUFTF2+GYAHesdxS7tLLPlyKcZzFoNuJDOBy9avp3zrVjTe3kROn+62yR+AV2gozRctoukjDwNQsGQJGZMecutxjcK1nNv9GySzf4UQFyEJoAt6dtVSqqNfIbTVQpq12M7x0uNX9wGhLUHrBdWlUHyifoJsQDaTiew3ZwIQOmkS+vh4dQOqAxovL4xTpxIzbx5af38qtm8nddztVO7dq3ZoohE42/3bH61MuhBCXIQkgC6oY5wJALN3Ku/teZfhy4dz5w938u///psjhUdQrtSq56W3LwcDjaIbOP/jTzCfPIlXZCRhj/5N7XDqVPCQwcR/+w36hAQs2dmkTbyfwmVfX7mOhbgE5+7fISpHI4RwVZIAuqBX+01n3Z3r+Gevf9Irshc6jY6DBQf5YO8HjP3PWEatHMW83fNIyku6dKJgbGf/180nglSfOEH+okUAREz7B1p/f5Ujqns+LVsS/83XBA0aBGYzWTNmkPnPF7GZTGqHJtxQ1YEDLtX9q9VqGTBgAAMGDECrlVuOEK5Co0hTwzUrKSnBYDBQXFxMcPAV1uerhUJTIRuPb2Rdxjp+O/UbZpvZsS8qIIpbm9/KwLiBdAvvhk57epHoTbNgwxvQdQKM/bDeYqtvx6dMoSxxHf433kjzTxe79di/K1EUhfyPPyb33blgs+HboQPN5r+Hd0yM2qEJN5Iz513yP/qIoEGDaDb/PbXDEcIlNdT925VJAlgLalxAZdVlbDm5hcT0RLac3EKlpdKxr6lvU25pfgsDmw/k+sJsvL97EKKvg79tbJDY6lrZli0cf+Rv4OVFwsoV+LRqpXZIDaL8t984+dxUrEVF6Jo0Ifqd2QTedJPaYQk3oCgKR4cMxZyRQfQ7szGMGKF2SEK4JEkAJQGsFbUvIJPFxG+nfmNdxjo2HN9AaXWpY1+QVwA3F+YwsMpKnycP4Kt3r65TW3U1qbeNojo9ndAHHiBi+jS1Q2pQ5pMnOfHU05iSkkCrJfyZZ2j6yMONugVU1J4pOZnUcbej8fGhzW+/ygQQIS5B7fu3K5AEsBZc6QIyW83syNpBYkYi6zLWUWAqcOzz0/nQt1l/BsUNol9MPwL1gSpGWjN5ixaR+84cdGFhtPxptUc+Ns1WVUXWq69S/P1yAIIGDSRq5kx0ga5ff0Idrtj9W15eTvzpmftpaWnyKDjhElzp/q0WSQBrwVUvIKvNyt7cvST+8DfWaSrIPGeBYW+tN72je9M3pi9GPyPBPsE08WmCwceAwceAj+4qFpyuJ+bsbI4OG45SUYHxzdexDu1HcVWx41VSXYJFsagdZsNQFEJ+3kXUR6vQWqxUxYSRMX08Vc2NakcmXI2i0PrRefhkFZDxwp2U9OusdkQAmCpM3H/d/QB8tuczfP19VY5IuJsOTTvQsWnHOv1MV71/NyRJAGvB5S+g7x9B2fcNyX0nkxgaSWJ6ImklaZd9i5+XH8H6YAw+BkdiGKx3ThINPgYMeoPTMXqd/rKfW22tpriqmKKqInsiV13slNSd+//BnybRZW8xKc20vHifBkW6PWl1UmHqCitNS6FSDwtGaPmjncyoFGfFZynM+tRKtRc89LSOKr1r/NzYqmwkP2pfjaDDwg5ofeS6FVdnctfJPN7t8Tr9TJe/fzcAefZUY2ZshwboWJxHx4Ezeeq6pzhadJS1GWvZn7f/ggTMptiotFRSaakkuyL7qk51JnE8kxACTonduZNVLqd9hkKXvVZswKJB9uRPq9E6PjvYJ5hgfTDeWu+r/Ga4uVj4oWM1gz7+k5iUQqausLFnYCx/jG6FopMbqoBeu1KANI53NtKnZVe1w3EwV5pJxp4A9o/pj7efh/3silqLN8SrHUKjJC2AteDyf0EcXA3L7oGIzvD41ssealNslJnL7F2sVSVOLXVFVUWUVJWcbcGrPntMSXUJNsVWo3DOJHIXa0UM9gnGoAui49RP0KdnYRszmOD/eR6Dr4FA70C0GklyABSLhZw571KweDEA/jfeSMycd/AKDVU5MqGmc2f/xsx5h+Dhw9UOyaG8vJzA0+NWy8rKZAygcAkuf/9uAI2qBXDBggW8/fbbZGZm0rFjR+bOnUu/fv0ueuyiRYv47LPP2L9/PwA9evTgzTff5IYbbmjIkOvXmWcC5x0GqwV0l67uM8lZsD4YrmK+hSNxNJ1t7SuqKkJBoYlPE3uLoN5AsE8wQfqgyyZyBZ99TnZ6FromTWgz7RV0wU1qHoiH0Hh5EfH3F/Dr3IlT/3yRit9/J/X2O2j23jz8OrvGmC/R8EzJyZgz7Is/Bw4YoHY4Qgg30GgSwK+//ppnnnmGBQsWcNNNN7Fw4UKGDRtGcnIyzZs3v+D4jRs3cs8999CnTx98fX2ZNWsWgwcPJikpiZjGsvBukzjw9gdzBRSmQljrOj/FuYljLLHX/DmWvDxy37PPWgx/9ll0TST5u5zgYcPwadWKE08+RXVaGukT7iXif18i5M471Q5NqKB0zc8ABA4YIEu/CCFqpNH0q82ZM4eHHnqIhx9+mPbt2zN37lxiY2P58MOLPwXjyy+/ZPLkyXTr1o127dqxaNEibDYb69ata+DI65FWC+Ft7V/nHFA3livImfMutrIyfDt2pMkdt6sdjlvwad2a+G+/IfDWW1HMZrJe+l8yX3oJW1WV2qGJBmR/9q89AQx2wWf/arVaevbsSc+ePeVRcEK4kEbRAlhdXc2uXbuYNs15seDBgwfz22+/1egzKioqMJvNhF5mLFVVVRVV59xcS0pKri3ghhTeHk7tgdyDwCi1o7moyr17KV5uX+su8qUX0eh0KkfkPnRBQTSb/x75Hy0id948ir79jqLlK+zJfz3QeHkRctddGP/+gtSTi3B0//r6umT3r5+fHzt27FA7DCHEeRrFn2N5eXlYrVYiIiKctkdERJCVlVWjz5g2l+J60gAAIABJREFUbRoxMTEMHDjwksfMnDkTg8HgeMXGXnuXZ4MxtrP/m5OsbhyXoFitZL32OgCGsWPx69ZN5Yjcj0arJeyxR4n96CN0ISFgtYLZXC8vpbKSgqVLOfnMM9hMJrWLLjin+7d/f+n+FULUWKNoATzj/MdkKYpSo0dnzZo1i6+++oqNGzfi63vpRUqnT5/Oc8895/h/SUmJ6yeBxg72f3MOqhvHJRR99z2mpCS0gYEYpz535TeISwrs15dWmzZiLSi48sHXqGLHTjL/538oXZtIxsMPE/vBB+gMhno7n7g8RVEoWbMGgOBhQ1WORgjhThpFAhgWFoZOp7ugtS8nJ+eCVsHzzZ49mzfffJPExES6dOly2WN9fHzw8VH/SRlXJfx0C2B+CliqwevyCzY3JGtREbnvvgtA+FNP4hUWpnJE7k+r16ONjKy3zzfcNhIvo5ETTzxB5c5dpN93H7GLFuFdj+cUl2ZKTsZ8/LjLdv+CfXhNhw72P0STk5Px93ev55IL0Vg1ii5gvV5Pjx49WLt2rdP2tWvX0qdPn0u+7+233+a1115jzZo19OzZs77DVIehGeiDwGaBgqNqR+Mk9733sBYV4dO6NSETJqgdjqihgF43EPflF3gZjVSlHCFt/D1UHTmidlgeqfR061/ggAFoXTSxUhSF9PR00tPTkWVnhXAdjSIBBHjuuef4+OOPWbx4MQcOHODZZ58lIyODxx57DID777+f6dOnO46fNWsWL774IosXLyY+Pp6srCyysrIoKytTqwj1Q6M5Zxyg68wENiUnU7jsawAiXnwRjVejaIz2GL5t2xL/1f+hT0jAkpVF2r33UbF7t9pheRR796/rzv4VQri2RpMA3n333cydO5dXX32Vbt26sXnzZlavXk1cXBwAGRkZZGZmOo5fsGAB1dXV3HHHHURFRTles2fPVqsI9SfctRJARVHsEz9sNoKHDyegVyNafNuDeMfEEPflF/h17YqtuJiMv06itDEto+Ti3KH7VwjhuhpVs8vkyZOZPHnyRfdt3LjR6f9paWn1H5CrODMRJNc1EsCS//yHyj170Pj7Y/z7C2qHI2rBKySE5ks+5eSzz1G2cSMnnnyKyJdfJuTuu9QOrdFzh+5fIYTrajQtgOIyXKgL2FpWRvbb9lbWsMcfk8kDjYDWz49m78/HcMftYLOR9fLL5L7/gYz3qkfS/SuEqC1JAD1B+OlnAhccA7O6a7flvf8B1rw89PHxhD7wgKqxiLqj8fIi6rXXCJv8OAB5779P1sszUCwWlSNrnExJ0v0rhKgdSQA9QVAk+DYBxWZfDkYlVSkpFHz+OQAR//wftHrXWZJG1J5GoyH8qaeIfPl/QaOh6JtvOPG0LBhdH0p/dp/uX41GQ4cOHejQoUON1mUVQjQMSQA9gUYDxtOtgCp1AyuKQtYbb4LVSuCttxLYr58qcYj6F3LPPcTMm4tGr6ds3Toy/joJa1GR2mE1GoqiUPKT+yz+7O/vT1JSEklJSbIGoBAuRBJAT3E6AVRO7ac0MZH8JUuoSk1tsNOX/vwzFb//jkavJ2L6tCu/Qbi14MGDab74E7TBwVTu2UPaffdhPmcWvrh2pqRkzCdO2Lt/+/dXOxwhhJuSBNBDmCqakrU7mJT/Wc6JKU+S89a/ODZsOGn3TKDw62+wlpbW27ltFRVkv/UvAJo+8gj6Zs3q7VzCdfj37EncF5/jFRFB9ZGjpI2/B9Phw2qH5fYc3b833+zy3b9CCNclCWAjZsnPJ3/JEo6NHkPq/35J4eFArBVWdOFh+N94I2i1VO7ZQ9bLL5PStx8npz5P2dZfUazWOo0j76OPsGRl4R0TQ9NHHq7TzxauzbdNG+KXfYW+ZUss2dmk3zeRih071A7LbTl1/7rB7N/SAhPbfjxEbEQCzcIT+Hb2NjZ9dYi9iRmk/plHQWY5FnPd/r4RQtRMo1oHUIBSXU3ppk0Ur1hJ2ebNcHoWpsbbm8DIEpq0qCRg7u9o/A2Yc3Io+eEHilasoPrIUUpWraJk1Sq8IiIwjB6NYcwYfBJa1Cqe6vR0Cj5ZDEDE9GlofX1rXUbhXryjooj/8guOT36Cyt27yXjoYaLfmU3woEFqh+Z23KH7t6zQxNHduRzZlU3WsRKqzJWcyLEPNzl+oICcI5XOb9BAYBMfDEY/DOH+GML9nL729tGpUAohGj+NIot1XbOSkhIMBgPFxcUEBwerFoeiKJiSkyle+f8o+eEHpwH3vl26YBgzGsPw4eg+6gEVefDIBojp7vz+/UkUr1hB8apV2IqLHfv8unXDMHYswcOGoruGMh5/9DHKNm0ioG9fYhd9JLMAPZjNZLK3Mq9bB1otkf/7EiHjx6sdllvJmT2b/I8/IWjoUJrNfVftcBzKi6s4ujuHI7tyyDxy9vcHGgiN9WHCP/sCsDMxBXOphuLcSvsrp4Jq0+VbAP0N+tNJ4enkMNyPJkZ/gsP98PGTNgxxbVzl/q0mSQBrQe0LyJKXR/EPP1K8YgVV54yt8goPxzB6lL0Fr1Wrs29YMhLStsCYD6HbhIt+pq26mrL1GyhesYKyrVvhdHewRq8naOBADGPHEtCnNxrdlf8qL92wgROPTwZvbxL+3/+rdWuicH+KxULWq69R9M03AIRNfpywJ5+UPwxqQFEUjg4ajPnECWLmvkvwUHVnAFeUVDuSvlNHiuCcO0lUKwOtekTQsns4eFkIDAwEoKysjICAAMdxiqJgKjM7ksGi3EqKc04nh7kVVJVffh1J30BvR4uh3leSwUvR++oICvUlMNSXoNMvvYcnz2rfv12BZ18BbkiprqZ048azXbxnEjRvbwIH3kqTsWMJ6NMHjddFqtbY3p4A5iRf8vO1ej3BQ4cQPHTI6S7iHyleuYKqlCOUrF5NyerVeBmN9i7isWPwSUi46OfYqqrIfnMmAE0ffECSPwHYF4yOfGUGXkYjee+/T96CDzHn5BA1Y8bFr1nhYNqfZO/+9fNTrfu3srSao3ty7Unf4ULObT6ITAg+nfQZCQzxcWwvv0wSp9Fo8AvS4xekJzLBcMF+U7mZkrwzSWGFIzksyq2ksqQaU5kZU5mZ7NSSOi2nJ/Dx9yIwxJegpr4EhfgQ2PRschgU6ot/sB6NVv4wa8zkN64bcHTxrlhJyY8/Onfxdu1Ck7FjCR42DJ3hwl+gTsLPPBLuYI3O62000vShSYRO+iumpGSKV6yg5McfseTkkL9oEfmLFuHXtSuGsWMuOH/B4sWYjx/Hy2gk7LHHrrrMovHSaDSET3kCr/Bwsl55heLvvseaX0DMnHfQ+vmpHZ7LUmvxZ1OZmWN7c0nZmc3Jw0UotrNZnzE+mNY9jbTsbiQotO7H9/oGeOMb4I0x7sIWmmqT5XTLoT05tJhtdX7+RkGBqkoLpfkmygpNlOabqKqwnH6VkX+y7KJv0+o0BIb4nE4QfS9IEANDffDylvGZ7ky6gGuhvpqQj+3JZcfqVGLifAnJTUK//hssKWeTNq/wcPu4vjFj8GnZsuYfnL4NPh0Khlh4dv81xWarrqZsw0Z7F/GWLed1Ed+KYexY9PHxHLttFIrJRPTs2RhGjrimc4nGr3TdOk4+NxWlqgq/bt1o9uECvEJC1A7L5Th3/86t9xnApnIzqf/N5cjOHE4cLMR2btIXF0TLHkZadTcSHHblhL28vPySXcBCHdUmC6UFJsoKqigtMNlf5ySI5UVV1CQz8AvyJijUFy99/SaC7XpH0b5PVJ1+pnQBSwJYK/V1Aa2dvZHDR87+Nau1mTGUpBIVbiVhYGdih/VGq/e++g+uLIR/xdu/nnYcfGsXsyU39+wYxJRzHjHn5QUWC/49e9L8889kfBeAzQaZe+HIOshz47XwNBqI7AytBtpblOugbit27+b445OxFRfj3bw5fl271kGgjYtiMlG6di0aPz/a/PZrvbSUVlVaHEnf8QMF2Kxnbw1hsYG06mGkVQ8jhvCra32sqKigQ4cOACQnJ8vTQNyAzWqjrKjKOUEsMFF2OlEsLTBhqW64FtfrR7bghpF1O4xIEkBJAGulvi6gjLfmcnT1LgpC2lEU0RmTNtBpv2+AN83ahRDbPpRm7UJq9Fe4wzvtoDQTHkqE2OvrJF6nLuoffsBaXAw6HS2Wf49v27Z1do6842Uc3pFN6t5cqk2XHxzuEmw2sJrBWm1/0ch+1DRa0OlB523/txbJoGKx2hcjt0k33uVo9Hp0QYFXPvAaVFVYnJK+pjFnk74mEZK0ibMURaGq3OJIDM+9bupDSJQ/TaPr9rqXBFASwFqprwuo6tgxilesxDB2DPoWLSjMquDEwQKOHyjk5OFCzOctm2AI97Mng+1DaNY2BB//y7QOfjYGjm2A296DHg/UWcxn2KqrKd/6K7omTfDvfl2tP68op4KUHdmk7MimMKuiDiIUQlxKSFSAY0xfaJR01YrGSxJASQBrRY0LyGq1kZNawvED9oQwO63EaVC2RmMfmH2mhTAywYDO65wHvqyZDr8vgBsnw9CZDRLz1SovruLIzhwO78gmJ+3s7D6dt5b4zmG0uT4CQ4SLTBYozYKMP+D4Nji+E6rPG1Ad3haa94bmN0JEJ9A1knlXlio4tRcyfoeMbVCY5rzfrwnE9rKXPbYX+Mu4PlfnrdddXW+CEG5MEkBJAGvFFS6g6koLJw8XcvxAIScOFlzQSual1xLdOoTY9vaEMDRrOZofnoSEv8D9K1WJ+WKqKswc3ZNLyo5sTh46u7yERqshtl0IrW+IIKFruPprV5lNkP6rfSzfkUTIO+S83y8UWt1qHyPX8hYINKoTZ0Mryjj7PTm2CarPe7Z0VFf796TVQGh2vb3bWHiEyspK+p9etmbz5s34yUxv4QJc4f6tNkkAa8EVL6DSApOju/jEwQIqS81O+/0DNDSzbiAmOIOmf32PkCh/1RZQtVRbSduXT8qObNL252GznL0UIxMMtLnBvqaYf7BelfgAUBTIP2pPbI4kQtpWsJzzKCuN1p7QtBpoT/yiuoHWw5dGsJrh+Paz37OsP533+wRDwoDTSfKt0CRWnThFg5BZwMIVueL9u6FJAlgLrn4BKTaF/FNl9mTwQAGnUoouulZWYIgPIZH+hEQGEBIV4PjaL8i7zmfw2qw2Thws5PCObI7tzXUazxgaHUCbGyJo3TNC3a6oqlJI3Xy2Raso3Xl/UPTZVr6EAeAn3ZuXVZYDR9efTgjXQWWB8/7wdmdbTONuAm95XnRjIgmgcEWufv9uCJIA1oK7XUAWs5WsYyUcX/ovssuiKLQ0o8J26eTFR1NKqPdJQrxOEuJ14vS/JwnS5aHR1PyyURTINrfmcGVfjlT2ptLWxLEvSJdDa79faeO3labex2tVvjpjs+A0Y1ent49lO9OFaWxfJ8ufeCSb9exyOEcS4cQOUM75o8TbHzqOhe7328cOyvfZ7UkCKFyRu92/64MkgLXgthfQ6hdg+0cAmGwBFFmaUWBpRqGlGYVW+78lViOgvejbvTQmQnRnksKzL4MuE53mbItevjmWFFN/Dpv6UWqNcGz31RTT2u9XWvtuIdL70FUlkw0mJB5aDbInfPF9wad+lt7weJWFcGzj2dbB0syz+8Lb2RPBLuMhoKlqIYrakQRQuCK3vX/XIUkAa8FtLyBFgbJse2vMJVjMNgpzqynMOfOqojCnmqK86ku+TasFQ1M9IUY9xQVm8jOrHPu89RoSOgXRulswzVr5o9O5cMuOTg+B4WpH4XkUxT52cPdnkLQczKcnNOn00G6kfdmi+P72C024DUkAhSty2/t3HZIEsBY88QKyWW2U5JkoyCynMKucwqwKCjPt/5qrnDNDrZeGuI5NaX19BPFdwvCu58cFiUbEVAL7v4NdS+1dxmeExMN1E+G6+yAoUrXwRM1JAihckSfev88nCWAtyAV0lqIolBVWOZJCva8XLbqG4Rsgy32IWsr8rz0R3PctVJ1eF1KjgzZDoPsD9m76xrK+YiNUXl5OfHw8AGlpaZIACpcg929JAGtFLiAhGlB1BSSvtCeDx38/uz0oGq67194yGBKnXnxCCLch929JAGtFLiAhVJJ7yD5WcO//nbOsjAZa/sU+caTtCPBScf1IIYRLk/u3JIC1IheQECqzVMHBVbB7qX028Rn+YdDtHnsXcVhr1cITQrgmuX9LAlgrcgEJ4UIKUmHP57DnSyjLOru9eR/7DOIOo8FbHkPW0CorKxk2bBgAP/30kzwKTrgEuX9LAlgrcgEJ4YKsFkj5xd4qmPLL2YWmfQz2LmKddA03pHKTmcD7lgJQ9sUDBPjKxDBxldrfBh1G1elHyv0bZOqcEKJx0XlBu+H2V8kpe4vgns+gKMM+iUQ0rOpz2hiSloPehdcAFa6pacs6TwCFJIBCiMYsOBoGvAD9pkLqJshJVjsiz1NZBTOn2r8eOAP8fNSMRrijZterHUGjJF3AtSBNyEIIcXmyELRwRXL/vtTDXoUQQgghRKMlCaAQQgghhIeRMYBCCCHqlb+/v9ohCCHOIwmgEEKIehMQEEB5ebnaYQghziNdwEIIIYQQHkYSQCGEEEIIDyMJoBBCiHpjMpkYMWIEI0aMwGQyqR2OEOI0GQMohBCi3litVlavXu34WgjhGqQFUAghhBDCw0gCKIQQQgjhYSQBFEIIIYTwMJIACiGEEEJ4GEkAhRBCCCE8jMwCrgVFUQAoKSlRORIhhHBN5z4FpKSkRGYCC5dw5r595j7uiSQBrIXS0lIAYmNjVY5ECCFcX3R0tNohCOGktLQUg8Ggdhiq0CienP7Wks1m49SpUwQFBaHRaGr9eSUlJcTGxnL8+HGCg4PrIELX5knllbI2Tp5UVvCs8kpZG68z5U1OTqZt27ZotZ45Gk5aAGtBq9XSrFmzOv/c4OBgj/ghPMOTyitlbZw8qazgWeWVsjZeMTExHpv8gUwCEUIIIYTwOJIACiGEEEJ4GN2MGTNmqB2EOEun03HzzTfj5eUZvfOeVF4pa+PkSWUFzyqvlLXx8rTyXoxMAhFCCCGE8DDSBSyEEEII4WEkARRCCCGE8DCSAAohhBBCeBhJAIUQQgghPIwkgC5kwYIFtGjRAl9fX3r06MGWLVvUDqnWZs6cyfXXX09QUBBGo5ExY8Zw6NAhp2NuvvlmNBqN02v8+PEqRXztZsyYcUE5IiMjHfsVRWHGjBlER0fj5+fHzTffTFJSkooRX7v4+PgLyqrRaHjiiScA96/TzZs3c9tttxEdHY1Go2HlypVO+2tSl4WFhUycOBGDwYDBYGDixIkUFRU1ZDFq5HJlNZvN/OMf/6Bz584EBAQQHR3N/fffz6lTp5w+42LXw7Rp0xq6KFd0pXp98MEHLyjHjTfe6HRMVVUVTz75JGFhYQQEBDBq1ChOnDjRkMWosSuV92I/wxqNhrfffttxjLvUbU3uNTWpu4yMDG677TYCAgIICwvjqaeeorq6uiGL0mAkAXQRX3/9Nc888wz//Oc/2bNnD/369WPYsGFkZGSoHVqtbNq0iSeeeILff/+dtWvXYrFYGDx4sNMD4gEeeeQRMjMzHa+FCxeqFHHtdOzY0akc+/btc+ybNWsWc+bM4f3332fHjh1ERkYyaNAgxzOl3cmOHTucyrl27VoA7rzzTscx7lyn5eXldO3alffff/+i+2tSlxMmTGDv3r2sWbOGNWvWsHfvXiZOnNhQRaixy5W1oqKC3bt389JLL7F7926WL1/O4cOHGTVq1AXHvvrqq071/eKLLzZE+FflSvUKMHToUKdyrF692mn/M888w4oVK1i2bBlbt26lrKyMkSNHYrVa6zv8q3al8p5bzszMTBYvXoxGo+H22293Os4d6rYm95or1Z3VamXEiBGUl5ezdetWli1bxvfff8/UqVPVKlb9UoRLuOGGG5THHnvMaVu7du2UadOmqRRR/cjJyVEAZdOmTY5tAwYMUJ5++mkVo6obL7/8stK1a9eL7rPZbEpkZKTy1ltvObaZTCbFYDAo//73vxsqxHrz9NNPKy1btlRsNpuiKI2nThVFUQBlxYoVjv/XpC6Tk5MVQPn9998dx2zbtk0BlIMHDzZc8Ffp/LJezPbt2xVASU9Pd2yLi4tT3n333foOr05drKwPPPCAMnr06Eu+p6ioSPH29laWLVvm2Hby5ElFq9Uqa9asqbdY60JN6nb06NHKLbfc4rTNHetWUS6819Sk7lavXq1otVrl5MmTjmO++uorxcfHRykuLm7YAjQAaQF0AdXV1ezatYvBgwc7bR88eDC//fabSlHVj+LiYgBCQ0Odtn/55ZeEhYXRsWNHnn/+ebdsFQNISUkhOjqaFi1aMH78eI4dOwZAamoqWVlZTnXs4+PDgAED3L6Oq6ur+eKLL5g0aRIajcaxvbHU6flqUpfbtm3DYDDQq1cvxzE33ngjBoPB7eu7uLgYjUZDkyZNnLb/61//omnTpnTr1o033njDbbvNNm7ciNFopE2bNjzyyCPk5OQ49u3atQuz2exU99HR0XTq1Mnt6zU7O5tVq1bx0EMPXbDPHev2/HtNTepu27ZtdOrUiejoaMcxQ4YMoaqqil27djVg9A3Dc5fAdiF5eXlYrVYiIiKctkdERJCVlaVSVHVPURSee+45+vbtS6dOnRzb7733Xlq0aEFkZCT79+9n+vTp/Pe//3V0K7qLXr168dlnn9GmTRuys7N5/fXX6dOnD0lJSY56vFgdp6enqxFunVm5ciVFRUU8+OCDjm2NpU4vpiZ1mZWVhdFovOC9RqPRrX+mTSYT06ZNY8KECQQHBzu2P/3003Tv3p2QkBC2b9/O9OnTSU1N5eOPP1Yx2qs3bNgw7rzzTuLi4khNTeWll17illtuYdeuXfj4+JCVlYVeryckJMTpfY3hd/XSpUsJCgpi3LhxTtvdsW4vdq+pSd1lZWVd8HMdEhKCXq93+/q9GEkAXci5rSdgv4jP3+bOpkyZwp9//snWrVudtj/yyCOOrzt16kTr1q3p2bMnu3fvpnv37g0d5jUbNmyY4+vOnTvTu3dvWrZsydKlSx0DyRtjHX/yyScMGzbM6a/mxlKnl3OlurxYvbpzfZvNZsaPH4/NZmPBggVO+5599lnH1126dCEkJIQ77rjD0XLkLu6++27H1506daJnz57ExcWxatWqCxKjc7lzvZ6xePFi7r33Xnx9fZ22u2PdXupeczGN/ef2cqQL2AWEhYWh0+ku+AsjJyfngr9G3NWTTz7Jf/7zHzZs2ECzZs0ue2z37t3x9vYmJSWlgaKrHwEBAXTu3JmUlBTHbODGVsfp6ekkJiby8MMPX/a4xlKnQI3qMjIykuzs7Avem5ub65b1bTabueuuu0hNTWXt2rVOrX8Xc+YPniNHjjREePUmKiqKuLg4x3UbGRlJdXU1hYWFTse5+8/xli1bOHTo0BV/jsH16/ZS95qa1F1kZOQFP9eFhYWYzWa3rt9LkQTQBej1enr06HFB99jatWvp06ePSlHVDUVRmDJlCsuXL2f9+vW0aNHiiu9JSkrCbDYTFRXVABHWn6qqKg4cOEBUVJSjO/TcOq6urmbTpk1uXceffvopRqORESNGXPa4xlKnQI3qsnfv3hQXF7N9+3bHMX/88QfFxcVuV99nkr+UlBQSExNr1OqzZ88eALev7/z8fI4fP+4oR48ePfD29naq+8zMTPbv3+929XquTz75hB49etC1a9crHuuqdXule01N6q53797s37+fzMxMxzG//PILPj4+9OjRo2EK0pBUmnwizrNs2TLF29tb+eSTT5Tk5GTlmWeeUQICApS0tDS1Q6uVxx9/XDEYDMrGjRuVzMxMx6uiokJRFEU5cuSI8sorryg7duxQUlNTlVWrVint2rVTrrvuOsVisagc/dWZOnWqsnHjRuXYsWPK77//rowcOVIJCgpy1OFbb72lGAwGZfny5cq+ffuUe+65R4mKilJKSkpUjvzaWK1WpXnz5so//vEPp+2NoU5LS0uVPXv2KHv27FEAZc6cOcqePXscM19rUpdDhw5VunTpomzbtk3Ztm2b0rlzZ2XkyJFqFemSLldWs9msjBo1SmnWrJmyd+9ep5/hqqoqRVEU5bfffnO859ixY8rXX3+tREdHK6NGjVK5ZBe6XFlLS0uVqVOnKr/99puSmpqqbNiwQendu7cSExPjVK+PPfaY0qxZMyUxMVHZvXu3cssttyhdu3Z1yWv7StexoihKcXGx4u/vr3z44YcXvN+d6vZK9xpFuXLdWSwWpVOnTsqtt96q7N69W0lMTFSaNWumTJkyRa1i1StJAF3IBx98oMTFxSl6vV7p3r2701Ip7gq46OvTTz9VFEVRMjIylP79+yuhoaGKXq9XWrZsqTz11FNKfn6+uoFfg7vvvluJiopSvL29lejoaGXcuHFKUlKSY7/NZlNefvllJTIyUvHx8VH69++v7Nu3T8WIa+fnn39WAOXQoUNO2xtDnW7YsOGi1+0DDzygKErN6jI/P1+59957laCgICUoKEi59957lcLCQhVKc3mXK2tqauolf4Y3bNigKIqi7Nq1S+nVq5diMBgUX19fpW3btsrLL7+slJeXq1uwi7hcWSsqKpTBgwcr4eHhire3t9K8eXPlgQceUDIyMpw+o7KyUpkyZYoSGhqq+Pn5KSNHjrzgGFdxpetYURRl4cKFip+fn1JUVHTB+92pbq90r1GUmtVdenq6MmLECMXPz08JDQ1VpkyZophMpgYuTcPQKIqi1FPjohBCCCGEcEEyBlAIIYQQwsNIAiiEEEII4WEkARRCCCGE8DCSAAohhBBCeBhJAIUQQgghPIwkgEIIIYQQHkYSQCGEEEIIDyMJoBBCXIX4+Hjmzp2rdhhCCFErkgAKIVzWgw8+yJgxYwC4+eabeeaZZxrs3EuWLKFJkyYXbN+xYwd/+9vfGiwOIYSoD17cBEocAAAFP0lEQVRqByCEEA2puroavV5/ze8PDw+vw2iEEEId0gIohHB5Dz74IJs2bWLevHloNBo0Gg1paWkAJCcnM3z4cAIDA4mIiGDixInk5eU53nvzzTczZcoUnnvuOcLCwhg0aBAAc+bMoXPnzgQEBBAbG8vkyZMpKysDYOPGjfz1r3+luLjYcb4ZM2YAF3YBZ2RkMHr0aAIDAwkODuauu+4iOzvbsX/GjBl069aNzz//nPj4eAwGA+PHj6e0tNRxzHfffUfnzp3x8/OjadOmDBw4kPLy8vr6dgohhCSAQgjXN2/ePHr37s0jjzxCZmYmmZmZxMbGkpmZyYABA+jWrRs7d+5kzZo1ZGdnc9dddzm9f+nSpXh5efHrr7+ycOFCALRaLe+99x779+9n6dKlrF+/nr///e8A9OnTh7lz5xIcHOw43/PPP39BXIqiMGbMGAoKCti0aRNr167l6NGj3H333U7HHT16lJUrV/Ljjz/y448/smnTJt566y0AMjMzueeee5g0aRIHDhxg48aNjBs3DnlMuxCiPkkXsBDC5RkMBvR6Pf7+/kRGRjq2f/jhh3Tv3p0333zTsW3x4sXExsZy+PBh2rRpA0CrVq2YNWuW02eeO56wRYsWvPbaazz++OMsWLAAvV6PwWBAo9E4ne98iYmJ/Pnnn6SmphIbGwvA559/TseOHdmxYwfXX389ADabjSVLlhAUFATAxIkTWbduHW+88QaZmZlYLBbGjRtHXFwcAJ07d67Nt0sIIa5IWgCFEG5r165dbNiwgcDAQMerXbt2gL3V7YyePXte8N4NGzYwaNAgYmJiCAoK4v777yc/P/+qul4PHDhAbGysI/kD6NChA02aNOHAgQOObfHx8Y7kDyAqKoqcnBwAunbtyq233krnzp258847WbRoEYWFhTX/JgghxDWQBFAI4bZsNhu33XYbe/fudXqlpKTQv39/x3EBAQFO70tPT2f48OF06tSJ77//nl27dvHBBx8AYDaba3x+RVHQaDRX3O7t7e20X6PRYLPZANDpdKxdu5affvqJDh06MH/+fNq2bUtqamqN4xBCiKslCaAQwi3o9XqsVqvTtu7du5OUlER8fDytWrVyep2f9J1r586dWCwW3nnnHW688UbatGnDqVOnrni+83Xo0IGMjAyOHz/u2JacnExxcTHt27evcdk0Gg033XQTr7zyCnv27EGv17NixYoav18IIa6WJIBCCLcQHx/PH3/8QVpaGnl5edhsNp544gkKCgq455572L59O8eOHeOXX35h0qRJl03eWrZsicViYf78+Rw7dozPP/+cf//73xecr6ysjHXr1pGXl0dFRcUFnzNw4EC6dOnCvffey+7du9m+fTv3338/AwYMuGi388X88ccfvPnmm+zcuZOMjAyWL19Obm7uVSWQQghxtSQBFEK4heeffx6dTkeHDh0IDw8nIyOD6Ohofv31V6xWK0OGDKFTp048/fTTGAwGtNpL/3rr1q0bc+bM4V//+hedOnXiyy+/ZObMmU7H9OnTh8cee4y7776b8PDwCyaRgL3lbuXKlYSEhNC/f38GDhxIQkICX3/9dY3LFRwczObNmxk+fDht2rThxRdf5J133mHYsGE1/+YIIcRV0iiy1oAQQgghhEeRFkAhhBBCCA8jCaAQQgghhIeRBFAIIYQQwsNIAiiEEEII4WEkARRCCCGE8DCSAAohhBBCeBhJAIUQQgghPIwkgEIIIYQQHkYSQCGEEEIIDyMJoBBCCCGEh5EEUAghhBDCw0gCKIQQQgjhYf4/8tqjN9yqNQkAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![aps_random_maximum_metric.png](attachment:aps_random_maximum_metric.png)"
   ]
  },
  {
   "attachments": {
    "aps_kmeans_maximum_metric.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xd4VFX+x/H3THohCSWNEBJKIKEISJMOgjQLYFeKFda1Iroi6/4EUWFd14K4KAKCKKsoRRGRXgVFRZAOoQdICAkkIb3d3x8js0aKgZQ7k/m8nmceZu7M3PsdmHA/OeeecyyGYRiIiIiIiMuwml2AiIiIiFQuBUARERERF6MAKCIiIuJiFABFREREXIwCoIiIiIiLUQAUERERcTEKgCIiIiIuRgFQRERExMUoAIqIiIi4GAVAERERERejACgiIiLiYhQARURERFyMAqCIiIiIi1EAFBEREXExCoAiIiIiLkYBUERERMTFKACKiIiIuBgFQBEREREXowAoIiIi4mIUAEVERERcjAKgiIiIiItRABQRERFxMQqAIiIiIi5GAVBERETExSgAioiIiLgYBUARERERF6MAKCIiIuJiFABFREREXIwCoIiIiIiLUQAUERERcTEKgCIiIiIuRgFQRERExMUoAIqIiIi4GAVAERERERejACgiIiLiYhQARURERFyMAqCIiIiIi1EAFBEREXExCoAiIiIiLkYBUERERMTFKACKiIiIuBgFQBEREREXowAoIiIi4mIUAEVERERcjAKgiIiIiItRABQRERFxMQqAIiIiIi5GAVBERETExSgAioiIiLgYBUARERERF6MAKCIiIuJiFABFREREXIwCoIiIiIiLUQAUERERcTEKgCIiIiIuRgFQRERExMW4m12AMysuLubkyZNUq1YNi8VidjkiIiJSCoZhcO7cOWrXro3V6pptYQqAZXDy5EkiIyPNLkNERESuQkJCAnXq1DG7DFMoAJZBtWrVANsXKCAgwJQasrKyqF27NmALpH5+fqbUISIi4iwyMjKIjIy0n8ddkQJgGZzv9g0ICDAtALq5udnvBwQEKACKiIiUkitfvuWaHd8iIiIiLkwtgE7O3d2d++67z35fRERE5M8oMTg5Ly8vZs2aZXYZIiIi4kTUBSwiIiLiYtQC6OQMwyA7OxsAX19fl76gVUREREpHLYBOLjs7G39/f/z9/e1BUERERORyFABFREREXIwCoIiIiIiLUQAUERERcTEKgCIiIiIuRgFQRERExMUoAIqIiIi4GM0D6OTc3Ny4/fbb7fdFKkpydjIzd87kbN5Zs0uRCuDn7kef6D60DWur+URFXIDFMAzD7CKcVUZGBoGBgaSnpxMQEGB2OSIV5tfTv/L0mqc5nXPa7FKkgtWtVpfbGt3GgAYDqOlT0+xyRCqEzt8KgGWiL5C4ggXxC3jlh1coKC6gYVBDBjYciAW1EFU1RzKOsOTwErIKsgBwt7pzfeT13N7odtqHt8dq0RVDUnXo/K0AWCb6AklVVlBcwL9+/Bef7fsMgJ51e/Jq51fx8/AzuTKpKNkF2Sw7sox5++exPWW7fXsd/zrc1ug2BjYcSC2fWiZWKFI+dP5WACwTR/gCZWVl4e/vD0BmZiZ+fjo5S9mdyT3DM2uf4edTPwPwWMvHGHHNCLUCuZB9Z/Yxb/88Fh9aTGZBJgDuFne6R3bn9ka306F2B30fxGk5wvnbbAqAZeAIXyAFQClvu1N3M3LNSBKzEvHz8GNi54n0qNvD7LLEJDmFOSw/spx5++ex7fQ2+/YI/whujbmVgQ0HEuIbYmKFIlfOEc7fZlMALANH+AIpAEp5+ubQN4zdNJa8ojyiAqJ4p8c71A+qb3ZZ4iDiz8YzP34+iw4u4lz+OQDcLG50rdOV2xvdTqfanXCzajYCcXyOcP42mwJgGTjCF0gBUMpDUXERb//yNrN2zQKgS0QX/tn1nwR4uuZ/jHJ5uYW5rDi6gnn75/FL8i/27WF+YdwacyuDGg4izC/MxApFLs8Rzt9mUwAsA0f4AikASlml56Xz3Prn2HRyEwAPN3+Yx1s+rpYcKZVDaYeYFz+PRQcXkZ6XDoDVYqVLRBdub3Q7nSM6427VlLPiWBzh/G02BcAycIQvkAKglEX82XieWvMUCecS8HH3YXyn8fSN7mt2WeKE8oryWHl0JfP2z7MPHgII8Q3h1phb6RLRRb9UyFUJ9gku9+tMHeH8bbYqFQCnTJnC66+/TmJiIk2bNuXtt9+mS5cul3x9WloaL7zwAgsWLODs2bPUq1ePN954g/79+5fqeI7wBVIAlKu18uhK/v7d38kpzCHCP4JJPSbRuEZjs8uSKuBw+mEWxC/gqwNfaeUYKbNHWzzKX1v+tVz36Qjnb7NVmXb5uXPnMnLkSKZMmUKnTp2YOnUq/fr1Y/fu3dStW/eC1+fn53PDDTcQEhLCvHnzqFOnDgkJCVSrVs2E6q+em5ubPbBqKTgpjWKjmCnbpjB1+1QA2oe15/Vur1Pdu7rJlUlVUS+wHs+0eYYnWj3B6mOrmR8/nyMZR8wuS5yU5h6tGFWmBbB9+/Zce+21vPfee/ZtcXFxDBw4kIkTJ17w+vfff5/XX3+dvXv34uHhcVXH1G8Q4mwy8zMZs2EMa4+vBWBI3BCeafOMrtESEZei8zdUiVk88/Pz2bJlC7179y6xvXfv3mzatOmi71m0aBEdOnTgscceIzQ0lGbNmjFhwgSKiooqo2SRSnck/Qj3LrmXtcfX4mn15NXOrzK63WiFPxERF1Ql/udPSUmhqKiI0NDQEttDQ0NJSkq66HsOHTrE6tWrGTx4MEuWLCE+Pp7HHnuMwsJCXnzxxYu+Jy8vj7y8PPvjjIyM8vsQIhVo/fH1PL/+ec4VnCPEN4RJPSbRrFYzs8sSERGTVIkWwPMslpIL1BuGccG284qLiwkJCeGDDz6gdevW3H333bzwwgslupD/aOLEiQQGBtpvkZGR5Vr/1cjKysLPzw8/Pz+ysrLMLkccjGEYTN8xncdXPc65gnO0CmnF3JvmKvyJiLi4KhEAa9WqhZub2wWtfcnJyRe0Cp4XHh5Oo0aNSgyciIuLIykpifz8/Iu+Z8yYMaSnp9tvCQkJ5fchyiA7O5vs7GyzyxAHk12QzbPrnmXSL5MwMLij0R3M6D2DWj61zC5NRERMViUCoKenJ61bt2bFihUltq9YsYKOHTte9D2dOnXiwIEDFBcX27ft37+f8PBwPD09L/oeLy8vAgICStxEHNHxc8cZ+u1Qlh9djrvVnf+77v94scOLeLhd3YAnERGpWqpEAAQYNWoU06dP58MPP2TPnj08/fTTHDt2jEceeQSAYcOGMWbMGPvr//rXv5KamspTTz3F/v37+eabb5gwYQKPPfaYWR9BpFxsTtzMPd/cw/6z+6npXZMZvWdwZ+M7zS5LREQcSJUYBAJw1113kZqayvjx40lMTKRZs2YsWbKEqKgoAI4dO4bV+r+8GxkZyfLly3n66ae55ppriIiI4KmnnmL06NFmfQSRMvt076e89uNrFBlFNK3ZlLd7vK01WUVE5AJVZh5AMzjCPEJaCUTOS8hI4MaFN2JgcHP9m3mxw4t4u3ubXZaIiMNxhPO32apMC6CIq1udsBoDgzahbXi186uXHAEvIiKiAOjkrFYr3bp1s98X17U2YS0AvaJ6KfyJiMhlKQA6OR8fH9auXWt2GWKytNw0tiZvBaBbnW4mVyMiIo5OTUYiVcCGExsoMoqIqR5DnWp1zC5HREQcnAKgSBVwvvu3e53uptYhIiLOQQHQQZ3NuvhqJH+UlZVFcHAwwcHBWgrOReUX5fPdie8AuL7u9SZXIyIizkAB0AGt3H2KLv9aw5IdiaV6fUpKCikpKRVclTiqn5J+Irswm2CfYJrUbGJ2OSIi4gQUAB3QpoOpZOYVMurzbew4nm52OeLg1iSsAaBbZDesFv1Ii4jIn9PZwgH9vX8s3RoFk1tQzMOzfyIpPdfsksRBGYZhv/6vR2QPc4sRERGnoQDogNzdrEy+txUxIf6cyshj+OyfyckvMrsscUB7z+zlVPYpfNx9aBfWzuxyRETESSgAOqgAbw9m3NeW6r4e7DiRzjNfbKO4WKv2SUnnu3871u6oZd9ERKTUFAAdWN2avkwd2gYPNwtLdiTx9sr9ZpckDsY+/Utkd1PrEBER56IA6ODa1avBhEHNAXhn9QG+2naixPNWq5U2bdrQpk0bLQXnYpKykthzZg8WLHSt09XsckRExIloKTgncEebSA6czmTqukP8bd52Imv4cm3d6oBtKbiffvrJ5ArFDOdb/1qGtKSGdw1zixEREaeiJiMn8VyfWHrFhZJfWMyI2Vs4kZZjdkliMnX/iojI1VIAdBJuVguT7m5JXHgAKZl5PDTrJ7LyCs0uS0ySmZ/J5qTNgKZ/ERGRK6cA6ET8vNyZfl8bavl7sTfpHE99to1zmVlER0cTHR1Ndna22SVKJdl4ciOFxYVEB0RTL7Ce2eWIiIiTUQB0MhFBPnwwrDWe7lZW7jnFmyv2cvToUY4ePYphaJoYV6HuXxERKQsFQCd0bd3qvH77NQDM2HDE3GKk0hUWF7L++HpAAVBERK6OAqCTGtAygievb2h2GWKCrclbycjPIMgriBbBLcwuR0REnJACoBMb2asRfZqG2h8npOoaQFdwfvWPrnW64m7VTE4iInLlFACdmNVqYeKt19gfPzpnCxm5BSZWJBXNMAz79X8a/SsiIldLAdDJ+Xi62e8fOJ3FE//dSmFRsYkVSUU6lH6IhHMJeFg96Fi7o9nliIiIk1IAdHIWi4UmTZrQoFEs3p5W1u0/zatL9phdllSQ892/7cPb4+vha3I1IiLirBQAnZyvry+7du3iwL49vD34OgBmbjzCnM1HTa5MKoK6f0VEpDwoAFYh/ZuH82zvRgCM/WoXmw6kmFyRlKeUnBS2n94OQLc63UyuRkREnJkCYBXzWI+GDGxZm8Jig7/O+YVDpzPNLknKyfrj6zEwaFqzKaF+oX/+BhERkUtQAHRy2dnZNG3alKZNm5KdnY3FYuGft11Dq7pBpOcU8PBHP5OerZHBVcH56/80+bOIiJSVAqCTMwyD3bt3s3v3bvtScN4ebnwwtA0RQT4cSsni0f9uoUAjg51aTmEOP5z8AdD1fyIiUnYKgFVUcDUvpt/XBl9PNzYeSGXcol1aK9iJbU7cTG5RLrX9atOoeiOzyxERESenAFiFxYUH8M7drbBYYM7mY3y06YjZJclV+n33r8ViMbkaERFxdgqAVVyvJqGM6RcLwPjFu1m7L9nkiuRKFRvF9ulfdP2fiIiUBwVAFzC8S33uaF2HYgOe+O9W4k+dM7skuQI7UnZwJvcM/h7+tAltY3Y5IiJSBSgAugCLxcKrg5rTrl4NzuUV8tBHP3MmK9/ssqSUzrf+dY7ojIebh7nFiIhIlaAA6OQsFgtRUVFERUVd9towT3cr7w9pTd0avhw7k81fPv5ZLYFOQt2/IiJS3iyGhoZetYyMDAIDA0lPTycgIMDsckol/tQ5bp2yiXN5hQDEhlXjpmvCuema2kTX8jO5OvmjhIwE+i/sj7vFnbV3rSXQK9DskkREnJ4znr/Lm7vZBUjligmtxscPt2fyqnjWx59mb9I59iad49/L99M8IpCbrgnnxmvCqVPd1+xShf+N/m0d2lrhT0REyo0CoAtqGRnEjPvbkp5dwLLdSXz960k2HUxlx4l0dpxIZ+K3e2lVN4ibr6nNjdeEExrgbXbJLmvt8bWAun9FRKR8qQu4DByhCTknJ4euXbsCsH79enx8fK5qP6mZeSzdZQuDmw+f4fy3wmKBttE1uPmacPo1D6eWv1d5lS5/Ij0vnW5zu1FkFLHk1iVEVos0uyQRkSrBEc7fZlMALANH+AJlZWXh7+8PQGZmJn5+Zb+OLzkjlyU7Evl6eyJbjp61b7daoGODWtx0TTh9m4UR5OtZ5mPJpS0+tJgxG8bQMKghCwcsNLscEZEqwxHO32ZTF7BcICTAm/s71eP+TvU4kZbDku2JfL39JNuPp/PdgRS+O5DCP77cSZeYWtx0TW1uaBpKgLemJylva47Zrv/T2r8iIlLeFADlsiKCfBjetT7Du9bnaGoWi7cnsnh7InsSM1iz7zRr9p3Gc6GV7o2CualFbXrFheDrqa9VWeUX5bPx5EZAAVBERMqfuoDLwBGakCuiC7g0DiRnsnj7Sb7+9SQHT2fZt3t7WOkZF0rrutWxasnaq3YkeyvzT47Fz606f4meicWiKTvLQw1/L7rFBBPoqxZrEVfmCOdvsykAloEjfIHMCoDnGYbB3qRzLN5+ksXbEzmaml2px6+qvEK/wrPG9+SfbUde0q1ml1OluFstdGhQkz5Nw+jdJJQQjXIXcTmOcP42mwJgGTjCF8jsAPh7hmGw40Q63+xI5MTZHNPqcHaGYbC5cBR5nKGZ29PUtLY0u6QqwQAOnMpk3+9WwLFY4Nq61enTNJQ+TcOIqqnJ0EVcgSOcv81megAsKCggKSmJ7OxsgoODqVGjhpnlXBFH+AJlZWURHR0NwJEjR0wNgFI+9p7Zyx1f34GPuw/r71qPt7taqMrT4ZQslu1KYtmuJLYeSyvxXGxYNfo2C6NP0zBiw6pddnlFEXFejnD+NpspV+tnZmYyZ84cPv30U3788Ufy8vLsz9WpU4fevXszYsQI2rZta0Z5TsXPz4/Tp0+bXYaUo/OjfzuEd1D4qwD1avnxSLcGPNKtAUnpuazYncTSXUn8cOiMfWWct1fGE1XTlz5NbWGwVWQQVl3UKiJVSKW3AL711lu8+uqrREdHc8stt9CuXTsiIiLw8fHhzJkz7Ny5kw0bNrBw4UKuu+46Jk+eTExMTGWWWGr6DUIqwp1f38meM3sY33E8g2IGmV2Oy0jLzmflnmSW7Upi/f7T5BUW258LqeZF79+6ia+rXxMPNw3KEXFmOn+bEADvuOMOXnzxRZo3b37Z1+Xl5TFjxgw8PT15+OGHK6m6K6MvkJS3pKwkbph3AxYsrLlzDTV9appdkkvKzi9k3b7TLN2VxOo9yZzLK7Q/F+DtTq+4UPo0C6NrTDA+nm4mVioiV0Pnbwe4BtCZOcIXKCcnh379+gHw7bffXvVScOIY5u6dyyubX6FlcEs+7v+x2eUIkF9YzPeHUlm6M4kVu5NIycy3P+ftYaV7oxD6NAvl+thQAn00vYyIM3CE87fZHGrG3qNHj5KVlUVsbCxWq7pYSqO4uJh169bZ74tzW3P8t9U/6mryZ0fh6W6lW6NgujUK5pWBzfjl2FmW7bRdN3j8bA5Ld9nuu1sthAZ4U1HjRtysFlrUCaJnXAjdGgVrKUYRKRNTAuBHH33E2bNnGTlypH3biBEjmDFjBgCNGzdm2bJlREZGmlGeiCmyCrL4MfFHALpHdje3GLkoN6uFttE1aBtdgxdujGN3YgbLdiaxbNcp9p06x4m0ip3+6GhqNot+PYmb1ULrqOr0jA2hZ1wIDYL9NWJZRK6IKV3AHTp0YMSIETzwwAMALF26lJtvvplZs2YRFxfH448/TpMmTZg+fXpll3ZFHKEJ2ZHmAZSyWX5kOc+se4aogCi+Hvi1TuhOJuFMNqlZ+X/+wquUnVfIdwdSWLUnucRchgBRNX25PjaEnrGhtKtXA0939aCIXI4jnL/NZkoL4P79+2nTpo398VdffcUtt9zC4MGDAZgwYYI9HIq4irUJawHoXqe7wp8TiqzhS2QN3wo9RseGtXiubywJZ7JZsy+ZlXuS+eFgKkdTs5m58QgzNx7B38udro1q0TM2lO6Ng6np71WhNYmIczIlAObk5JRI3Js2beLBBx+0P65fvz5JSUlmlCZiisLiQtafWA+o+1f+XGQNX4Z1iGZYh2iy8grZEJ/C6r2nWL33NCmZeSzZkcSSHUlYLNAqMoiecaH0jAuhcagmtxYRG1MCYFRUFFu2bCEqKoqUlBR27dpF586d7c8nJSURGBhoRmkiptiWvI30vHSCvIJoGaKl36T0/Lzc6dssjL7NwiguNth+Ip3Ve06xam8yu05m8MuxNH45lsbry/YREeRDz7gQro8N4br6NfH20BQ2Iq7KlAA4bNgwHnvsMXbt2sXq1auJjY2ldevW9uc3bdpEs2bNzCjNKfn6Vmy3k1S8NQm20b9d63TF3epQg/PFiVitFlpGBtEyMohRvRuTmJ7D6r3JrN6TzHcHUjiRlsPs748y+/uj+Hq60blhLXrGhdAjNoSQalp1RsSVmHKmGT16NNnZ2SxYsICwsDC++OKLEs9v3LiRe+6554r3O2XKFF5//XUSExNp2rQpb7/9Nl26dLnoa2fNmnXR6wxzcnLw9nae/wj9/PzIysoyuwwpA8Mw7AFQ3b9SnsIDfRjcPorB7aPIyS9i08EUVv0WCJMyclm++xTLd58CoFGov1oExSHd064u97Sra3YZVY4pAdBqtfLyyy/z8ssvX/T5PwbC0pg7dy4jR45kypQpdOrUialTp9KvXz92795N3boX/+IEBASwb9++EtucKfxJ1XA4/TAJ5xLwsHrQsXZHs8uRKsrH0+23awFDMQYa7DqZwao9yazee4pfj6ez/1Sm2SWKXNT1sblml1AlmdbX9MUXX/Dll19SUFBAr169GDFiRJn29+abb/LQQw/Zl417++23WbZsGe+99x4TJ0686HssFgthYWFlOq5IWZ1v/WsX3g4/D03jIxXPYrHQLCKQZhGBPNUrhuRzuew6mYEWhhJHFF1T/y9WBFMC4AcffMAjjzxCTEwM3t7ezJ8/n8OHD18yqP2Z/Px8tmzZwvPPP19ie+/evdm0adMl35eZmUlUVBRFRUW0bNmSl19+mVatWl3y9Xl5eeTl5dkfZ2RkXFW95Sk3N5fbbrsNgPnz56sF0wmdn/7l+sjrzS1EXFZINW9CGuv/DhFXYspsoZMnT+aFF15g3759/Prrr8yYMYN33333qveXkpJCUVERoaGhJbaHhoZecjqZ2NhYZs2axaJFi/j000/x9vamU6dOxMfHX/I4EydOJDAw0H5zhJVKioqKWLJkCUuWLKGoqMjscuQKpeSk8OvpXwHbABAREZHKYEoAPHToUIkBGEOHDiUvL6/Mc//9cX4rwzAuOefVddddx5AhQ2jRogVdunTh888/p1GjRkyePPmS+x8zZgzp6en2W0JCQpnqFdlwfAMGBk1qNiHMT5cjiIhI5TBtIujzy5cBuLm54eXlRXZ29lXtr1atWri5uV0QIJOTky9oFbwUq9VK27ZtL9sC6OXlhZeXZtWX8qPRvyIiYgbTBoFMnz69RAgsLCxk1qxZ1KpVy77tySefLNW+PD09ad26NStWrGDQoEH27StWrGDAgAGl2odhGGzbto3mzZuX8hOIlE1uYS7fn/wegB6RPUyuRkREXIkpAbBu3bpMmzatxLawsDA+/vhj+2OLxVLqAAgwatQohg4dSps2bejQoQMffPABx44d45FHHgFsk09HRETYB5q89NJLXHfddcTExJCRkcE777zDtm3b+M9//lMOn1Dkz21O3ExuUS7hfuE0rt7Y7HJERMSFmBIAjxw5Uu77vOuuu0hNTWX8+PEkJibSrFkzlixZQlRUFADHjh3Dav3fJY9paWmMGDHCvuxcq1atWL9+Pe3atSv32kQu5vfdv1qfVUREKpPFcMCJn1JTU/n4448ZOXKk2aVcVkZGBoGBgaSnpxMQEGBKDVlZWfau9MzMTPz8NF+SMyg2irn+8+tJzU1l6g1TNQG0iEglcoTzt9lMGQV8MYZhsGzZMu68805q167Nq6++anZJTsHPzw/DMDAMQ+HPiexM2Ulqbip+Hn60DW1rdjkiIuJiTA+AR44c4cUXXyQqKor+/fvj7e3NN998U+YpYUQc2fnJnztHdMbDzcPcYkRExOWYEgDz8vL49NNP6dmzJ3FxcezcuZM333wTq9XK888/T69evXBz06LkUnWdv/5Po39FRMQMpgwCiYiIoEmTJgwZMoR58+ZRvXp1AO655x4zynFqubm5DB06FICPP/5YS8E5gYRzCRxIO4CbxY3OEZ3NLkdERFyQKS2ARUVFWCwWLBaLWvrKqKioiHnz5jFv3jwtBeckznf/tg5tTaBXoLnFiIiISzIlACYmJjJixAg+/fRTwsLCuO2221i4cKGmwhCXcD4AavUPERExiykB0Nvbm8GDB7N69Wp27NhBXFwcTz75JIWFhbz66qusWLFCrVlSJaXnpbPl1BZAAVBERMxj+ijgBg0a8Morr3D06FG++eYb8vLyuOmmm0q9hq+IM/nuxHcUGUU0DGpIZLVIs8sREREXZdpawH9ktVrp168f/fr14/Tp0yWWhRNxdoZh8OvpX5mzZw6g0b8iImIuhwmAvxccHMyoUaMA24lT1waKszqdfZqvD33NwviFHMk4AoC71Z0+0X3MLUxERFxapQfAuLg4/u///o/bb78dT0/PS74uPj6eN998k6ioKJ5//vlKrFCkbAqKClh/fD0LDyy0d/kC+Lj7cEPUDdzd+G4a12hscpUiIuLKKj0A/uc//2H06NE89thj9O7dmzZt2lC7dm28vb05e/Ysu3fv5rvvvmP37t08/vjjPProo5VdolPx9fUlMzPTfl/ME382noUHFvLNoW84k3vGvr1lcEsGxQyiT3Qf/Dy0XJ+IiJjPYhiGYcaBN23axNy5c1m/fj1HjhwhJyeHWrVq0apVK/r06cOQIUMICgoyo7RS02LSkpGfwdLDS1kYv5CdqTvt22v51OLmBjczsOFA6gfWN7FCERH5I52/TbwGsGPHjnTs2NGsw4tctWKjmB+TfmRh/EJWHVtFXlEeAO4Wd7pFdmNQw0F0iuiEu9UhL7EVERFxzEEgUnp5eXn85S9/AWDq1Kl4eXmZXFHVdSLzBIsOLOLLA19yMuukfXvDoIYMajiIG+vfSE2fmiZWKCIiUjqmdQFXBY7QhJyVlYW/vz8AmZmZ+PnpGrPylFuYy6pjq1h4YCGbEzfbt1fzqEa/ev0YFDOIpjWbaqS6iIgTcYTzt9nUAijyB4ZhsCt1FwvjF/Lt4W85V3AGVP8rAAAgAElEQVTO/lz78PYMajiInnV74u3ubWKVIiIiV08BUJzKgbMHmLp9Kmdzz1bYMZJzkjmcftj+uLZfbQY2HMgtDW8hwj+iwo4rIiJSWRQAxSnkF+Uzfcd0pu2YRmFxYYUfz8vNi15RvRjYcCDtwtphtZi+aqKIiEi5MT0AHjx4kJkzZ3Lw4EEmTZpESEgIS5cuJTIykqZNm5pdnjiAbcnbGLtpLIfSDwHQvU53+tXrV2HX3XlaPWkb3pYAT9e8LkRERKo+UwPgunXr6NevH506dWL9+vW8+uqrhISEsH37dqZPn868efPMLE9MlpmfyaRfJjF331wMDGp41+Dv7f9O76jeGnQhIiJSBqb2az3//PO88sorrFixosSycD169OD77783sTIx27qEdQz8aiCf7fsMA4NBDQexaOAi+kT3UfgTEREpI1NbAHfs2MF///vfC7YHBweTmppqQkXOx9fXl+TkZPt9Z5eak8prP77Gt0e+BaCOfx3GdhzLdeHXmVyZiIhI1WFqAAwKCiIxMZF69eqV2L5161YiIjTasjQsFgvBwcFml1FmhmGw6OAiXv/5ddLz0rFarNzX5D7+2vKv+Lj7mF2eiIhIlWJqALz33nsZPXo0X3zxBRaLheLiYjZu3Mizzz7LsGHDzCxNKlHCuQTGfz+eHxJ/ACC2RiwvdXyJJjWbmFyZiIhI1WTqSiAFBQXcf//9fPbZZxiGgbu7O0VFRdx7773MmjULNzc3s0orFUeYSTwvL49Ro0YB8OabbzrVUnCFxYXM2TOHd7e+S25RLl5uXjza8lGGNhmKh9XD7PJERKSKcoTzt9kcYim4Q4cO8csvv1BcXEyrVq2IiYkxu6RScYQvkLMuBbfvzD7GbhrLrtRdALQLa8fYDmOpG1DX5MpERKSqc4Tzt9lM6wIuKCigcePGLF68mCZNmlC/fn2zSpFKlFuYy9TtU5m5cyZFRhHVPKvxtzZ/Y2DDgRrdKyIiUklMC4AeHh7k5eXppO9Cfkr6iZe+f4mjGUcBuCHqBsa0G0Owr/MPYhEREXEmpg4CeeKJJ3jttdeYPn067u6mL0oiFSQjP4M3f36T+fHzAQjxCeHv1/2dnnV7mlyZiIiIazI1dW3evJlVq1axfPlymjdvfsH1awsWLDCpMikvK4+uZMLmCZzOOQ3AnY3uZGTrkVTzrGZyZSIiIq7L9HkAb7vtNjNLkAqSnJ3MhM0TWHVsFQDRAdGM6ziO1qGtTa5MRERETA2AM2fONPPwDuunpJ9Ym7CWhkENaRjUkAZBDfD1cOxVPgzDICkriQNpB9iVuovZu2ZzruAc7hZ3Hmz+ICOuGYGXm/NMUSMiIlKVOcSFd6dPn2bfvn1YLBYaNWpUJVa2KIsfEn9g9u7ZJbZF+EfQIKiBPRQ2DGpIvcB6+Pj4cPjwYQB8fCp+xQzDMEjJSSE+LZ6DaQc5mHaQ+LR4DqUdIrMgs8Rrm9dqzriO42hUvVGF1yUiIiKlZ2oAzMrK4oknnmD27NkUFxcD4ObmxrBhw5g8eXKVWNv2arQObc3guMEcSDvAgbMHSM1N5UTmCU5knmD98fX211ktVur417G3EsYYMTQIakC9gHp4uJV9IuUzuWdsAe+sLewdSDvAgbQDZORnXPT17hZ3ogOjaRjUkPbh7RnUcBBuVseezFtERMQVmToR9F/+8hdWrlzJu+++S6dOnQD47rvvePLJJ7nhhht47733zCqtVCprIsmzuWc5kHagRAg7mHaQtLy0i77ezeJGVECULRQGxdhbDusG1MXdemHmT89Lv2DfB9IOcCb3zEX3b7VYqVutrq0lsrotfDYMbEhUQFS5BE8REZGKpImgTQ6AtWrVYt68eXTv3r3E9jVr1nDnnXdy+vRpcworJTO/QIZhkJqbyp7kPfzzpX9yNvcs0XdFcyTrCOcKzl30PR5WD1sLXWBDqntX53D6YQ6mHSQ5J/mir7dgIcI/gobV/3ctYkxQDNGB0bqeT0REnJYCoMldwNnZ2YSGhl6wPSQkhOzsbBMqch4Wi4VaPrW4tta1LJm5BIAV01bg6+vLqexTF23RyynMIf5sPPFn4y/YX7hf+AUthvUC6zn84BMRERG5cqa2APbs2ZOaNWsye/ZsvL29AcjJyeG+++7jzJkzrFy50qzSSsURfoMo7VrAxUYxiVmJ9mv60vLSqBdYjwZBDWgQ2AB/T//KLFtERMQ0jnD+NpupLYCTJk2ib9++1KlThxYtWmCxWNi2bRve3t4sW7bMzNKqHKvFSoR/BBH+EXSt09XsckRERMREpgbAZs2aER8fzyeffMLevXsxDIO7776bwYMHV8qUJiIiIiKuyPR5AH18fBg+fLjZZYiIiIi4DKuZB584cSIffvjhBds//PBDXnvtNRMqEhEREan6TA2AU6dOJTY29oLtTZs25f333zehIhEREZGqz9Qu4KSkJMLDwy/YHhwcTGJiogkVOR8fHx927txpvy8iIiLyZ0wNgJGRkWzcuJF69eqV2L5x40Zq165tUlXOxWq10rRpU7PLEBERESdiagB8+OGHGTlyJAUFBVx//fUArFq1iueee45nnnnGzNJEREREqixTA+Bzzz3HmTNnePTRR8nPzwfA29ub0aNHM2bMGDNLcxr5+flMmDABgL///e94enqaXJGIiIg4OlNXAjkvMzOTPXv24OPjQ0xMDF5ezrHOrCPMJF7alUBERETExhHO32YzfR5AAH9/f9q2bcvRo0c5ePAgsbGxWK2mDlCWq1VcDPPuh6PfV9wxLFaI7Q+9XgJv1/zBFRERKQtTAuBHH33E2bNnGTlypH3biBEjmDFjBgCNGzdm2bJlREZGmlGelMXRjbD7q4o/zs8fQvxKGPgfqKel7URERK6EKQHw/fffZ8SIEfbHS5cuZebMmcyePZu4uDgef/xxXnrpJaZPn25GeVIW2+bY/mx+B3R+umKOkX4clvwN0o7CRzdD+0eg51jw9K2Y44mIiFQxpgTA/fv306ZNG/vjr776iltuuYXBgwcDMGHCBB544AEzSpOyyM2AXV/a7rcbAaEVND1NaFOI6gTL/wFbZsLm9+HAShg0Feq0+fP3i4iIuDhTLrTLyckpcdHlpk2b6Nr1f9149evXJykpyYzSpCx2LYTCHKjVCOq0rdhjefnDzW/D4PlQLRxSD8CMG2DVeCjMr9hji4iIODlTAmBUVBRbtmwBICUlhV27dtG5c2f780lJSQQGBppRmpTF1k9sf7YcDBZL5Rwzphc8+j00vxOMYtjwBkzrAUk7K+f4IiIiTsiULuBhw4bx2GOPsWvXLlavXk1sbCytW7e2P79p0yaaNWtmRmlOx9vbmx9//NF+3zSn98PxH8HiBi3urtxj+1SH26ZB3E2w+Gk4tRM+6A49xkDHp8DNIQa7i4iIOAxTzoyjR48mOzubBQsWEBYWxhdffFHi+Y0bN3LPPfeYUZrTcXNzo23bCu5uLY1tv7X+xdwA1cLMqaHJAKjbAb4eCfu+sXUH7/sWBr4PtRqaU5OIiIgDMqUL2Gq18vLLL7N161a+/fZb4uLiSjz/xRdf8NBDD13xfqdMmUK9evXw9vamdevWbNiwoVTv++yzz7BYLAwcOPCKjylAUSH8+pntfqsh5tbiHwJ3z7GFPq8AOP4TvN8ZfnjfNkehiIiImBMAK8LcuXMZOXIkL7zwAlu3bqVLly7069ePY8eOXfZ9R48e5dlnn6VLly6VVGn5ys/P5/XXX+f111+3L6dX6Q6shMxT4FsTYvqYU8PvWSzQ8h7btYH1u9sGpiwdDbNvgbTLfx9ERERcgUMsBVce2rdvz7XXXst7771n3xYXF8fAgQOZOHHiRd9TVFREt27deOCBB9iwYQNpaWl8+eWXpT6mIywl4xBLwc0dAnu+huseg74TKv/4l1NcDD/PgBUvQkE2eFaDvhNtLZWVNVBFREQciiOcv81WJVoA8/Pz2bJlC7179y6xvXfv3mzatOmS7xs/fjzBwcGl7m7Oy8sjIyOjxM3lZaXYrrMDaDXY3FouxmqFdsPhke8gsj3kn4NFj8Ond8M5TTUkIiKuqUoEwJSUFIqKiggNDS2xPTQ09JLzCW7cuJEZM2Ywbdq0Uh9n4sSJBAYG2m9aqg7Y/jkUF0LtVhU38XN5qNkAHvjWtn6wmyfsXwpTroOdC8yuTEREpNJViQB4nuUPXXqGYVywDeDcuXMMGTKEadOmUatWrVLvf8yYMaSnp9tvCQkJZa7ZqRlGybn/HJ3VDTqPhBHrIOwayDkL8x6AeQ9C9hmzqxMREak0pk6QVlRUxKxZs1i1ahXJyckU/2GU5urVq0u1n1q1auHm5nZBa19ycvIFrYIABw8e5MiRI9x88832beeP7e7uzr59+2jQoMEF7/Py8sLLy6tUNbmExG2QvAvcvKD57WZXU3qhTeDhVbD+ddvE0Tvnw5Hv4JbJ0MgBBrGIiIhUMFMD4FNPPcWsWbO48cYbadas2UVb60rD09OT1q1bs2LFCgYNGmTfvmLFCgYMGHDB62NjY9mxY0eJbf/4xz84d+4ckyZNUtduaZ1v/Yu7yTYZszNx94TrX4DGfWHhI5CyH/57J7QaCn0mgLdrXhQsIiKuwdQA+Nlnn/H555/Tv3//Mu9r1KhRDB06lDZt2tChQwc++OADjh07xiOPPALYVh+JiIhg4sSJeHt7X7DSSFBQEIBWICmtglzY8dsE3mbP/VcWEa3hL+th9Svw/X9g68dwYBWExFbcMf2C4bpHoXbLijuGiIjIZZgaAD09PWnYsHxWaLjrrrtITU1l/PjxJCYm0qxZM5YsWUJUVBQAx44dw2qtUpc8Arbl39asWWO/X2n2LobcdAioA/W6Vd5xK4KHD/R5FRr3gy//apsr8NzJij3m9rkQexP0+LtjD54REZEqydR5AN944w0OHTrEu+++e9Xdv2Zy6XmEPh4EB1dD1+dsXalVRV6mbWLrwrwKOoBha2Hc8YXtPhZoOgi6j4HgRhV0TKlKioqKKCgoMLsMEYfm4eGBm5vbJZ936fP3b0wNgIMGDWLNmjXUqFGDpk2b4uHhUeL5BQsce4oOl/0CpSXA280BA57cBjXqmV2R80neC2snwu7fJh63WKH5ndDtOduUNSJ/YBgGSUlJpKWlmV2KiFMICgoiLCzsog1MLnv+/h1Tu4CDgoJKDNqQK1dQUMAHH3wAwIgRIy4I0RXi188AA6K7KPxdrZBYuPMjSNoBaybCvm9g+2e2lsGW99qCYFBds6sUB3I+/IWEhODr6+uUvSYilcEwDLKzs0lOTgYgPDzc5IocU5VZCs4MjvAbRKUvBVdcDJNbwdkjMPB925q7UnYnfoE1E+DACttjqwdcOwy6PgsBtc2tTUxXVFTE/v37CQkJoWbNmmaXI+IUUlNTSU5OplGjRhd0BzvC+dtsDjEq4vTp03z33Xds3LiR06dPm12OXM6xTbbw51kNmtxidjVVR8S1MGQePLQC6neH4gLbGsaTWsK3z8O5U2ZXKCY6f82fr6+vyZWIOI/zPy+6ZvbiTA2AWVlZPPjgg4SHh9O1a1e6dOlC7dq1eeihh8jOzjazNLmUrXNsfzYbBJ4V3NroiiLbwbCv4P5voG5HKMqDze/BpBaw/B+2tZfFZanbV6T09PNyeaYGwFGjRrFu3Tq+/vpr0tLSSEtL46uvvmLdunU888wzZpYmF5N37n+DFloNNbeWqi66MzywBIZ+CXXaQmEObJpsC4KrxtuWsROpwo4cOYLFYmHbtm1ml3IBR67tj8aNG0fLlo4352j37t0ZOXKk2WW4NFMD4Pz585kxYwb9+vUjICCAgIAA+vfvz7Rp05g3b56ZpcnF7FoIBdlQM8YWSqRiWSzQoIetW/jeLyC8BeRn2pave/saWPtP21yMIvKnHCm0VWb4efbZZ1m1apX98f3338/AgQMr5dgAa9euxWKxXDB6fcGCBbz88suVVodcyNQAmJ2dfdG1ekNCQtQF7IjOL/3WaogtnEjlsFigUW8YsQ7umgMhTSEvwzaNzNvX2AJhXqbZVYqIA/L396+QgUP5+fllen+NGjWoVq1aOVUjV8PUANihQwfGjh1Lbm6ufVtOTg4vvfQSHTp0MLEyuUBKPCRsBosbtLjb7Gpck8ViW3f5ke/g9plQqxHkptm6hCe1sHURF+SYXaWI3bx582jevDk+Pj7UrFmTXr16kZWVZX9+5syZxMXF4e3tTWxsLFOmTLns/nbv3k3//v3x9/cnNDSUoUOHkpLyv+tii4uLee2112jYsCFeXl7UrVuXV199FYB69WxTVrVq1QqLxUL37t1LXcePP/5Iq1at8Pb2pk2bNmzduvVPP/uUKVOIiYnB29ub0NBQbr/9dsDWArdu3TomTZqExWLBYrFw5MgRANatW0e7du3w8vIiPDyc559/nsLCQvs+u3fvzuOPP87jjz9OUFAQNWvW5B//+AeXm8zj913A48aN46OPPuKrr76yH3vt2rUAnDhxgrvuuovq1atTs2ZNBgwYYK/rfN0DBw5k4sSJ1K5dm0aNbBPXf/LJJ7Rp04Zq1aoRFhbGvffea59+5ciRI/To0QOA6tWrY7FYuP/+++2f5fetoGfPnmXYsGFUr14dX19f+vXrR3x8vP35WbNmERQUxLJly4iLi8Pf35++ffuSmJj4p/8WcnGmzgM4adIk+vbtS506dWjRooW9ed7b25tly5aZWZrT8PLyYvHixfb7FeZ861/MDVAtrOKOI3/OaoVmt0KTAbBjHqz7J5w5ZBsksmkyNO4P1kvPgO/Q3L0h7maIbK9WZieXmJjIPffcw7/+9S8GDRrEuXPn2LBhgz2sTJs2jbFjx/Luu+/SqlUrtm7dyvDhw/Hz8+O+++676P66devG8OHDefPNN8nJyWH06NHceeedrF69GoAxY8Ywbdo03nrrLTp37kxiYiJ79+4FbCGuXbt2rFy5kqZNm+Lp6VmqOrKysrjpppu4/vrr+eSTTzh8+DBPPfXUZT/7zz//zJNPPsnHH39Mx44dOXPmDBs2bABs5739+/fTrFkzxo8fD0BwcDAnTpygf//+3H///cyePZu9e/cyfPhwvL29GTdunH3fH330EQ899BCbN2/m559/ZsSIEURFRTF8+PA//Td59tln2bNnDxkZGcycOROwtcRlZ2fTo0cPunTpwvr163F3d+eVV16hb9++bN++3f53tWrVKgICAlixYoX93zE/P5+XX36Zxo0bk5yczNNPP83999/PkiVLiIyMZP78+dx2223s27ePgIAAfHx8Llrb/fffT3x8PIsWLSIgIIDRo0fTv39/du/ebZ/fNjs7m3//+998/PHHWK1WhgwZwrPPPsucOXP+9LPLhUwNgM2aNSM+Pp5PPvmEvXv3YhgGd999N4MHD77kl0RKcnd358Ybb6zYgxQV/jb5M9BycMUeS0rP6gYt7oJmt8Gvn8K6f0H6Mdgy0+zKyub7dyE4FlrfD9fcBb41zK7IYf2+Ne2P3NzcSqwPfrnXWq3WEv/nXuq1VzLPaGJiIoWFhdx66632NdmbN29uf/7ll1/mjTfe4NZbbwVsLXS7d+9m6tSpFw2A7733Htdeey0TJkywb/vwww+JjIxk//79hIeHM2nSJN599137+xs0aEDnzp0BW8gCqFmzJmFh//sl9s/qmDNnDkVFRXz44Yf4+vrStGlTjh8/zl//+tdLfvZjx47h5+fHTTfdRLVq1YiKiqJVq1YABAYG4unpia+vb4k6pkyZQmRkpH1p1NjYWE6ePMno0aN58cUX7WvZR0ZG8tZbb2GxWGjcuDE7duzgrbfeKlUA9Pf3x8fHh7y8vBLH/uSTT7BarUyfPt0+cnbmzJkEBQWxdu1aevfuDdj+/adPn24PhAAPPvig/X79+vV55513aNeuHZmZmfj7+1Ojhu3nNyQkhKCgoIvWdT74bdy4kY4dOwIwZ84cIiMj+fLLL7njjjsA23Qu77//Pg0a2FZLevzxx+0hWq6cqQEQwMfHp1RfXDHRwVWQmQS+NaFRX7OrkT9yc4drh9rC0s75tnkanVXaUdj1JZzeC0ufhxVjoelAWxis20Gtgn9wfhL4i+nfvz/ffPON/fHlrq3u1q2bvSsQIDo6ukTX6nlXsm5AixYt6NmzJ82bN6dPnz707t2b22+/nerVq3P69GkSEhJ46KGHSvz/X1hYSGBg4EX3t2XLFtasWXPRz3zw4EHS0tLIy8ujZ8+epa6xNHXs2bOHFi1alJiD8c8uUbrhhhuIioqifv369O3bl759+zJo0KDLzuO4Z88eOnToUGLqkk6dOpGZmcnx48epW9e2MtB1111X4jUdOnTgjTfeoKio6LJr317Oli1bOHDgwAXX5OXm5nLw4EH74+bNm5cIfwBbt25l3LhxbNu2jTNnzlBcXAzYQnCTJk1Kdfw9e/bg7u5O+/bt7dtq1qxJ48aN2bNnj32br6+vPfyBbYWP893NcuUqPQAuWrSIfv364eHhwaJFiy772ltu0UTDf6agoMDe/D148OCKWQrufPfvNXeBu+flXyvmcfesGiuz9HsNtn8OWz6CUztg+1zbrVYjuPY+aHEP+Gk1DEfn5ubGihUr2LRpE8uXL2fy5Mm88MILbN682R6Epk2bVuKkf/59F1NcXMzNN9/Ma6+9dsFz4eHhHDp06IprPB9WLlfH1SyWVa1aNX755RfWrl3L8uXLefHFFxk3bhw//fTTJVvBDMO4YN6688eu6PnsiouLad269UW7Us+3nMKFLcBZWVn07t2b3r1788knnxAcHMyxY8fo06fPFQ0SudTf8R//Tv54frNYLFf17yM2lR4ABw4cSFJSEiEhIZcdim6xWCgqKqrEypxTfn4+DzzwAAB33HFH+QfArFTY963tfqsh5btvkYvxDoR2w6Htw3DyF9gyC3bMh5T9sPwFWPUSxN1iaxWM7uzSrYKZmZce/f3HIHW5lpLz3Yvn/f7i/7KwWCx06tSJTp068eKLLxIVFcXChQsZNWoUERERHDp0iMGDS3dZybXXXsv8+fOJjo7G3f3CU1dMTAw+Pj6sWrWKhx9++ILnz7dc/f68Ehoa+qd1NGnShI8//picnBx7N/kPP/zwp/W6u7vTq1cvevXqxdixYwkKCmL16tXceuuteHp6XnB+a9KkCfPnzy8RejZt2kS1atWIiIiwv+6Px/7hhx+IiYkpdevfxY597bXXMnfuXEJCQq5oWbS9e/eSkpLCP//5TyIjIwHb9Y9/PB5w2fN5kyZNKCwsZPPmzfYu4NTUVPbv309cXFyp65ErU+mjgIuLiwkJCbHfv9RN4c9B7PjctixZeEsIbWp2NeJKLBaIaA23TIZn9sJNb9nmQizKh53z4KObYHJr2PiOy66Q4ufnd8nb76//+7PX/vGa60u97kps3ryZCRMm8PPPP3Ps2DEWLFjA6dOn7Sf0cePGMXHiRPugiB07djBz5kzefPPNi+7vscce48yZM9xzzz38+OOPHDp0iOXLl/Pggw9SVFSEt7c3o0eP5rnnnmP27NkcPHiQH374gRkzZgC2LnAfHx+WLl3KqVOnSE9PL1Ud9957L1arlYceeojdu3ezZMkS/v3vf1/2sy9evJh33nmHbdu2cfToUWbPnk1xcTGNGzcGbF3smzdv5siRI6SkpFBcXMyjjz5KQkICTzzxBHv37uWrr75i7NixjBo1qkRAT0hIYNSoUezbt49PP/2UyZMn/+mglN+Ljo5m+/bt7Nu3j5SUFAoKChg8eDC1atViwIABbNiwgcOHD7Nu3Tqeeuopjh8/fsl91a1bF09PTyZPnsyhQ4dYtGjRBXP7RUVFYbFYWLx4MadPn77oLy0xMTEMGDCA4cOH89133/Hrr78yZMgQIiIiGDBgQKk/m1whw8GcPXvW7BJKLT093QCM9PR002rIzMw0AAMwMjMzy3fnxcWGMaWjYYwNMIzNH5TvvkWu1olfDGPRU4bxam3bd3NsgGG8VNMwPr/PMA6uMYyiIpMLLH85OTnG7t27jZycHLNLKbXdu3cbffr0MYKDgw0vLy+jUaNGxuTJk0u8Zs6cOUbLli0NT09Po3r16kbXrl2NBQsWGIZhGIcPHzYAY+vWrfbX79+/3xg0aJARFBRk+Pj4GLGxscbIkSON4uJiwzAMo6ioyHjllVeMqKgow8PDw6hbt64xYcIE+/unTZtmREZGGlar1ejWrVup6jAMw/j++++NFi1aGJ6enkbLli2N+fPnX1Db723YsMHo1q2bUb16dcPHx8e45pprjLlz59qf37dvn3HdddcZPj4+BmAcPnzYMAzDWLt2rdG2bVvD09PTCAsLM0aPHm0UFBTY39etWzfj0UcfNR555BEjICDAqF69uvH888/bP//FjB071mjRooX9cXJysnHDDTcY/v7+BmCsWbPGMAzDSExMNIYNG2bUqlXL8PLyMurXr28MHz7cfn677777jAEDBlyw///+979GdHS04eXlZXTo0MFYtGjRBX8348ePN8LCwgyLxWLcd9999s/y1FNP2V9z5swZY+jQoUZgYKDh4+Nj9OnTx9i/f7/9+ZkzZxqBgYEljr1w4ULjcjHmcj83jnD+NpvFMMzrQH/ttdeIjo7mrrvuAmxdmPPnzyc8PJwlS5bQokULs0orlYyMDAIDA0lPT7+iZvPylJWVZb8oOjMz84p/S7+sk9vgg27g5gXP7gOf6uW3b5Gyysu0DXrZMsvWVXxe9XrQ+j7biHX/ENPKK0+5ubkcPnyYevXqXdCyJ66je/futGzZkrffftvsUpzC5X5uHOH8bTZTJ4KeOnWq/bqBFStWsHLlSpYuXUq/fv3429/+ZmZpAv8b/BF3k8KfOB4vf1vQG7EG/rIe2jwEntXg7GFYOQ7ejIO5Q+HAKvjtYn8REbExdRqYxMREewBcvHgxd955J0zuEVQAACAASURBVL179yY6OvqCEVlSyQpyYccXtvua+08cXXgLuOlN6P2ybc3qLbPg+E+wZ5HtFhQF1w6D2BvBzQlHsucXQVEBFOZB4Z+//KpYrM75dyMiV8XUAFi9enUSEhKIjIxk6dKlvPLKK4Bt6LcGgZhs3ze2ZcYC6kD97mZXI1I6nn620eqthkDSTvjlI/h1rm1+wdUv227OyD8SOr0BZwrBvQJHPbt7g3eQbSS2h49Lj7B2RL+fq1GkrEwNgLfeeiv33nsvMTExpKam0q9fPwC2bdtGw4YNzSzNaXh5efH555/b75ebrb/NB9XyHuddVkxcW1gz6P869HoJdn8Jv8yGU7vMrurqePjbWuhwq7hQZhRBYa5t0vfMJFtr4Pkw6OmnMChSxZgaAN966y2io6NJSEjgX//6l30wQ2JiIo8++qiZpTkNd3d3+zI55Sb9OBy0ra1Jy3vLd98ilc3T1/Y9dubvcm4uHD4MIfWgogaBFBdCboat5T/3nG26naxk283qbguC3kG2ay8tpl4+LiLlwNQA6OHhwbPPPnvB9pEjR5pQjeMoTE0l8cWxhI55Hs86dSq/gF8/BQyI6gw16lf+8UXkoip00garu23dZd8aUFwEeed+C4MZtnCYnWq7WdzAO+C3MFhNPQTisEyc5MQpaCk4B5Q0bhyZq1ZRcOwoUZ9+ittl1vssLCxk4cKFAAwaNOiiM+RfEcP4X/evVv4QcQjnV/jJzs6+YNLmCmF1+//27jwuymr/A/jnYRl2RhZZZVNzC5WAMiyVW4oBbm1qebWyLE1T0+5NX+rVNMO0rK6GabfFbNHuL/VWrrimYqmAoWJmigwqKKCyDLLNnN8fXOc6gQoKnFk+79drXj2eOfM838MZer6c53nOAZxa1b6EvnbKnYorQEVxbTJ49XLtCwrg4A44qQEHde261EQm4tra182yRKoFaPF5AG1sbAxLwf15+aHrmcNScM01j1B1fj7OPDkUNQUFcOnTG0HJyVBusMxPk88DeGYf8HlC7XQar52ovfeHiKTLy8vDlStX4OPjA2dn52ZfH7ZeQgDV5bUJYWVJ7SpB17N3qR0VdHDlE8UkjRAC5eXluHjxIlq1agV/f/86dTgPoIQRQP1183HpOTdXvez9/NAm+UPk/HUktLt/wsWFi+A7fVrLHPza3H/hjzL5IzIhfn5+AG6+pm/LsgN0eqD6au1LVwWg4H9v2zrUPkls7wTYcgSGWl6rVq0MvzdUF8frTZRT164IWJCEc69OwaWVK6Fq1xYeQ4c270ErS2uflgSACF7+JTIliqLA398fPj4+qK6uvvUHWtplDZC9Czi1C7iQafyeZ/vap7IV3i9ItyG0FxD2YKM+Ym9vD9sbXDmjWlITwIkTJ6J9+/aYOHGiUfnSpUvxxx9/WP1yN+7x8ag8fRqFS5Yif+48qIJD4HJ/M06QfWxd7eUdr7uAoPua7zhEdNtsbW1N88Tm36H21fNFoCSvdi7R4z8A2XuAslxAs1N2hGSunJyAzn1lR2FxpK4FHBgYiO+//x5RUVFG5enp6Rg0aBDOnj0rKbKGaYl7CIQQOD/1NZRs3AgbtRpha1ZDFRpqeL9J7wH8pD+Q+zPQdw7w4Kt3FDcREQCg/BJwcitwOUd2JGSuQh8AQhs3AngrvAdQ8ghgUVER1Gp1nXJ3d3cUFhZKiMj0KIoC/7fmo+rcWVT8monccS8jdPU3sK3n53ZHCk/WJn+KLdD9qabdNxFZL2dPoPtw2VEQ0Z9Inc2zffv22Lx5c53yTZs2oW1bzj93jY2jI4KWLoWdvz+qsrNx7tVXIZr6HqDD/536pX1fwI03zRIREVkyqSOAU6ZMwYQJE1BQUICHHnoIALB9+3a8++67Vn//35/ZtW6NoOQPcWbEX6FN3Y8LSUnw+8c/oFKp8NlnnwEAVKrbnHZBVwMc/qZ2m3P/ERERWTyp9wACwLJlyzB//nycP38eABAaGoo5c+Zg1KhRMsNqEBn3EJRu24azr0wEhIDvrJnwHDHiznf6+1bg6ycBZy9gym+AHefvIiIiy8V7ACVfAgaAcePG4ezZs7hw4QJKSkpw+vRps0j+ZHHr2xetp9Q+oHHhrSSU7d135zvNWFX7327DmPwRERFZAekJYE1NDbZt24a1a9ca1u07f/48ysrKJEdmurxeeAHqIUMAnQ45kydj/b/+hQ0bNqCmpqbxO9MWASc21W5HNMFoIhEREZk8qfcA5uTk4JFHHoFGo0FlZSX69esHNzc3LFy4EBUVFfjoo49khmeyFEWB39w3UJWbi6KDB/HomDEAaqeBafRawEe+rV3OyT+idqJWIiIisnhSRwAnTZqE6OhoXL582WiB80cffRTbt2+XGJnps1Gp0GbJP2EfEGAoE1VVjduJEP9b+o0PfxAREVkNqQng3r17MXPmzDpPr4aEhODcuXOSojIfdp6eaPPB/56Wzk9KQqOe6cn7FbhwtHbNzvDHmyFCIiIiMkVSE0C9Xg+dTlen/OzZs3Bzc5MQkflxaN/esF28/j+49PnKhn/42tx/nRJrJ2slIiIiqyA1AezXr5/RfH+KoqCsrAyzZ89GQkKCxMjM18WFC1G6swFrblZXAJnf1m7z8i8REZFVkZoAvvfee9i9eze6dOmCiooKPP300wgNDcW5c+fw9ttvywzNLLV67DHgv2sHV5z4/eaVT2wEKq4A7oFA29iWCI+IiIhMhNSngAMCAnD48GGsXr0aaWlp0Ov1eP755zFixAijh0KoYXynvY6i/HyU//wzzo4bh9B/fws7L6/6K197+CPiacDGtuWCJCIiIumkrQRSXV2NF198EbNmzTLbdX9NYSbx6upqrFixAgDw4osvwkarxZlhw1GVkwOne+5B8OefwcbBwfhDxWeB98IBCGBiBuBpnj9/IiKi22EK52/ZpF0Ctre3x7p162Qd3mLY29tj/PjxGD9+POzt7WHbqhXaLFsGG3d3XM3IQP4//lH3yeBfvwEggJAHmfwRERFZIan3AD766KNYv369zBAskkPbMLR5/z3A1hbF//keRSs+/t+bVy8DP/93gu17uPIHERGRNZJ6D2D79u0xb948pKamIioqCi4uLkbvT5w4UVJk5kOn02HPnj0AgF69esHWtvZ+PpeePeE3ayby57yBgvfegyosFO5xccD2uUB5IdC6ExD+hMTIiYiISBZp9wACQFhY2A3fUxQFp0+fbsFoGs8U7iHQarVwdXUFULsU3J+T6Pw35+Pyl19CcXJC6OIZcNz9PAABPLsBCH1QQsRERERymcL5WzapI4DZ2dkyD28VfKe9jqozZ6Dduxe5f/sHQh9WYN9jGJM/IiIiKybtHsDS0lKkpKRg06ZNKCwslBWGxVPs7BD43mKoAjxRU6bH2X2toe89U3ZYREREJJGUBDAzMxOdOnVC//79kZiYiPbt22Pbtm0yQrEKtihHUI9c2Kp0qCi0xfn5ixu3ZjARERFZFCkJ4LRp0xAcHIy9e/fi0KFD6NOnDyZMmCAjFOuwdQZUDsVoM8QbsLND6abNKFz6oeyoiIiISBIp9wAeOnQIGzduRHR0NADg008/hY+PD8rKygwPNFATOb0bOPJvAAqcx34I/+6nkTdjJgo//BDVZ3Ph89prsGvdWnaURERE1IKkjAAWFhYiODjY8G8vLy84OzujoKBARjiWq6YS2DC1dvveF4CAe9Dq8cfhPWECoCgo/s/3OBWfgEsrV0LU1MiNlYiIiFqMlBFARVFQWloKR0dHAIAQwlBWUlJiqGetj2Y3hr29PRYuXGjYNpK6BCg6Cbj4AA/978GP1hPGw7V3L+TPnYeKo0dxIWkBrvzfd/CdNRMu993XkuETERGRBFLmAbSxsYGiKEZl15LA67d1Ol1Lh9YoJj2P0OUzwIc9gJoK4LGPgW5D61QROh2ufPcdCha/B92VKwAA98RE+Pz977D39WnhgImIiFqGSZ+/W4iUEcCdO3fKOKz1EALY9Hpt8hfaC+j6ZL3VFFtbeAwdCrd+/VDwwQe4suZblGzYgLKdO+E9fjw8R42E8udRRSIiIjJ7UlcCMXem8BeETqdDeno6ACAyMrJ2KbjfNgCrnwZs7IFx+4DWHRu0r6tHjyF/3lxU/JoJAFC1awe/mTPgEhPTbPETERG1NFM4f8smbSLo5pCcnIywsDA4OjoiKirKsEZufdauXYvo6Gi0atUKLi4uiIiIwKpVq1ow2qZRUVGB++67D/fddx8qKiqAKm3t6B8A9HylwckfADiF343Qb76B//z5sPX0RNWpU9A8NxpnJ7+K6ry8ZmoBERERtTSLSQDXrFmDyZMnY8aMGcjIyECvXr0QHx8PjUZTb31PT0/MmDED+/fvR2ZmJp577jk899xz2LJlSwtH3sR2LwSKcwF1MND7b43+uGJjg1aPP4Z2mzbCY8QIwMYGpZs341RCIgpXfAx9VVUzBE1EREQtyWIuAffo0QORkZFYtmyZoaxz584YMmQIkpKSGrSPyMhIJCYmYt68eQ2qbwpDyFqt1jB3YtnpNLis6gfoa4CnVgMd4+94/xW//Yb8eW/ialoaAEAVEgLfmTPh2otrCRMRkXkyhfO3bBYxAlhVVYW0tDTExcUZlcfFxSE1NfWWnxdCYPv27Thx4gR69+59w3qVlZUoKSkxepmULdNrk7+OiU2S/AGAY6dOCPlyFQLeXgBbb29U5eQgd8wY5E6YgKqz55rkGERERNSyTCIB/OOPP7BlyxZcvXoVABq9Tm1hYSF0Oh18fX2Nyn19fZGfn3/DzxUXF8PV1RUqlQqJiYlYsmQJ+vXrd8P6SUlJUKvVhldQUFCj4mx2mv2AnRMQv6BJd6soCtSDB6Pd5k3wfOYZwNYWZdu243RiIgqSk6GvrGzS4xEREVHzkpoAFhUVoW/fvujQoQMSEhKQ998HDV544QVMnTq10fu72dyC9XFzc8Phw4dx8OBBzJ8/H1OmTMGuXbtuWH/69OkoLi42vHJzcxsdY7Pr83egVfCt690GW1dX+E6fhrB1a+F8770QlZUo/OcSnB44CKU3+bkRERGRaZGaAL766quws7ODRqOBs7OzoXzYsGHYvHlzg/fj7e0NW1vbOqN9Fy9erDMqeD0bGxu0b98eERERmDp1Kp544omb3i/o4OAAd3d3o5dJ8boLiJnQ7Idx7NABwV+sRMC778DOxwfVGg3Ojh2H3LHjUGWKSTEREREZkTIR9DVbt27Fli1b0KZNG6Pyu+66Czk5OQ3ej0qlQlRUFFJSUvDoo48aylNSUjB48OAG70cIgUozu5xpf+EIZvdxACBgP2ARYKdqkeMqigJ1YiJc+8SicFkyLq38AmW7dkGbmgqvF16A5+jnoKhaJpamptjYQLGT+qtBRETUrKSe5bRardHI3zWFhYVwcHBo1L6mTJmCkSNHIjo6GjExMVixYgU0Gg3Gjh0LABg1ahQCAwMNI3xJSUmIjo5Gu3btUFVVhY0bN+KLL74weorY5Ol1UKX8HXNiHYBuw4GOD7V4CLauLvD929/Q6vHHceHNN6FN3Y/C5GQUJie3eCxNxsYGLvffD/dBA+HWtx9sXV1kR0RERNSkpCaAvXv3xhdffGGYdkVRFOj1eixatAh/+ctfGrWvYcOGoaioCHPnzkVeXh7Cw8OxceNGhISEAAA0Gg1sbP53xVur1eLll1/G2bNn4eTkhE6dOuHLL7/EsGHDmq6Bze3Qp0Der4CDGohr2NQ1zcWhbVsEffIJSrdsxcWFC1F9/rzUeO6IXg9taiq0qanId3wDbg8/DPeBA+D6wANcGo+IiCyC1HkAs7KyEBsbi6ioKOzYsQODBg3CsWPHcOnSJezbtw/t2rWTFVqDSJ1HqPQCsDQa+opiHO88Fej6BDp37myU5Moi9HrotVrZYdy2msJClGzahJLvf0DVmTOGclsPD7gnJEA9cAAcu3e/6QNGRERkujgPoAlMBJ2fn49ly5YhLS0Ner0ekZGRGD9+PPz9/WWG1SBSv0DfjQGOfAutVze4TtwLACgrK4OLCy9XNhUhBCqOHkXxDz+gZMNG6IqKDO/ZBwdDPXAg1AMHQBUaKi9IIiJqNCaAJpAAmjNpX6DTu4EvBgFQoB2xAa4dalflYALYfERNDbT796P4hx9QmrIN4r9zVgKAY7duUA8YAPfEBNh5eUmMkoiIGoIJoOQEMDMzs95yRVHg6OiI4ODgRj8M0pKkfIFqqoBlPYGik8C9Y6CNfeN/S8ExAWwReq0WpTt2oPiHH6DdlwrodLVv2NrC5YGeUA8cCLeHH4ZNPQ84ERGRfEwAJSeANjY2hvuoroVx/X1V9vb2GDZsGJYvXw5HR0cpMd6MlC/QnneB7XMBFx9gwkFo9fZMACWqKSpCycZNKP7hB1Rc9weN4uwMt74PQz1wIFxiYjitDBGRCWECKHki6HXr1uGuu+7CihUr8Ouvv+Lw4cNYsWIFOnbsiK+//hqffPIJduzYgZkzZ8oM03RcPgPsXlS73X8+4NRKajgE2Hl5wXPkXxH27Rq027wJ3uPHwz44GKK8HCXf/4DcMS/iZJ9Y5L/1Fq4eOdLoZQ6JiIiag9QRwPvuuw/z5s1D//79jcq3bNmCWbNm4cCBA1i/fj2mTp2KU6dOSYryxlr8L4ivhwO/bwJCewHP/AAoCrRaLUcATYwQAhW//oriH35EycaN0F2+bHhPFRoK1z59zHaSbCKilubSMwYuMTFNuk+OAEqeB/DIkSOGefquFxISgiNHjgAAIiIiDGsEW7XfNtQmfzb2QOK7AKcgMVmKosApIgJOERHwnfY6yvbtQ8kPP6J0+3ZUnTmDS9dNLUNERDenODg0eQJIkhPATp06YcGCBVixYgVU/x0Rqa6uxoIFC9CpUycAwLlz5266nq9VqNICm16v3e75CtC6o+Ete3t7vPbaa4ZtMi2KvT3cYmPhFhsLXZkWZdu3oeL4bwAvBRMRNYhT9+6yQ7BIUi8Bp6amYtCgQbCxsUG3bt2gKAoyMzOh0+nw448/4v7778eqVauQn5+Pv/3tb7LCvKEWG0LeNgfY+x6gDgbG/wKo+HQpERHR7eIlYBOYB7CsrAxffvklfv/9dwgh0KlTJzz99NNwc3OTGVaDtMgX6OJvwEcPAPoa4KnVQMf45jkOERGRlWACKPkSMAC4urpi7NixssMwTUIAG6bWJn8dE+pN/vR6PTQaDQAgODjYJJaCIyIiItMmPQEEatcE1mg0qKqqMiofNGiQpIhMROYaIGcvYOcEPLKg3ipXr15FWFgYAD4FTERERA0jNQE8ffo0Hn30URw5cgSKotSZDFp3bYUFa3T1MrD1v/Mf9vk74FH3aWkiIiKi2yH1euGkSZMQFhaGCxcuwNnZGceOHcNPP/2E6Oho7Nq1S2Zo8m2fB2gLAO+OQMwE2dEQERGRBZE6Arh//37s2LEDrVu3ho2NDWxsbPDggw8iKSkJEydOREZGhszw5DmXBhz6tHY78V3AjpMGExERUdOROgKo0+kMq1h4e3vj/PnzAGongj5x4oTM0ORKXQpAAN2GAWG9ZEdDREREFkbqCGB4eDgyMzPRtm1b9OjRAwsXLoRKpcKKFSvQtm1bmaHJ9ehHgF84EPFX2ZEQERGRBZKaAM6cORNarRYA8Oabb2LAgAHo1asXvLy8sGbNGpmhyWXnAPSaKjsKIiIislBSE8D+/fsbttu2bYusrCxcunQJHh4ehieB6ebs7Ozw8ssvG7aJiIiIbkXaSiA1NTVwdHTE4cOHER4eLiOEO8aZxImIiMwPz98SHwKxs7NDSEiIdc/1R0RERCSB1KeAZ86cienTp+PSpUsywzBrQggUFBSgoKAAkpd1JiIiIjMh9aaxf/7zn/jjjz8QEBCAkJCQOsuYpaenS4rMfJSXl8PHxwcAl4IjIiKihpGaAA4ZMkTm4YmIiIisktQEcPbs2TIPT0RERGSVpN4DCABXrlzBv/71L6N7AdPT03Hu3DnJkRERERFZJqkjgJmZmejbty/UajXOnDmDMWPGwNPTE+vWrUNOTg6++OILmeERERERWSSpI4BTpkzBs88+i5MnT8LR0dFQHh8fj59++kliZERERESWS2oCePDgQbz00kt1ygMDA5Gfny8hIiIiIiLLJ/USsKOjI0pKSuqUnzhxAq1bt5YQkfmxs7PDM888Y9gmIiIiuhWpGcPgwYMxd+5cfPvttwAARVGg0Wgwbdo0PP744zJDMxsODg74/PPPZYdBREREZkTqJeB33nkHBQUF8PHxwdWrV9GnTx+0b98ebm5umD9/vszQiIiIiCyW1BFAd3d37N27Fzt27EB6ejr0ej0iIyPRt29fmWGZFSEEysvLAQDOzs5QFEVyRERERGTqFCFxAdkzZ84gNDRU1uHvWElJCdRqNYqLi+Hu7i4lBq1WC1dXVwBcCo6IiKghTOH8LZvUS8Bt27bFgw8+iOXLlxsmgSYiIiKi5iU1ATx06BBiYmLw5ptvIiAgAIMHD8a///1vVFZWygyLiIiIyKJJTQAjIyOxaNEiaDQabNq0CT4+PnjppZfg4+OD0aNHywyNiIiIyGJJvQewPunp6Xj++eeRmZkJnU4nO5ybMoV7CHgPIBERUeOYwvlbNqkjgNfk5uZi4cKFiIiIwL333gsXFxcsXbpUdlhEREREFknqNDArVqzAV199hX379qFjx44YMWIE1q9fb9ZPBhMRERGZOqkJ4Lx58zB8+HB88MEHiIiIkBmK2bK1tcUTTzxh2CYiIiK6Fan3AAohbjhx8eHDh00+KeQ9BEREROaH52/J9wD+OfkrLi5GcnIyIiMjERUVJSkqIiIiIstmEg+B7NixA3/961/h7++PJUuWICEhAYcOHZIdFhEREZFFknYP4NmzZ/H555/j008/hVarxdChQ1FdXY3vvvsOXbp0kRWW2eE0MERERNRYUkYAExIS0KVLF2RlZWHJkiU4f/48lixZIiMUIiIiIqsjZQRw69atmDhxIsaNG4e77rpLRghEREREVkvKCOCePXtQWlqK6Oho9OjRA0uXLkVBQYGMUIiIiIisjpQEMCYmBh9//DHy8vLw0ksvYfXq1QgMDIRer0dKSgpKS0tlhEVERERkFaQ+Bezs7IzRo0dj7969OHLkCKZOnYoFCxbAx8cHgwYNkhkaERERkcUyiWlgAKBjx45YuHAhzp49i2+++UZ2OEREREQWS+pScPWxtbXFkCFDMGTIENmhmAVbW1skJCQYtomIiIhuxeQSQGocR0dHbNiwQXYYREREZEZM5hIwEREREbUMJoBEREREVoYJoJnTarVwcXGBi4sLtFqt7HCIiIjIDPAeQAtQXl4uOwQiIiIyIxY1ApicnIywsDA4OjoiKioKe/bsuWHdjz/+GL169YKHhwc8PDzQt29fHDhwoAWjJSIiIpLDYhLANWvWYPLkyZgxYwYyMjLQq1cvxMfHQ6PR1Ft/165deOqpp7Bz507s378fwcHBiIuLw7lz51o4ciIiIqKWpQghhOwgmkKPHj0QGRmJZcuWGco6d+6MIUOGICkp6Zaf1+l08PDwwNKlSzFq1KgGHbOkpARqtRrFxcVwd3e/7djvhFarhaurKwCgrKwMLi4uUuIgIiIyF6Zw/pbNIkYAq6qqkJaWhri4OKPyuLg4pKamNmgf5eXlqK6uhqen5w3rVFZWoqSkxOhFREREZG4sIgEsLCyETqeDr6+vUbmvry/y8/MbtI9p06YhMDAQffv2vWGdpKQkqNVqwysoKOiO4iYiIiKSwSISwGsURTH6txCiTll9Fi5ciG+++QZr166Fo6PjDetNnz4dxcXFhldubu4dx3ynbGxs0KdPH/Tp0wc2NhbVnURERNRMLGIaGG9vb9ja2tYZ7bt48WKdUcE/e+edd/DWW29h27Zt6Nat203rOjg4wMHB4Y7jbUpOTk7YtWuX7DCIiIjIjFjEkJFKpUJUVBRSUlKMylNSUtCzZ88bfm7RokWYN28eNm/ejOjo6OYOk4iIiMgkWMQIIABMmTIFI0eORHR0NGJiYrBixQpoNBqMHTsWADBq1CgEBgYangheuHAhZs2aha+//hqhoaGG0UNXV1fDU7VERERElshiEsBhw4ahqKgIc+fORV5eHsLDw7Fx40aEhIQAADQajdE9csnJyaiqqsITTzxhtJ/Zs2djzpw5LRn6HdFqtQgNDQUAnDlzhtPAEBER0S1ZzDyAMpjCPEKcB5CIiKhxTOH8LZtF3ANIRERERA3HBJCIiIjIyjABJCIiIrIyTACJiIiIrAwTQCIiIiIrYzHTwFgrGxsbwyTWXAqOiIiIGoIJoJlzcnLCwYMHZYdBREREZoRDRkRERERWhgkgERERkZVhAmjmysvLERoaitDQUJSXl8sOh4iIiMwA7wE0c0II5OTkGLaJiIiIboUjgERERERWhgkgERERkZVhAkhERERkZZgAmqjL+VrZIRAREZGFYgJogo7+dA7fzD2AzJ1nZYdCREREFohPAZugqqs1EHqBvd/+jla+Tgju4nXDuoqioEuXLoZtIiIiolvhCKAJuicuGJ1i/CAEsGXFUVzKu/HlYGdnZxw7dgzHjh2Ds7NzC0ZJRERE5ooJoAlSFAWxT3eCf3s1qip02PDhr6goq5YdFhEREVkIJoAmytbeBvEvdYW7tyNKCiuwafkR6Gr0ssMiIiIiC8AE0IQ5uamQ8HI3qBxtcf7kFez++kSd1T7Ky8tx99134+677+ZScERERNQgTABNnFeAK+JeCIeiAMdT83B4W67R+0IIZGVlISsri0vBERERUYMwATQDIeFeeOCJuwAAqWv/wJnMQskRERERkTljAmgmuj3UBnf3CgAEsPWTYyg8WyY7JCIiIjJTTADNhKIo6DW8AwI7eqC6UocNyb+ivKRKdlhERERkhpgAmhFbWxs88mI41D5OKLtUiU0f1pheqgAAFjBJREFUZaKmWic7LCIiIjIzTADNjKOLPQaM7w4HZzvkny7B7tUnZIdEREREZoYJoBlq5euM/i+GQ7FRcOpgAfx9AhESEsKl4IiIiKhBmACaqaBOnug9vANU9o6Y8egX2L7uAJeCIyIiogZhAmjGwnsHottf2gAAtn2WhQJNqeSIiIiIyBwwATRzDzzRHsFdPFFTpceG5Exor1TKDomIiIhMHBNAM1dZVYk3Vr6AxT9MwOXCEmxclonqKj4ZTERERDfGBNDM6fV6pKWn4fT543BwtsXFnFJs//w4hJ7LwhEREVH9mABakLjnu8DGVsGp9Is4sCFbdjhERERkopgAWhD/9h6IHdERAHBowxn8fjBfckRERERkipgAWpjOPQNwT79gAMCOlb8hP7tYckRERERkapgAWqD7H22H0G7e0NXosXHZEZReqpAdEhEREZkQJoAWyMZGQb/RXeAV6IqrJVXYkJyJqooa2WERERGRiWACaAG8vb3h7e1tVKZytEPCy13h5GaPorNl2PZZFp8MJiIiIgBMAM2ei4sLCgoKUFBQABcXF6P33L2ckDCuG2ztbJD9ayF+/s8pSVESERGRKWECaOH82qrxl5GdAADpWzT4bX+e5IiIiIhINiaAVqBjDz9ExYcAAHZ++RvO/3FFckREREQkExNAM3f16lXExsYiNjYWV69evWG9HgPbot09raHXCWz66AhKCm9cl4iIiCybnewA6M7o9Xrs3r3bsH0jio2Ch5/tgpKidBRoSvGf9zPg11bdUmESERHdlrb3tEa7e3xkh2FxmABaEXsHWySM64Z/LziIksIKlBRyfkAiIjJtah9ntLtHdhSWhwmglXH1cMDQ6ffiVMZF6HWcFoaIiEwbr1Y1DyaAVsillQO6/SVIdhhEREQkCR8CISIiIrIyTACJiIiIrAwvAVsAZ2dn2SEQERGRGWECaOZcXFyg1Wplh0FERERmhJeAiYiIiKwME0AiIiIiK8ME0MxVVFQgMTERiYmJqKjgxM5ERER0a7wH0MzpdDps3LjRsE1ERER0KxwBJCIiIrIyTACJiIiIrIxFJYDJyckICwuDo6MjoqKisGfPnhvWPXbsGB5//HGEhoZCURS8//77LRgpERERkTwWkwCuWbMGkydPxowZM5CRkYFevXohPj4eGo2m3vrl5eVo27YtFixYAD8/vxaOloiIiEgei0kAFy9ejOeffx4vvPACOnfujPfffx9BQUFYtmxZvfXvvfdeLFq0CMOHD4eDg0MLR0tEREQkj0U8BVxVVYW0tDRMmzbNqDwuLg6pqalNdpzKykpUVlYa/l1cXAwAKCkpabJjNNb1q4CUlJTwSWAiIqJbuHbeFkJIjkQei0gACwsLodPp4Ovra1Tu6+uL/Pz8JjtOUlIS3njjjTrlQUFBTXaMOxEQECA7BCIiIrNRWloKtVotOwwpLCIBvEZRFKN/CyHqlN2J6dOnY8qUKYZ/6/V6XLp0CV5eXk1ynJKSEgQFBSE3Nxfu7u53vD9TZ03tZVstkzW1FbCu9rKtlutae7Oysqx64MQiEkBvb2/Y2trWGe27ePFinVHBO+Hg4FDnfsFWrVo12f6vcXd3t4pfwmusqb1sq2WyprYC1tVettVyBQYGwsbGYh6FaDSLaLlKpUJUVBRSUlKMylNSUtCzZ09JURERERGZJosYAQSAKVOmYOTIkYiOjkZMTAxWrFgBjUaDsWPHAgBGjRqFwMBAJCUlAah9cCQrK8uwfe7cORw+fBiurq5o3769tHYQERERNTfbOXPmzJEdRFMIDw+Hl5cX3nrrLbzzzju4evUqVq1ahe7duwMAPvjgA9jZ2WHIkCEAgNzcXHTu3BnLly9HWVkZUlNTsXz5cmRmZuLZZ5+V1g5bW1vExsbCzs5icvObsqb2sq2WyZraClhXe9lWy2Vt7a2PIqz5GWgiIiIiK2QR9wASERERUcMxASQiIiKyMkwAiYiIiKwME0AiIiIiK8ME0IQkJycjLCwMjo6OiIqKwp49e2SHdMeSkpJw7733ws3NDT4+PhgyZAhOnDhhVCc2NhaKohi9hg8fLini2zdnzpw67fDz8zO8L4TAnDlzEBAQACcnJ8TGxuLYsWMSI759oaGhddqqKArGjx8PwPz79KeffsLAgQMREBAARVGwfv16o/cb0peXL1/GyJEjoVaroVarMXLkSFy5cqUlm9EgN2trdXU1Xn/9dXTt2hUuLi4ICAjAqFGjcP78eaN91Pd9+PPa7KbgVv367LPP1mnH/fffb1SnsrISr7zyCry9veHi4oJBgwbh7NmzLdmMBrtVe+v7HVYUBYsWLTLUMZe+bci5piF9p9FoMHDgQLi4uMDb2xsTJ05EVVVVSzalxTABNBFr1qzB5MmTMWPGDGRkZKBXr16Ij4+HRqORHdod2b17N8aPH4+ff/4ZKSkpqKmpQVxcHLRarVG9MWPGIC8vz/Bavny5pIjvzN13323UjiNHjhjeW7hwIRYvXoylS5fi4MGD8PPzQ79+/VBaWiox4ttz8OBBo3Zem4T9ySefNNQx5z7VarXo3r07li5dWu/7DenLp59+GocPH8bmzZuxefNmHD58GCNHjmypJjTYzdpaXl6O9PR0zJo1C+np6Vi7di1+//13DBo0qE7duXPnGvX3zJkzWyL8RrlVvwLAI488YtSOjRs3Gr0/efJkrFu3DqtXr8bevXtRVlaGAQMGQKfTNXf4jXar9l7fzry8PHz66adQFAWPP/64UT1z6NuGnGtu1Xc6nQ6JiYnQarXYu3cvVq9eje+++w5Tp06V1azmJcgk3HfffWLs2LFGZZ06dRLTpk2TFFHzuHjxogAgdu/ebSjr06ePmDRpksSomsbs2bNF9+7d631Pr9cLPz8/sWDBAkNZRUWFUKvV4qOPPmqpEJvNpEmTRLt27YRerxdCWE6fCiEEALFu3TrDvxvSl1lZWQKA+Pnnnw119u/fLwCI3377reWCb6Q/t7U+Bw4cEABETk6OoSwkJES89957zR1ek6qvrc8884wYPHjwDT9z5coVYW9vL1avXm0oO3funLCxsRGbN29utlibQkP6dvDgweKhhx4yKjPHvhWi7rmmIX23ceNGYWNjI86dO2eo88033wgHBwdRXFzcsg1oARwBNAFVVVVIS0tDXFycUXlcXBxSU1MlRdU8iouLAQCenp5G5V999RW8vb1x991347XXXjPLUTEAOHnyJAICAhAWFobhw4fj9OnTAIDs7Gzk5+cb9bGDgwP69Olj9n1cVVWFL7/8EqNHj4aiKIZyS+nTP2tIX+7fvx9qtRo9evQw1Ln//vuhVqvNvr+Li4uhKEqdddDffvtteHl5ISIiAvPnzzfby2a7du2Cj48POnTogDFjxuDixYuG99LS0lBdXW3U9wEBAQgPDzf7fr1w4QI2bNiA559/vs575ti3fz7XNKTv9u/fj/DwcAQEBBjq9O/fH5WVlUhLS2vB6FuG9U6BbUIKCwuh0+ng6+trVO7r64v8/HxJUTU9IQSmTJmCBx98EOHh4YbyESNGICwsDH5+fjh69CimT5+OX3/9tc7azqauR48e+OKLL9ChQwdcuHABb775Jnr27Iljx44Z+rG+Ps7JyZERbpNZv349rly5YrSCjqX0aX0a0pf5+fnw8fGp81kfHx+z/p2uqKjAtGnT8PTTT8Pd3d1QPmnSJERGRsLDwwMHDhzA9OnTkZ2djX/9618So228+Ph4PPnkkwgJCUF2djZmzZqFhx56CGlpaXBwcEB+fj5UKhU8PDyMPmcJ/69euXIl3Nzc8NhjjxmVm2Pf1neuaUjf5efn1/m99vDwgEqlMvv+rQ8TQBNy/egJUPsl/nOZOZswYQIyMzOxd+9eo/IxY8YYtsPDw3HXXXchOjoa6enpiIyMbOkwb1t8fLxhu2vXroiJiUG7du2wcuVKw43kltjHn3zyCeLj443+araUPr2ZW/Vlff1qzv1dXV2N4cOHQ6/XIzk52ei9V1991bDdrVs3eHh44IknnjCMHJmLYcOGGbbDw8MRHR2NkJAQbNiwoU5idD1z7tdrPv30U4wYMQKOjo5G5ebYtzc619TH0n9vb4aXgE2At7c3bG1t6/yFcfHixTp/jZirV155Bd9//z127tyJNm3a3LRuZGQk7O3tcfLkyRaKrnm4uLiga9euOHnypOFpYEvr45ycHGzbtg0vvPDCTetZSp8CaFBf+vn54cKFC3U+W1BQYJb9XV1djaFDhyI7OxspKSlGo3/1ufYHzx9//NES4TUbf39/hISEGL63fn5+qKqqwuXLl43qmfvv8Z49e3DixIlb/h4Dpt+3NzrXNKTv/Pz86vxeX758GdXV1WbdvzfCBNAEqFQqREVF1bk8lpKSgp49e0qKqmkIITBhwgSsXbsWO3bsQFhY2C0/c+zYMVRXV8Pf378FImw+lZWVOH78OPz9/Q2XQ6/v46qqKuzevdus+/izzz6Dj48PEhMTb1rPUvoUQIP6MiYmBsXFxThw4IChzi+//ILi4mKz6+9ryd/Jkyexbdu2Bo36ZGRkAIDZ93dRURFyc3MN7YiKioK9vb1R3+fl5eHo0aNm16/X++STTxAVFYXu3bvfsq6p9u2tzjUN6buYmBgcPXoUeXl5hjpbt26Fg4MDoqKiWqYhLUnSwyf0J6tXrxb29vbik08+EVlZWWLy5MnCxcVFnDlzRnZod2TcuHFCrVaLXbt2iby8PMOrvLxcCCHEH3/8Id544w1x8OBBkZ2dLTZs2CA6deok7rnnHlFTUyM5+saZOnWq2LVrlzh9+rT4+eefxYABA4Sbm5uhDxcsWCDUarVYu3atOHLkiHjqqaeEv7+/KCkpkRz57dHpdCI4OFi8/vrrRuWW0KelpaUiIyNDZGRkCABi8eLFIiMjw/Dka0P68pFHHhHdunUT+/fvF/v37xddu3YVAwYMkNWkG7pZW6urq8WgQYNEmzZtxOHDh41+hysrK4UQQqSmpho+c/r0abFmzRoREBAgBg0aJLlldd2sraWlpWLq1KkiNTVVZGdni507d4qYmBgRGBho1K9jx44Vbdq0Edu2bRPp6enioYceEt27dzfJ7/atvsdCCFFcXCycnZ3FsmXL6nzenPr2VucaIW7ddzU1NSI8PFw8/PDDIj09XWzbtk20adNGTJgwQVazmhUTQBPy4YcfipCQEKFSqURkZKTRVCnmCkC9r88++0wIIYRGoxG9e/cWnp6eQqVSiXbt2omJEyeKoqIiuYHfhmHDhgl/f39hb28vAgICxGOPPSaOHTtmeF+v14vZs2cLPz8/4eDgIHr37i2OHDkiMeI7s2XLFgFAnDhxwqjcEvp0586d9X5vn3nmGSFEw/qyqKhIjBgxQri5uQk3NzcxYsQIcfnyZQmtubmbtTU7O/uGv8M7d+4UQgiRlpYmevToIdRqtXB0dBQdO3YUs2fPFlqtVm7D6nGztpaXl4u4uDjRunVrYW9vL4KDg8UzzzwjNBqN0T6uXr0qJkyYIDw9PYWTk5MYMGBAnTqm4lbfYyGEWL58uXBychJXrlyp83lz6ttbnWuEaFjf5eTkiMTEROHk5CQ8PT3FhAkTREVFRQu3pmUoQgjRTIOLRERERGSCeA8gERERkZVhAkhERERkZZgAEhEREVkZJoBEREREVoYJIBEREZGVYQJIREREZGWYABIRERFZGSaARESNEBoaivfff192GEREd4QJIBGZrGeffRZDhgwBAMTGxmLy5MktduzPP/8crVq1qlN+8OBBvPjiiy0WBxFRc7CTHQARUUuqqqqCSqW67c+3bt26CaMhIpKDI4BEZPKeffZZ7N69Gx988AEURYGiKDhz5gwAICsrCwkJCXB1dYWvry9GjhyJwsJCw2djY2MxYcIETJkyBd7e3ujXrx8AYPHixejatStcXFwQFBSEl19+GWVlZQCAXbt24bnnnkNxcbHheHPmzAFQ9xKwRqPB4MGD4erqCnd3dwwdOhQXLlwwvD9nzhxERERg1apVCA0NhVqtxvDhw1FaWmqo83//93/o2rUrnJyc4OXlhb59+0Kr1TbXj5OIiAkgEZm+Dz74ADExMRgzZgzy8vKQl5eHoKAg5OXloU+fPoiIiMChQ4ewefNmXLhwAUOHDjX6/MqVK2FnZ4d9+/Zh+fLlAAAbGxv885//xNGjR7Fy5Urs2LEDf//73wEAPXv2xPvvvw93d3fD8V577bU6cQkhMGTIEFy6dAm7d+9GSkoKTp06hWHDhhnVO3XqFNavX48ff/wRP/74I3bv3o0FCxYAAPLy8vDUU09h9OjROH78OHbt2oXHHnsMXKadiJoTLwETkclTq9VQqVRwdnaGn5+foXzZsmWIjIzEW2+9ZSj79NNPERQUhN9//x0dOnQAALRv3x4LFy402uf19xOGhYVh3rx5GDduHJKTk6FSqaBWq6EoitHx/mzbtm3IzMxEdnY2goKCAACrVq3C3XffjYMHD+Lee+8FAOj1enz++edwc3MDAIwcORLbt2/H/PnzkZeXh5qaGjz22GMICQkBAHTt2vVOflxERLfEEUAiMltpaWnYuXMnXF1dDa9OnToBqB11uyY6OrrOZ3fu3Il+/fohMDAQbm5uGDVqFIqKihp16fX48eMICgoyJH8A0KVLF7Rq1QrHjx83lIWGhhqSPwDw9/fHxYsXAQDdu3fHww8/jK5du+LJJ5/Exx9/jMuXLzf8h0BEdBuYABKR2dLr9Rg4cCAOHz5s9Dp58iR69+5tqOfi4mL0uZycHCQkJCA8PBzfffcd0tLS8OGHHwIAqqurG3x8IQQURbllub29vdH7iqJAr9cDAGxtbZGSkoJNmzahS5cuWLJkCTp27Ijs7OwGx0FE1FhMAInILKhUKuh0OqOyyMhIHDt2DKGhoWjfvr3R689J3/UOHTqEmpoavPvuu7j//vvRoUMHnD9//pbH+7MuXbpAo9EgNzfXUJaVlYXi4mJ07ty5wW1TFAUPPPAA3njjDWRkZEClUmHdunUN/jwRUWMxASQisxAaGopffvkFZ86cQWFhIfR6PcaPH49Lly7hqaeewoEDB3D69Gls3boVo0ePvmny1q5dO9TU1GDJkiU4ffo0Vq1ahY8++qjO8crKyrB9+3YUFhaivLy8zn769u2Lbt26YcSIEUhPT8eBAwcwatQo9OnTp97LzvX55Zdf8NZbb+HQoUPQaDRYu3YtCgoKGpVAEhE1FhNAIjILr732GmxtbdGlSxe0bt0aGo0GAQEB2LdvH3Q6Hfr374/w8HBMmjQJarUaNjY3/t9bREQEFi9ejLfffhvh4eH46quvkJSUZFSnZ8+eGDt2LIYNG4bWrVvXeYgEqB25W79+PTw8PNC7d2/07dsXbdu2xZo1axrcLnd3d/z0009ISEhAhw4dMHPmTLz77ruIj49v+A+HiKiRFMG5BoiIiIisCkcAiYiIiKwME0AiIiIiK8MEkIiIiMjKMAEkIiIisjJMAImIiIisDBNAIiIiIivDBJCIiIjIyjABJCIiIrIyTACJiIiIrAwTQCIiIiIrwwSQiIiIyMowASQiIiKyMv8PKcTpi/LKpH8AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![aps_kmeans_maximum_metric.png](attachment:aps_kmeans_maximum_metric.png)"
   ]
  },
  {
   "attachments": {
    "aps_skmeans_maximum_metric.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![aps_skmeans_maximum_metric.png](attachment:aps_skmeans_maximum_metric.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final discussion\n",
    "\n",
    "By evaluating the output plots for each considered setting, we can choose the best initialization strategy and stop criterion. In this case, the **skmeans** (spherical-kmeans) and the **maximum_metrix** output the best results; indeed, using them the APS scores are more stable within each run of the algorithm and across different runs. Moreover, with this setting the results reach higher APS scores compared to the other settings. \n",
    "This leads to more reliable link predictions, which can be found in the *myOutFile_skmeans_maximum_metric.txt* file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anna  BirdBox  0.5352081663980298\n",
      "\n",
      "Anna  You should have left  0.5160894658264544\n",
      "\n",
      "Chiara  In Time  0.6156207351321733\n",
      "\n",
      "Luca  Crazy, Stupid, Love  0.5856731206651906\n",
      "\n",
      "Luca  Footloose  0.5856731206651906\n",
      "\n",
      "Mario  Gravity  0.503934730383202\n",
      "\n",
      "Mario  Oceans 8  0.5521496508367011\n",
      "\n",
      "Mario  Spider-Man  0.5862005227851046\n",
      "\n",
      "Renato  Crazy, Stupid, Love  0.585673238844747\n",
      "\n",
      "Renato  Doctor Sleep  0.5526846416619409\n",
      "\n",
      "Renato  Footloose  0.585673238844747\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"./results/case_study_1/myOutFile_skmeans_maximum_metric.txt\") as f:\n",
    "    for line in f:\n",
    "        print(line)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}