Codes/code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import needed libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from copy import deepcopy\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import random\n",
    "import time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constants\n",
    "BLOCKS_POPULATION_FILE = './data/blocks_population.txt'\n",
    "CONFIG = './data/problem_config.txt'\n",
    "MAX_EXE = 2\n",
    "MAX_BANDWIDTH_FOR_A_BTS = 1000000\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>887</td>\n",
       "      <td>783</td>\n",
       "      <td>254</td>\n",
       "      <td>678</td>\n",
       "      <td>510</td>\n",
       "      <td>56</td>\n",
       "      <td>310</td>\n",
       "      <td>217</td>\n",
       "      <td>127</td>\n",
       "      <td>74</td>\n",
       "      <td>714</td>\n",
       "      <td>133</td>\n",
       "      <td>356</td>\n",
       "      <td>366</td>\n",
       "      <td>455</td>\n",
       "      <td>651</td>\n",
       "      <td>350</td>\n",
       "      <td>477</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>697</td>\n",
       "      <td>679</td>\n",
       "      <td>566</td>\n",
       "      <td>71</td>\n",
       "      <td>121</td>\n",
       "      <td>19</td>\n",
       "      <td>341</td>\n",
       "      <td>565</td>\n",
       "      <td>41</td>\n",
       "      <td>499</td>\n",
       "      <td>228</td>\n",
       "      <td>290</td>\n",
       "      <td>687</td>\n",
       "      <td>923</td>\n",
       "      <td>936</td>\n",
       "      <td>619</td>\n",
       "      <td>192</td>\n",
       "      <td>809</td>\n",
       "      <td>910</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>513</td>\n",
       "      <td>549</td>\n",
       "      <td>830</td>\n",
       "      <td>960</td>\n",
       "      <td>747</td>\n",
       "      <td>207</td>\n",
       "      <td>686</td>\n",
       "      <td>126</td>\n",
       "      <td>907</td>\n",
       "      <td>39</td>\n",
       "      <td>450</td>\n",
       "      <td>49</td>\n",
       "      <td>173</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>235</td>\n",
       "      <td>193</td>\n",
       "      <td>98</td>\n",
       "      <td>288</td>\n",
       "      <td>937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>259</td>\n",
       "      <td>983</td>\n",
       "      <td>25</td>\n",
       "      <td>316</td>\n",
       "      <td>70</td>\n",
       "      <td>925</td>\n",
       "      <td>196</td>\n",
       "      <td>529</td>\n",
       "      <td>995</td>\n",
       "      <td>556</td>\n",
       "      <td>555</td>\n",
       "      <td>801</td>\n",
       "      <td>738</td>\n",
       "      <td>320</td>\n",
       "      <td>865</td>\n",
       "      <td>520</td>\n",
       "      <td>352</td>\n",
       "      <td>687</td>\n",
       "      <td>925</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>654</td>\n",
       "      <td>174</td>\n",
       "      <td>69</td>\n",
       "      <td>500</td>\n",
       "      <td>90</td>\n",
       "      <td>996</td>\n",
       "      <td>286</td>\n",
       "      <td>255</td>\n",
       "      <td>501</td>\n",
       "      <td>334</td>\n",
       "      <td>364</td>\n",
       "      <td>236</td>\n",
       "      <td>242</td>\n",
       "      <td>693</td>\n",
       "      <td>780</td>\n",
       "      <td>655</td>\n",
       "      <td>493</td>\n",
       "      <td>319</td>\n",
       "      <td>615</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>197</td>\n",
       "      <td>142</td>\n",
       "      <td>639</td>\n",
       "      <td>101</td>\n",
       "      <td>815</td>\n",
       "      <td>680</td>\n",
       "      <td>552</td>\n",
       "      <td>62</td>\n",
       "      <td>950</td>\n",
       "      <td>721</td>\n",
       "      <td>480</td>\n",
       "      <td>512</td>\n",
       "      <td>966</td>\n",
       "      <td>899</td>\n",
       "      <td>415</td>\n",
       "      <td>975</td>\n",
       "      <td>911</td>\n",
       "      <td>694</td>\n",
       "      <td>194</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>235</td>\n",
       "      <td>196</td>\n",
       "      <td>50</td>\n",
       "      <td>986</td>\n",
       "      <td>277</td>\n",
       "      <td>612</td>\n",
       "      <td>277</td>\n",
       "      <td>549</td>\n",
       "      <td>827</td>\n",
       "      <td>533</td>\n",
       "      <td>182</td>\n",
       "      <td>875</td>\n",
       "      <td>540</td>\n",
       "      <td>687</td>\n",
       "      <td>270</td>\n",
       "      <td>400</td>\n",
       "      <td>688</td>\n",
       "      <td>259</td>\n",
       "      <td>856</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>504</td>\n",
       "      <td>34</td>\n",
       "      <td>270</td>\n",
       "      <td>71</td>\n",
       "      <td>127</td>\n",
       "      <td>902</td>\n",
       "      <td>546</td>\n",
       "      <td>430</td>\n",
       "      <td>310</td>\n",
       "      <td>641</td>\n",
       "      <td>342</td>\n",
       "      <td>259</td>\n",
       "      <td>116</td>\n",
       "      <td>257</td>\n",
       "      <td>377</td>\n",
       "      <td>499</td>\n",
       "      <td>693</td>\n",
       "      <td>70</td>\n",
       "      <td>922</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>941</td>\n",
       "      <td>490</td>\n",
       "      <td>652</td>\n",
       "      <td>767</td>\n",
       "      <td>289</td>\n",
       "      <td>168</td>\n",
       "      <td>382</td>\n",
       "      <td>622</td>\n",
       "      <td>362</td>\n",
       "      <td>208</td>\n",
       "      <td>372</td>\n",
       "      <td>203</td>\n",
       "      <td>209</td>\n",
       "      <td>130</td>\n",
       "      <td>65</td>\n",
       "      <td>657</td>\n",
       "      <td>194</td>\n",
       "      <td>954</td>\n",
       "      <td>267</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>121</td>\n",
       "      <td>563</td>\n",
       "      <td>606</td>\n",
       "      <td>521</td>\n",
       "      <td>142</td>\n",
       "      <td>29</td>\n",
       "      <td>386</td>\n",
       "      <td>186</td>\n",
       "      <td>304</td>\n",
       "      <td>288</td>\n",
       "      <td>343</td>\n",
       "      <td>945</td>\n",
       "      <td>70</td>\n",
       "      <td>107</td>\n",
       "      <td>485</td>\n",
       "      <td>564</td>\n",
       "      <td>790</td>\n",
       "      <td>580</td>\n",
       "      <td>9</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>93</td>\n",
       "      <td>608</td>\n",
       "      <td>595</td>\n",
       "      <td>60</td>\n",
       "      <td>205</td>\n",
       "      <td>682</td>\n",
       "      <td>708</td>\n",
       "      <td>897</td>\n",
       "      <td>747</td>\n",
       "      <td>571</td>\n",
       "      <td>506</td>\n",
       "      <td>918</td>\n",
       "      <td>571</td>\n",
       "      <td>846</td>\n",
       "      <td>542</td>\n",
       "      <td>221</td>\n",
       "      <td>270</td>\n",
       "      <td>390</td>\n",
       "      <td>301</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>587</td>\n",
       "      <td>486</td>\n",
       "      <td>185</td>\n",
       "      <td>212</td>\n",
       "      <td>37</td>\n",
       "      <td>276</td>\n",
       "      <td>338</td>\n",
       "      <td>987</td>\n",
       "      <td>29</td>\n",
       "      <td>753</td>\n",
       "      <td>116</td>\n",
       "      <td>966</td>\n",
       "      <td>923</td>\n",
       "      <td>905</td>\n",
       "      <td>896</td>\n",
       "      <td>120</td>\n",
       "      <td>94</td>\n",
       "      <td>781</td>\n",
       "      <td>727</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>289</td>\n",
       "      <td>770</td>\n",
       "      <td>493</td>\n",
       "      <td>730</td>\n",
       "      <td>559</td>\n",
       "      <td>82</td>\n",
       "      <td>528</td>\n",
       "      <td>635</td>\n",
       "      <td>94</td>\n",
       "      <td>217</td>\n",
       "      <td>264</td>\n",
       "      <td>246</td>\n",
       "      <td>183</td>\n",
       "      <td>574</td>\n",
       "      <td>901</td>\n",
       "      <td>283</td>\n",
       "      <td>562</td>\n",
       "      <td>349</td>\n",
       "      <td>191</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>64</td>\n",
       "      <td>935</td>\n",
       "      <td>597</td>\n",
       "      <td>649</td>\n",
       "      <td>215</td>\n",
       "      <td>503</td>\n",
       "      <td>841</td>\n",
       "      <td>771</td>\n",
       "      <td>985</td>\n",
       "      <td>383</td>\n",
       "      <td>333</td>\n",
       "      <td>169</td>\n",
       "      <td>647</td>\n",
       "      <td>322</td>\n",
       "      <td>801</td>\n",
       "      <td>314</td>\n",
       "      <td>860</td>\n",
       "      <td>277</td>\n",
       "      <td>732</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>687</td>\n",
       "      <td>198</td>\n",
       "      <td>472</td>\n",
       "      <td>770</td>\n",
       "      <td>134</td>\n",
       "      <td>814</td>\n",
       "      <td>526</td>\n",
       "      <td>652</td>\n",
       "      <td>125</td>\n",
       "      <td>564</td>\n",
       "      <td>582</td>\n",
       "      <td>978</td>\n",
       "      <td>621</td>\n",
       "      <td>968</td>\n",
       "      <td>616</td>\n",
       "      <td>883</td>\n",
       "      <td>718</td>\n",
       "      <td>984</td>\n",
       "      <td>581</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>891</td>\n",
       "      <td>491</td>\n",
       "      <td>678</td>\n",
       "      <td>960</td>\n",
       "      <td>190</td>\n",
       "      <td>375</td>\n",
       "      <td>550</td>\n",
       "      <td>709</td>\n",
       "      <td>695</td>\n",
       "      <td>551</td>\n",
       "      <td>324</td>\n",
       "      <td>478</td>\n",
       "      <td>765</td>\n",
       "      <td>55</td>\n",
       "      <td>46</td>\n",
       "      <td>358</td>\n",
       "      <td>29</td>\n",
       "      <td>214</td>\n",
       "      <td>793</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>970</td>\n",
       "      <td>220</td>\n",
       "      <td>176</td>\n",
       "      <td>516</td>\n",
       "      <td>260</td>\n",
       "      <td>416</td>\n",
       "      <td>48</td>\n",
       "      <td>503</td>\n",
       "      <td>293</td>\n",
       "      <td>953</td>\n",
       "      <td>605</td>\n",
       "      <td>202</td>\n",
       "      <td>996</td>\n",
       "      <td>198</td>\n",
       "      <td>757</td>\n",
       "      <td>573</td>\n",
       "      <td>219</td>\n",
       "      <td>386</td>\n",
       "      <td>131</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>763</td>\n",
       "      <td>811</td>\n",
       "      <td>934</td>\n",
       "      <td>703</td>\n",
       "      <td>645</td>\n",
       "      <td>518</td>\n",
       "      <td>118</td>\n",
       "      <td>386</td>\n",
       "      <td>297</td>\n",
       "      <td>260</td>\n",
       "      <td>303</td>\n",
       "      <td>739</td>\n",
       "      <td>595</td>\n",
       "      <td>272</td>\n",
       "      <td>966</td>\n",
       "      <td>725</td>\n",
       "      <td>615</td>\n",
       "      <td>699</td>\n",
       "      <td>731</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>490</td>\n",
       "      <td>253</td>\n",
       "      <td>933</td>\n",
       "      <td>32</td>\n",
       "      <td>926</td>\n",
       "      <td>693</td>\n",
       "      <td>398</td>\n",
       "      <td>531</td>\n",
       "      <td>494</td>\n",
       "      <td>657</td>\n",
       "      <td>513</td>\n",
       "      <td>389</td>\n",
       "      <td>820</td>\n",
       "      <td>516</td>\n",
       "      <td>391</td>\n",
       "      <td>748</td>\n",
       "      <td>42</td>\n",
       "      <td>65</td>\n",
       "      <td>362</td>\n",
       "      <td>849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>683</td>\n",
       "      <td>10</td>\n",
       "      <td>391</td>\n",
       "      <td>195</td>\n",
       "      <td>259</td>\n",
       "      <td>712</td>\n",
       "      <td>799</td>\n",
       "      <td>206</td>\n",
       "      <td>216</td>\n",
       "      <td>712</td>\n",
       "      <td>871</td>\n",
       "      <td>193</td>\n",
       "      <td>967</td>\n",
       "      <td>466</td>\n",
       "      <td>800</td>\n",
       "      <td>489</td>\n",
       "      <td>477</td>\n",
       "      <td>997</td>\n",
       "      <td>610</td>\n",
       "      <td>486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9    10   11   12   13   14   \n",
       "0   196  887  783  254  678  510   56  310  217  127   74  714  133  356  366  \\\n",
       "1   697  679  566   71  121   19  341  565   41  499  228  290  687  923  936   \n",
       "2   513  549  830  960  747  207  686  126  907   39  450   49  173   29   32   \n",
       "3   259  983   25  316   70  925  196  529  995  556  555  801  738  320  865   \n",
       "4   654  174   69  500   90  996  286  255  501  334  364  236  242  693  780   \n",
       "5   197  142  639  101  815  680  552   62  950  721  480  512  966  899  415   \n",
       "6   235  196   50  986  277  612  277  549  827  533  182  875  540  687  270   \n",
       "7   504   34  270   71  127  902  546  430  310  641  342  259  116  257  377   \n",
       "8   941  490  652  767  289  168  382  622  362  208  372  203  209  130   65   \n",
       "9   121  563  606  521  142   29  386  186  304  288  343  945   70  107  485   \n",
       "10   93  608  595   60  205  682  708  897  747  571  506  918  571  846  542   \n",
       "11  587  486  185  212   37  276  338  987   29  753  116  966  923  905  896   \n",
       "12  289  770  493  730  559   82  528  635   94  217  264  246  183  574  901   \n",
       "13   64  935  597  649  215  503  841  771  985  383  333  169  647  322  801   \n",
       "14  687  198  472  770  134  814  526  652  125  564  582  978  621  968  616   \n",
       "15  891  491  678  960  190  375  550  709  695  551  324  478  765   55   46   \n",
       "16  970  220  176  516  260  416   48  503  293  953  605  202  996  198  757   \n",
       "17  763  811  934  703  645  518  118  386  297  260  303  739  595  272  966   \n",
       "18  490  253  933   32  926  693  398  531  494  657  513  389  820  516  391   \n",
       "19  683   10  391  195  259  712  799  206  216  712  871  193  967  466  800   \n",
       "\n",
       "     15   16   17   18   19  \n",
       "0   455  651  350  477  673  \n",
       "1   619  192  809  910  347  \n",
       "2   235  193   98  288  937  \n",
       "3   520  352  687  925  497  \n",
       "4   655  493  319  615  559  \n",
       "5   975  911  694  194  451  \n",
       "6   400  688  259  856  105  \n",
       "7   499  693   70  922  630  \n",
       "8   657  194  954  267   32  \n",
       "9   564  790  580    9  101  \n",
       "10  221  270  390  301  558  \n",
       "11  120   94  781  727  924  \n",
       "12  283  562  349  191  691  \n",
       "13  314  860  277  732  363  \n",
       "14  883  718  984  581  415  \n",
       "15  358   29  214  793   54  \n",
       "16  573  219  386  131  555  \n",
       "17  725  615  699  731   59  \n",
       "18  748   42   65  362  849  \n",
       "19  489  477  997  610  486  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the blocks_population file\n",
    "population = pd.read_csv(BLOCKS_POPULATION_FILE, header=None)\n",
    "population\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tower_construction_cost              500\n",
       "tower_maintanance_cost                 1\n",
       "user_satisfaction_levels     [0.2, 1, 3]\n",
       "user_satisfaction_scores    [10, 20, 40]\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the problem_config file\n",
    "config = pd.read_json(CONFIG, typ='series')\n",
    "config\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.5</th>\n",
       "      <th>1.5</th>\n",
       "      <th>2.5</th>\n",
       "      <th>3.5</th>\n",
       "      <th>4.5</th>\n",
       "      <th>5.5</th>\n",
       "      <th>6.5</th>\n",
       "      <th>7.5</th>\n",
       "      <th>8.5</th>\n",
       "      <th>9.5</th>\n",
       "      <th>10.5</th>\n",
       "      <th>11.5</th>\n",
       "      <th>12.5</th>\n",
       "      <th>13.5</th>\n",
       "      <th>14.5</th>\n",
       "      <th>15.5</th>\n",
       "      <th>16.5</th>\n",
       "      <th>17.5</th>\n",
       "      <th>18.5</th>\n",
       "      <th>19.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>196</td>\n",
       "      <td>887</td>\n",
       "      <td>783</td>\n",
       "      <td>254</td>\n",
       "      <td>678</td>\n",
       "      <td>510</td>\n",
       "      <td>56</td>\n",
       "      <td>310</td>\n",
       "      <td>217</td>\n",
       "      <td>127</td>\n",
       "      <td>74</td>\n",
       "      <td>714</td>\n",
       "      <td>133</td>\n",
       "      <td>356</td>\n",
       "      <td>366</td>\n",
       "      <td>455</td>\n",
       "      <td>651</td>\n",
       "      <td>350</td>\n",
       "      <td>477</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>697</td>\n",
       "      <td>679</td>\n",
       "      <td>566</td>\n",
       "      <td>71</td>\n",
       "      <td>121</td>\n",
       "      <td>19</td>\n",
       "      <td>341</td>\n",
       "      <td>565</td>\n",
       "      <td>41</td>\n",
       "      <td>499</td>\n",
       "      <td>228</td>\n",
       "      <td>290</td>\n",
       "      <td>687</td>\n",
       "      <td>923</td>\n",
       "      <td>936</td>\n",
       "      <td>619</td>\n",
       "      <td>192</td>\n",
       "      <td>809</td>\n",
       "      <td>910</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>513</td>\n",
       "      <td>549</td>\n",
       "      <td>830</td>\n",
       "      <td>960</td>\n",
       "      <td>747</td>\n",
       "      <td>207</td>\n",
       "      <td>686</td>\n",
       "      <td>126</td>\n",
       "      <td>907</td>\n",
       "      <td>39</td>\n",
       "      <td>450</td>\n",
       "      <td>49</td>\n",
       "      <td>173</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>235</td>\n",
       "      <td>193</td>\n",
       "      <td>98</td>\n",
       "      <td>288</td>\n",
       "      <td>937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>259</td>\n",
       "      <td>983</td>\n",
       "      <td>25</td>\n",
       "      <td>316</td>\n",
       "      <td>70</td>\n",
       "      <td>925</td>\n",
       "      <td>196</td>\n",
       "      <td>529</td>\n",
       "      <td>995</td>\n",
       "      <td>556</td>\n",
       "      <td>555</td>\n",
       "      <td>801</td>\n",
       "      <td>738</td>\n",
       "      <td>320</td>\n",
       "      <td>865</td>\n",
       "      <td>520</td>\n",
       "      <td>352</td>\n",
       "      <td>687</td>\n",
       "      <td>925</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.5</th>\n",
       "      <td>654</td>\n",
       "      <td>174</td>\n",
       "      <td>69</td>\n",
       "      <td>500</td>\n",
       "      <td>90</td>\n",
       "      <td>996</td>\n",
       "      <td>286</td>\n",
       "      <td>255</td>\n",
       "      <td>501</td>\n",
       "      <td>334</td>\n",
       "      <td>364</td>\n",
       "      <td>236</td>\n",
       "      <td>242</td>\n",
       "      <td>693</td>\n",
       "      <td>780</td>\n",
       "      <td>655</td>\n",
       "      <td>493</td>\n",
       "      <td>319</td>\n",
       "      <td>615</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.5</th>\n",
       "      <td>197</td>\n",
       "      <td>142</td>\n",
       "      <td>639</td>\n",
       "      <td>101</td>\n",
       "      <td>815</td>\n",
       "      <td>680</td>\n",
       "      <td>552</td>\n",
       "      <td>62</td>\n",
       "      <td>950</td>\n",
       "      <td>721</td>\n",
       "      <td>480</td>\n",
       "      <td>512</td>\n",
       "      <td>966</td>\n",
       "      <td>899</td>\n",
       "      <td>415</td>\n",
       "      <td>975</td>\n",
       "      <td>911</td>\n",
       "      <td>694</td>\n",
       "      <td>194</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.5</th>\n",
       "      <td>235</td>\n",
       "      <td>196</td>\n",
       "      <td>50</td>\n",
       "      <td>986</td>\n",
       "      <td>277</td>\n",
       "      <td>612</td>\n",
       "      <td>277</td>\n",
       "      <td>549</td>\n",
       "      <td>827</td>\n",
       "      <td>533</td>\n",
       "      <td>182</td>\n",
       "      <td>875</td>\n",
       "      <td>540</td>\n",
       "      <td>687</td>\n",
       "      <td>270</td>\n",
       "      <td>400</td>\n",
       "      <td>688</td>\n",
       "      <td>259</td>\n",
       "      <td>856</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.5</th>\n",
       "      <td>504</td>\n",
       "      <td>34</td>\n",
       "      <td>270</td>\n",
       "      <td>71</td>\n",
       "      <td>127</td>\n",
       "      <td>902</td>\n",
       "      <td>546</td>\n",
       "      <td>430</td>\n",
       "      <td>310</td>\n",
       "      <td>641</td>\n",
       "      <td>342</td>\n",
       "      <td>259</td>\n",
       "      <td>116</td>\n",
       "      <td>257</td>\n",
       "      <td>377</td>\n",
       "      <td>499</td>\n",
       "      <td>693</td>\n",
       "      <td>70</td>\n",
       "      <td>922</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.5</th>\n",
       "      <td>941</td>\n",
       "      <td>490</td>\n",
       "      <td>652</td>\n",
       "      <td>767</td>\n",
       "      <td>289</td>\n",
       "      <td>168</td>\n",
       "      <td>382</td>\n",
       "      <td>622</td>\n",
       "      <td>362</td>\n",
       "      <td>208</td>\n",
       "      <td>372</td>\n",
       "      <td>203</td>\n",
       "      <td>209</td>\n",
       "      <td>130</td>\n",
       "      <td>65</td>\n",
       "      <td>657</td>\n",
       "      <td>194</td>\n",
       "      <td>954</td>\n",
       "      <td>267</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.5</th>\n",
       "      <td>121</td>\n",
       "      <td>563</td>\n",
       "      <td>606</td>\n",
       "      <td>521</td>\n",
       "      <td>142</td>\n",
       "      <td>29</td>\n",
       "      <td>386</td>\n",
       "      <td>186</td>\n",
       "      <td>304</td>\n",
       "      <td>288</td>\n",
       "      <td>343</td>\n",
       "      <td>945</td>\n",
       "      <td>70</td>\n",
       "      <td>107</td>\n",
       "      <td>485</td>\n",
       "      <td>564</td>\n",
       "      <td>790</td>\n",
       "      <td>580</td>\n",
       "      <td>9</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.5</th>\n",
       "      <td>93</td>\n",
       "      <td>608</td>\n",
       "      <td>595</td>\n",
       "      <td>60</td>\n",
       "      <td>205</td>\n",
       "      <td>682</td>\n",
       "      <td>708</td>\n",
       "      <td>897</td>\n",
       "      <td>747</td>\n",
       "      <td>571</td>\n",
       "      <td>506</td>\n",
       "      <td>918</td>\n",
       "      <td>571</td>\n",
       "      <td>846</td>\n",
       "      <td>542</td>\n",
       "      <td>221</td>\n",
       "      <td>270</td>\n",
       "      <td>390</td>\n",
       "      <td>301</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.5</th>\n",
       "      <td>587</td>\n",
       "      <td>486</td>\n",
       "      <td>185</td>\n",
       "      <td>212</td>\n",
       "      <td>37</td>\n",
       "      <td>276</td>\n",
       "      <td>338</td>\n",
       "      <td>987</td>\n",
       "      <td>29</td>\n",
       "      <td>753</td>\n",
       "      <td>116</td>\n",
       "      <td>966</td>\n",
       "      <td>923</td>\n",
       "      <td>905</td>\n",
       "      <td>896</td>\n",
       "      <td>120</td>\n",
       "      <td>94</td>\n",
       "      <td>781</td>\n",
       "      <td>727</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.5</th>\n",
       "      <td>289</td>\n",
       "      <td>770</td>\n",
       "      <td>493</td>\n",
       "      <td>730</td>\n",
       "      <td>559</td>\n",
       "      <td>82</td>\n",
       "      <td>528</td>\n",
       "      <td>635</td>\n",
       "      <td>94</td>\n",
       "      <td>217</td>\n",
       "      <td>264</td>\n",
       "      <td>246</td>\n",
       "      <td>183</td>\n",
       "      <td>574</td>\n",
       "      <td>901</td>\n",
       "      <td>283</td>\n",
       "      <td>562</td>\n",
       "      <td>349</td>\n",
       "      <td>191</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.5</th>\n",
       "      <td>64</td>\n",
       "      <td>935</td>\n",
       "      <td>597</td>\n",
       "      <td>649</td>\n",
       "      <td>215</td>\n",
       "      <td>503</td>\n",
       "      <td>841</td>\n",
       "      <td>771</td>\n",
       "      <td>985</td>\n",
       "      <td>383</td>\n",
       "      <td>333</td>\n",
       "      <td>169</td>\n",
       "      <td>647</td>\n",
       "      <td>322</td>\n",
       "      <td>801</td>\n",
       "      <td>314</td>\n",
       "      <td>860</td>\n",
       "      <td>277</td>\n",
       "      <td>732</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.5</th>\n",
       "      <td>687</td>\n",
       "      <td>198</td>\n",
       "      <td>472</td>\n",
       "      <td>770</td>\n",
       "      <td>134</td>\n",
       "      <td>814</td>\n",
       "      <td>526</td>\n",
       "      <td>652</td>\n",
       "      <td>125</td>\n",
       "      <td>564</td>\n",
       "      <td>582</td>\n",
       "      <td>978</td>\n",
       "      <td>621</td>\n",
       "      <td>968</td>\n",
       "      <td>616</td>\n",
       "      <td>883</td>\n",
       "      <td>718</td>\n",
       "      <td>984</td>\n",
       "      <td>581</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.5</th>\n",
       "      <td>891</td>\n",
       "      <td>491</td>\n",
       "      <td>678</td>\n",
       "      <td>960</td>\n",
       "      <td>190</td>\n",
       "      <td>375</td>\n",
       "      <td>550</td>\n",
       "      <td>709</td>\n",
       "      <td>695</td>\n",
       "      <td>551</td>\n",
       "      <td>324</td>\n",
       "      <td>478</td>\n",
       "      <td>765</td>\n",
       "      <td>55</td>\n",
       "      <td>46</td>\n",
       "      <td>358</td>\n",
       "      <td>29</td>\n",
       "      <td>214</td>\n",
       "      <td>793</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.5</th>\n",
       "      <td>970</td>\n",
       "      <td>220</td>\n",
       "      <td>176</td>\n",
       "      <td>516</td>\n",
       "      <td>260</td>\n",
       "      <td>416</td>\n",
       "      <td>48</td>\n",
       "      <td>503</td>\n",
       "      <td>293</td>\n",
       "      <td>953</td>\n",
       "      <td>605</td>\n",
       "      <td>202</td>\n",
       "      <td>996</td>\n",
       "      <td>198</td>\n",
       "      <td>757</td>\n",
       "      <td>573</td>\n",
       "      <td>219</td>\n",
       "      <td>386</td>\n",
       "      <td>131</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.5</th>\n",
       "      <td>763</td>\n",
       "      <td>811</td>\n",
       "      <td>934</td>\n",
       "      <td>703</td>\n",
       "      <td>645</td>\n",
       "      <td>518</td>\n",
       "      <td>118</td>\n",
       "      <td>386</td>\n",
       "      <td>297</td>\n",
       "      <td>260</td>\n",
       "      <td>303</td>\n",
       "      <td>739</td>\n",
       "      <td>595</td>\n",
       "      <td>272</td>\n",
       "      <td>966</td>\n",
       "      <td>725</td>\n",
       "      <td>615</td>\n",
       "      <td>699</td>\n",
       "      <td>731</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.5</th>\n",
       "      <td>490</td>\n",
       "      <td>253</td>\n",
       "      <td>933</td>\n",
       "      <td>32</td>\n",
       "      <td>926</td>\n",
       "      <td>693</td>\n",
       "      <td>398</td>\n",
       "      <td>531</td>\n",
       "      <td>494</td>\n",
       "      <td>657</td>\n",
       "      <td>513</td>\n",
       "      <td>389</td>\n",
       "      <td>820</td>\n",
       "      <td>516</td>\n",
       "      <td>391</td>\n",
       "      <td>748</td>\n",
       "      <td>42</td>\n",
       "      <td>65</td>\n",
       "      <td>362</td>\n",
       "      <td>849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.5</th>\n",
       "      <td>683</td>\n",
       "      <td>10</td>\n",
       "      <td>391</td>\n",
       "      <td>195</td>\n",
       "      <td>259</td>\n",
       "      <td>712</td>\n",
       "      <td>799</td>\n",
       "      <td>206</td>\n",
       "      <td>216</td>\n",
       "      <td>712</td>\n",
       "      <td>871</td>\n",
       "      <td>193</td>\n",
       "      <td>967</td>\n",
       "      <td>466</td>\n",
       "      <td>800</td>\n",
       "      <td>489</td>\n",
       "      <td>477</td>\n",
       "      <td>997</td>\n",
       "      <td>610</td>\n",
       "      <td>486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0.5   1.5   2.5   3.5   4.5   5.5   6.5   7.5   8.5   9.5   10.5  11.5   \n",
       "0.5    196   887   783   254   678   510    56   310   217   127    74   714  \\\n",
       "1.5    697   679   566    71   121    19   341   565    41   499   228   290   \n",
       "2.5    513   549   830   960   747   207   686   126   907    39   450    49   \n",
       "3.5    259   983    25   316    70   925   196   529   995   556   555   801   \n",
       "4.5    654   174    69   500    90   996   286   255   501   334   364   236   \n",
       "5.5    197   142   639   101   815   680   552    62   950   721   480   512   \n",
       "6.5    235   196    50   986   277   612   277   549   827   533   182   875   \n",
       "7.5    504    34   270    71   127   902   546   430   310   641   342   259   \n",
       "8.5    941   490   652   767   289   168   382   622   362   208   372   203   \n",
       "9.5    121   563   606   521   142    29   386   186   304   288   343   945   \n",
       "10.5    93   608   595    60   205   682   708   897   747   571   506   918   \n",
       "11.5   587   486   185   212    37   276   338   987    29   753   116   966   \n",
       "12.5   289   770   493   730   559    82   528   635    94   217   264   246   \n",
       "13.5    64   935   597   649   215   503   841   771   985   383   333   169   \n",
       "14.5   687   198   472   770   134   814   526   652   125   564   582   978   \n",
       "15.5   891   491   678   960   190   375   550   709   695   551   324   478   \n",
       "16.5   970   220   176   516   260   416    48   503   293   953   605   202   \n",
       "17.5   763   811   934   703   645   518   118   386   297   260   303   739   \n",
       "18.5   490   253   933    32   926   693   398   531   494   657   513   389   \n",
       "19.5   683    10   391   195   259   712   799   206   216   712   871   193   \n",
       "\n",
       "      12.5  13.5  14.5  15.5  16.5  17.5  18.5  19.5  \n",
       "0.5    133   356   366   455   651   350   477   673  \n",
       "1.5    687   923   936   619   192   809   910   347  \n",
       "2.5    173    29    32   235   193    98   288   937  \n",
       "3.5    738   320   865   520   352   687   925   497  \n",
       "4.5    242   693   780   655   493   319   615   559  \n",
       "5.5    966   899   415   975   911   694   194   451  \n",
       "6.5    540   687   270   400   688   259   856   105  \n",
       "7.5    116   257   377   499   693    70   922   630  \n",
       "8.5    209   130    65   657   194   954   267    32  \n",
       "9.5     70   107   485   564   790   580     9   101  \n",
       "10.5   571   846   542   221   270   390   301   558  \n",
       "11.5   923   905   896   120    94   781   727   924  \n",
       "12.5   183   574   901   283   562   349   191   691  \n",
       "13.5   647   322   801   314   860   277   732   363  \n",
       "14.5   621   968   616   883   718   984   581   415  \n",
       "15.5   765    55    46   358    29   214   793    54  \n",
       "16.5   996   198   757   573   219   386   131   555  \n",
       "17.5   595   272   966   725   615   699   731    59  \n",
       "18.5   820   516   391   748    42    65   362   849  \n",
       "19.5   967   466   800   489   477   997   610   486  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename the columns and the index\n",
    "population.rename(index=lambda x: x+0.5, columns=lambda x: x+0.5, inplace=True)\n",
    "population\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "GRIDS = population.shape[0]\n",
    "GRIDS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Neighbor:\n",
    "    def __init__(self, x, y, population):\n",
    "        # Initialize the class with x, y and population\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.population = population\n",
    "\n",
    "    def __eq__(self, other):\n",
    "        # Check if two neighbors are equal\n",
    "        return (self.x == other.x) and (self.y == other.y)\n",
    "\n",
    "    def __hash__(self):\n",
    "        # Hash the neighbor object\n",
    "        return hash((self.x, self.y, self.population))\n",
    "\n",
    "    def __repr__(self):\n",
    "        # Return a string representation of the object\n",
    "        return f'Neighbor({self.x}, {self.y}, {self.population})'\n",
    "\n",
    "    def calculate_bandwidth_per_user(self, bts, neighbors):\n",
    "        # Calculate bandwidth per user\n",
    "        return bts.calculate_bandwidth(self, neighbors) / self.population\n",
    "\n",
    "    def calculate_satisfaction(self, bts, neighbors):\n",
    "        # Calculate satisfaction based on bandwidth per user\n",
    "        bpu = self.calculate_bandwidth_per_user(bts, neighbors)\n",
    "\n",
    "        # Check if the bandwidth per user is less than the minimum required\n",
    "        if bpu < config['user_satisfaction_levels'][0]:\n",
    "            satisfaction = 0\n",
    "\n",
    "        # Check if the bandwidth per user is greater than the maximum required\n",
    "        elif bpu >= config['user_satisfaction_levels'][-1]:\n",
    "            satisfaction = config['user_satisfaction_scores'][-1]\n",
    "\n",
    "        # Check if the bandwidth per user is between the minimum and maximum required\n",
    "        else:\n",
    "            for i in range(1, len(config['user_satisfaction_levels'])):\n",
    "                if config['user_satisfaction_levels'][i - 1] <= bpu < config['user_satisfaction_levels'][i]:\n",
    "                    satisfaction = config['user_satisfaction_scores'][i - 1]\n",
    "                    break\n",
    "\n",
    "        # Return the satisfaction\n",
    "        return satisfaction * self.population\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Neighbor(0.5, 0.5, 196), Neighbor(0.5, 1.5, 887), Neighbor(0.5, 2.5, 783), Neighbor(0.5, 3.5, 254), Neighbor(0.5, 4.5, 678), Neighbor(0.5, 5.5, 510), Neighbor(0.5, 6.5, 56), Neighbor(0.5, 7.5, 310), Neighbor(0.5, 8.5, 217), Neighbor(0.5, 9.5, 127), Neighbor(0.5, 10.5, 74), Neighbor(0.5, 11.5, 714), Neighbor(0.5, 12.5, 133), Neighbor(0.5, 13.5, 356), Neighbor(0.5, 14.5, 366), Neighbor(0.5, 15.5, 455), Neighbor(0.5, 16.5, 651), Neighbor(0.5, 17.5, 350), Neighbor(0.5, 18.5, 477), Neighbor(0.5, 19.5, 673), Neighbor(1.5, 0.5, 697), Neighbor(1.5, 1.5, 679), Neighbor(1.5, 2.5, 566), Neighbor(1.5, 3.5, 71), Neighbor(1.5, 4.5, 121), Neighbor(1.5, 5.5, 19), Neighbor(1.5, 6.5, 341), Neighbor(1.5, 7.5, 565), Neighbor(1.5, 8.5, 41), Neighbor(1.5, 9.5, 499), Neighbor(1.5, 10.5, 228), Neighbor(1.5, 11.5, 290), Neighbor(1.5, 12.5, 687), Neighbor(1.5, 13.5, 923), Neighbor(1.5, 14.5, 936), Neighbor(1.5, 15.5, 619), Neighbor(1.5, 16.5, 192), Neighbor(1.5, 17.5, 809), Neighbor(1.5, 18.5, 910), Neighbor(1.5, 19.5, 347), Neighbor(2.5, 0.5, 513), Neighbor(2.5, 1.5, 549), Neighbor(2.5, 2.5, 830), Neighbor(2.5, 3.5, 960), Neighbor(2.5, 4.5, 747), Neighbor(2.5, 5.5, 207), Neighbor(2.5, 6.5, 686), Neighbor(2.5, 7.5, 126), Neighbor(2.5, 8.5, 907), Neighbor(2.5, 9.5, 39), Neighbor(2.5, 10.5, 450), Neighbor(2.5, 11.5, 49), Neighbor(2.5, 12.5, 173), Neighbor(2.5, 13.5, 29), Neighbor(2.5, 14.5, 32), Neighbor(2.5, 15.5, 235), Neighbor(2.5, 16.5, 193), Neighbor(2.5, 17.5, 98), Neighbor(2.5, 18.5, 288), Neighbor(2.5, 19.5, 937), Neighbor(3.5, 0.5, 259), Neighbor(3.5, 1.5, 983), Neighbor(3.5, 2.5, 25), Neighbor(3.5, 3.5, 316), Neighbor(3.5, 4.5, 70), Neighbor(3.5, 5.5, 925), Neighbor(3.5, 6.5, 196), Neighbor(3.5, 7.5, 529), Neighbor(3.5, 8.5, 995), Neighbor(3.5, 9.5, 556), Neighbor(3.5, 10.5, 555), Neighbor(3.5, 11.5, 801), Neighbor(3.5, 12.5, 738), Neighbor(3.5, 13.5, 320), Neighbor(3.5, 14.5, 865), Neighbor(3.5, 15.5, 520), Neighbor(3.5, 16.5, 352), Neighbor(3.5, 17.5, 687), Neighbor(3.5, 18.5, 925), Neighbor(3.5, 19.5, 497), Neighbor(4.5, 0.5, 654), Neighbor(4.5, 1.5, 174), Neighbor(4.5, 2.5, 69), Neighbor(4.5, 3.5, 500), Neighbor(4.5, 4.5, 90), Neighbor(4.5, 5.5, 996), Neighbor(4.5, 6.5, 286), Neighbor(4.5, 7.5, 255), Neighbor(4.5, 8.5, 501), Neighbor(4.5, 9.5, 334), Neighbor(4.5, 10.5, 364), Neighbor(4.5, 11.5, 236), Neighbor(4.5, 12.5, 242), Neighbor(4.5, 13.5, 693), Neighbor(4.5, 14.5, 780), Neighbor(4.5, 15.5, 655), Neighbor(4.5, 16.5, 493), Neighbor(4.5, 17.5, 319), Neighbor(4.5, 18.5, 615), Neighbor(4.5, 19.5, 559), Neighbor(5.5, 0.5, 197), Neighbor(5.5, 1.5, 142), Neighbor(5.5, 2.5, 639), Neighbor(5.5, 3.5, 101), Neighbor(5.5, 4.5, 815), Neighbor(5.5, 5.5, 680), Neighbor(5.5, 6.5, 552), Neighbor(5.5, 7.5, 62), Neighbor(5.5, 8.5, 950), Neighbor(5.5, 9.5, 721), Neighbor(5.5, 10.5, 480), Neighbor(5.5, 11.5, 512), Neighbor(5.5, 12.5, 966), Neighbor(5.5, 13.5, 899), Neighbor(5.5, 14.5, 415), Neighbor(5.5, 15.5, 975), Neighbor(5.5, 16.5, 911), Neighbor(5.5, 17.5, 694), Neighbor(5.5, 18.5, 194), Neighbor(5.5, 19.5, 451), Neighbor(6.5, 0.5, 235), Neighbor(6.5, 1.5, 196), Neighbor(6.5, 2.5, 50), Neighbor(6.5, 3.5, 986), Neighbor(6.5, 4.5, 277), Neighbor(6.5, 5.5, 612), Neighbor(6.5, 6.5, 277), Neighbor(6.5, 7.5, 549), Neighbor(6.5, 8.5, 827), Neighbor(6.5, 9.5, 533), Neighbor(6.5, 10.5, 182), Neighbor(6.5, 11.5, 875), Neighbor(6.5, 12.5, 540), Neighbor(6.5, 13.5, 687), Neighbor(6.5, 14.5, 270), Neighbor(6.5, 15.5, 400), Neighbor(6.5, 16.5, 688), Neighbor(6.5, 17.5, 259), Neighbor(6.5, 18.5, 856), Neighbor(6.5, 19.5, 105), Neighbor(7.5, 0.5, 504), Neighbor(7.5, 1.5, 34), Neighbor(7.5, 2.5, 270), Neighbor(7.5, 3.5, 71), Neighbor(7.5, 4.5, 127), Neighbor(7.5, 5.5, 902), Neighbor(7.5, 6.5, 546), Neighbor(7.5, 7.5, 430), Neighbor(7.5, 8.5, 310), Neighbor(7.5, 9.5, 641), Neighbor(7.5, 10.5, 342), Neighbor(7.5, 11.5, 259), Neighbor(7.5, 12.5, 116), Neighbor(7.5, 13.5, 257), Neighbor(7.5, 14.5, 377), Neighbor(7.5, 15.5, 499), Neighbor(7.5, 16.5, 693), Neighbor(7.5, 17.5, 70), Neighbor(7.5, 18.5, 922), Neighbor(7.5, 19.5, 630), Neighbor(8.5, 0.5, 941), Neighbor(8.5, 1.5, 490), Neighbor(8.5, 2.5, 652), Neighbor(8.5, 3.5, 767), Neighbor(8.5, 4.5, 289), Neighbor(8.5, 5.5, 168), Neighbor(8.5, 6.5, 382), Neighbor(8.5, 7.5, 622), Neighbor(8.5, 8.5, 362), Neighbor(8.5, 9.5, 208), Neighbor(8.5, 10.5, 372), Neighbor(8.5, 11.5, 203), Neighbor(8.5, 12.5, 209), Neighbor(8.5, 13.5, 130), Neighbor(8.5, 14.5, 65), Neighbor(8.5, 15.5, 657), Neighbor(8.5, 16.5, 194), Neighbor(8.5, 17.5, 954), Neighbor(8.5, 18.5, 267), Neighbor(8.5, 19.5, 32), Neighbor(9.5, 0.5, 121), Neighbor(9.5, 1.5, 563), Neighbor(9.5, 2.5, 606), Neighbor(9.5, 3.5, 521), Neighbor(9.5, 4.5, 142), Neighbor(9.5, 5.5, 29), Neighbor(9.5, 6.5, 386), Neighbor(9.5, 7.5, 186), Neighbor(9.5, 8.5, 304), Neighbor(9.5, 9.5, 288), Neighbor(9.5, 10.5, 343), Neighbor(9.5, 11.5, 945), Neighbor(9.5, 12.5, 70), Neighbor(9.5, 13.5, 107), Neighbor(9.5, 14.5, 485), Neighbor(9.5, 15.5, 564), Neighbor(9.5, 16.5, 790), Neighbor(9.5, 17.5, 580), Neighbor(9.5, 18.5, 9), Neighbor(9.5, 19.5, 101), Neighbor(10.5, 0.5, 93), Neighbor(10.5, 1.5, 608), Neighbor(10.5, 2.5, 595), Neighbor(10.5, 3.5, 60), Neighbor(10.5, 4.5, 205), Neighbor(10.5, 5.5, 682), Neighbor(10.5, 6.5, 708), Neighbor(10.5, 7.5, 897), Neighbor(10.5, 8.5, 747), Neighbor(10.5, 9.5, 571), Neighbor(10.5, 10.5, 506), Neighbor(10.5, 11.5, 918), Neighbor(10.5, 12.5, 571), Neighbor(10.5, 13.5, 846), Neighbor(10.5, 14.5, 542), Neighbor(10.5, 15.5, 221), Neighbor(10.5, 16.5, 270), Neighbor(10.5, 17.5, 390), Neighbor(10.5, 18.5, 301), Neighbor(10.5, 19.5, 558), Neighbor(11.5, 0.5, 587), Neighbor(11.5, 1.5, 486), Neighbor(11.5, 2.5, 185), Neighbor(11.5, 3.5, 212), Neighbor(11.5, 4.5, 37), Neighbor(11.5, 5.5, 276), Neighbor(11.5, 6.5, 338), Neighbor(11.5, 7.5, 987), Neighbor(11.5, 8.5, 29), Neighbor(11.5, 9.5, 753), Neighbor(11.5, 10.5, 116), Neighbor(11.5, 11.5, 966), Neighbor(11.5, 12.5, 923), Neighbor(11.5, 13.5, 905), Neighbor(11.5, 14.5, 896), Neighbor(11.5, 15.5, 120), Neighbor(11.5, 16.5, 94), Neighbor(11.5, 17.5, 781), Neighbor(11.5, 18.5, 727), Neighbor(11.5, 19.5, 924), Neighbor(12.5, 0.5, 289), Neighbor(12.5, 1.5, 770), Neighbor(12.5, 2.5, 493), Neighbor(12.5, 3.5, 730), Neighbor(12.5, 4.5, 559), Neighbor(12.5, 5.5, 82), Neighbor(12.5, 6.5, 528), Neighbor(12.5, 7.5, 635), Neighbor(12.5, 8.5, 94), Neighbor(12.5, 9.5, 217), Neighbor(12.5, 10.5, 264), Neighbor(12.5, 11.5, 246), Neighbor(12.5, 12.5, 183), Neighbor(12.5, 13.5, 574), Neighbor(12.5, 14.5, 901), Neighbor(12.5, 15.5, 283), Neighbor(12.5, 16.5, 562), Neighbor(12.5, 17.5, 349), Neighbor(12.5, 18.5, 191), Neighbor(12.5, 19.5, 691), Neighbor(13.5, 0.5, 64), Neighbor(13.5, 1.5, 935), Neighbor(13.5, 2.5, 597), Neighbor(13.5, 3.5, 649), Neighbor(13.5, 4.5, 215), Neighbor(13.5, 5.5, 503), Neighbor(13.5, 6.5, 841), Neighbor(13.5, 7.5, 771), Neighbor(13.5, 8.5, 985), Neighbor(13.5, 9.5, 383), Neighbor(13.5, 10.5, 333), Neighbor(13.5, 11.5, 169), Neighbor(13.5, 12.5, 647), Neighbor(13.5, 13.5, 322), Neighbor(13.5, 14.5, 801), Neighbor(13.5, 15.5, 314), Neighbor(13.5, 16.5, 860), Neighbor(13.5, 17.5, 277), Neighbor(13.5, 18.5, 732), Neighbor(13.5, 19.5, 363), Neighbor(14.5, 0.5, 687), Neighbor(14.5, 1.5, 198), Neighbor(14.5, 2.5, 472), Neighbor(14.5, 3.5, 770), Neighbor(14.5, 4.5, 134), Neighbor(14.5, 5.5, 814), Neighbor(14.5, 6.5, 526), Neighbor(14.5, 7.5, 652), Neighbor(14.5, 8.5, 125), Neighbor(14.5, 9.5, 564), Neighbor(14.5, 10.5, 582), Neighbor(14.5, 11.5, 978), Neighbor(14.5, 12.5, 621), Neighbor(14.5, 13.5, 968), Neighbor(14.5, 14.5, 616), Neighbor(14.5, 15.5, 883), Neighbor(14.5, 16.5, 718), Neighbor(14.5, 17.5, 984), Neighbor(14.5, 18.5, 581), Neighbor(14.5, 19.5, 415), Neighbor(15.5, 0.5, 891), Neighbor(15.5, 1.5, 491), Neighbor(15.5, 2.5, 678), Neighbor(15.5, 3.5, 960), Neighbor(15.5, 4.5, 190), Neighbor(15.5, 5.5, 375), Neighbor(15.5, 6.5, 550), Neighbor(15.5, 7.5, 709), Neighbor(15.5, 8.5, 695), Neighbor(15.5, 9.5, 551), Neighbor(15.5, 10.5, 324), Neighbor(15.5, 11.5, 478), Neighbor(15.5, 12.5, 765), Neighbor(15.5, 13.5, 55), Neighbor(15.5, 14.5, 46), Neighbor(15.5, 15.5, 358), Neighbor(15.5, 16.5, 29), Neighbor(15.5, 17.5, 214), Neighbor(15.5, 18.5, 793), Neighbor(15.5, 19.5, 54), Neighbor(16.5, 0.5, 970), Neighbor(16.5, 1.5, 220), Neighbor(16.5, 2.5, 176), Neighbor(16.5, 3.5, 516), Neighbor(16.5, 4.5, 260), Neighbor(16.5, 5.5, 416), Neighbor(16.5, 6.5, 48), Neighbor(16.5, 7.5, 503), Neighbor(16.5, 8.5, 293), Neighbor(16.5, 9.5, 953), Neighbor(16.5, 10.5, 605), Neighbor(16.5, 11.5, 202), Neighbor(16.5, 12.5, 996), Neighbor(16.5, 13.5, 198), Neighbor(16.5, 14.5, 757), Neighbor(16.5, 15.5, 573), Neighbor(16.5, 16.5, 219), Neighbor(16.5, 17.5, 386), Neighbor(16.5, 18.5, 131), Neighbor(16.5, 19.5, 555), Neighbor(17.5, 0.5, 763), Neighbor(17.5, 1.5, 811), Neighbor(17.5, 2.5, 934), Neighbor(17.5, 3.5, 703), Neighbor(17.5, 4.5, 645), Neighbor(17.5, 5.5, 518), Neighbor(17.5, 6.5, 118), Neighbor(17.5, 7.5, 386), Neighbor(17.5, 8.5, 297), Neighbor(17.5, 9.5, 260), Neighbor(17.5, 10.5, 303), Neighbor(17.5, 11.5, 739), Neighbor(17.5, 12.5, 595), Neighbor(17.5, 13.5, 272), Neighbor(17.5, 14.5, 966), Neighbor(17.5, 15.5, 725), Neighbor(17.5, 16.5, 615), Neighbor(17.5, 17.5, 699), Neighbor(17.5, 18.5, 731), Neighbor(17.5, 19.5, 59), Neighbor(18.5, 0.5, 490), Neighbor(18.5, 1.5, 253), Neighbor(18.5, 2.5, 933), Neighbor(18.5, 3.5, 32), Neighbor(18.5, 4.5, 926), Neighbor(18.5, 5.5, 693), Neighbor(18.5, 6.5, 398), Neighbor(18.5, 7.5, 531), Neighbor(18.5, 8.5, 494), Neighbor(18.5, 9.5, 657), Neighbor(18.5, 10.5, 513), Neighbor(18.5, 11.5, 389), Neighbor(18.5, 12.5, 820), Neighbor(18.5, 13.5, 516), Neighbor(18.5, 14.5, 391), Neighbor(18.5, 15.5, 748), Neighbor(18.5, 16.5, 42), Neighbor(18.5, 17.5, 65), Neighbor(18.5, 18.5, 362), Neighbor(18.5, 19.5, 849), Neighbor(19.5, 0.5, 683), Neighbor(19.5, 1.5, 10), Neighbor(19.5, 2.5, 391), Neighbor(19.5, 3.5, 195), Neighbor(19.5, 4.5, 259), Neighbor(19.5, 5.5, 712), Neighbor(19.5, 6.5, 799), Neighbor(19.5, 7.5, 206), Neighbor(19.5, 8.5, 216), Neighbor(19.5, 9.5, 712), Neighbor(19.5, 10.5, 871), Neighbor(19.5, 11.5, 193), Neighbor(19.5, 12.5, 967), Neighbor(19.5, 13.5, 466), Neighbor(19.5, 14.5, 800), Neighbor(19.5, 15.5, 489), Neighbor(19.5, 16.5, 477), Neighbor(19.5, 17.5, 997), Neighbor(19.5, 18.5, 610), Neighbor(19.5, 19.5, 486)]\n",
      "min_population=9 max_population=997\n"
     ]
    }
   ],
   "source": [
    "# Create a list of neighbors\n",
    "list_of_neighbors = []\n",
    "for i in population.index:\n",
    "    for j in population.columns:\n",
    "        list_of_neighbors.append(Neighbor(i, j, population.loc[i, j]))\n",
    "\n",
    "\n",
    "# Minimum and maximum of population\n",
    "min_population = np.min(population.values)\n",
    "max_population = np.max(population.values)\n",
    "print(list_of_neighbors)\n",
    "print(f\"{min_population=} {max_population=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BTS:\n",
    "    def __init__(self, x, y, bandwidth):\n",
    "        # Initialize the class with x, y and bandwidth\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.bandwidth = bandwidth\n",
    "        # Calculate the cost of tower construction and maintenance\n",
    "        self.cost = config['tower_construction_cost'] + \\\n",
    "            (config['tower_maintanance_cost'] * bandwidth)\n",
    "\n",
    "    def __repr__(self):\n",
    "        # Return a string representation of the object\n",
    "        return f'BTS({self.x}, {self.y}, {self.bandwidth})'\n",
    "\n",
    "    def __eq__(self, other):\n",
    "        # Check if two BTS objects are equal\n",
    "        return (self.x == other.x) and (self.y == other.y)\n",
    "\n",
    "    def __hash__(self):\n",
    "        # Hash the BTS object\n",
    "        return hash((self.x, self.y, self.bandwidth))\n",
    "\n",
    "    def calculate_bandwidth_prime(self, neighbor, neighbors):\n",
    "        # Calculate bandwidth prime\n",
    "        return (neighbor.population * self.bandwidth) / neighbors\n",
    "\n",
    "    def calculate_bandwidth(self, neighbor, neighbors):\n",
    "        # Calculate bandwidth based on covariance formula between two points\n",
    "        diff = np.array((neighbor.x, neighbor.y)) - np.array((self.x, self.y))\n",
    "        cov_ty_bx = np.exp(np.matmul(\n",
    "            np.matmul((-0.5 * diff), np.linalg.inv(np.array([[8, 0], [0, 8]]))), diff.T))\n",
    "        return cov_ty_bx * self.calculate_bandwidth_prime(neighbor, neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Chromosome:\n",
    "    def __init__(self, assigned_neighbors=None):\n",
    "        # Initialize the class with assigned neighbors\n",
    "        self.bts = list()\n",
    "\n",
    "        if assigned_neighbors:\n",
    "            self.assigned_neighbors = assigned_neighbors\n",
    "        else:\n",
    "            self.assigned_neighbors = {n: None for n in list_of_neighbors}\n",
    "\n",
    "        self.cost = None\n",
    "        self.satisfaction = None\n",
    "        self.fitness = None\n",
    "\n",
    "    def __repr__(self):\n",
    "        # Return a string representation of the object\n",
    "        return f'N_BTS: {len(self.bts)}, Cost: {self.cost}, Satisfaction: {self.satisfaction}, Fitness: {self.fitness}'\n",
    "\n",
    "    def initialize(self):\n",
    "        # Initialize the chromosome with random unique BTS objects in it\n",
    "        rand = np.random.randint(1, len(list_of_neighbors))\n",
    "        for __ in range(rand):\n",
    "            new_gen_bts = BTS(np.random.uniform(0, GRIDS), np.random.uniform(0, GRIDS), np.random.uniform(\n",
    "                config['user_satisfaction_levels'][0] * min_population, MAX_BANDWIDTH_FOR_A_BTS))\n",
    "            if new_gen_bts not in self.bts:\n",
    "                self.bts.append(new_gen_bts)\n",
    "\n",
    "        # Assign BTS objects to neighbors\n",
    "        self.assign_bts()\n",
    "\n",
    "    def assign_bts(self):\n",
    "        # Assign BTS objects to neighbors based on the Euclidean distance between them\n",
    "        for neighbor in self.assigned_neighbors:\n",
    "            bts = min(self.bts, key=lambda b: np.sqrt(\n",
    "                np.power(b.x - neighbor.x, 2) + np.power(b.y - neighbor.y, 2)))\n",
    "            self.assigned_neighbors[neighbor] = bts\n",
    "\n",
    "        assigned = set()\n",
    "        for neighbor, bts in self.assigned_neighbors.items():\n",
    "            assigned.add(deepcopy(bts))\n",
    "\n",
    "        self.bts = list(set(assigned))\n",
    "\n",
    "    def mutate(self):\n",
    "        # Mutate the chromosome by adding or removing BTS objects and changing their positions\n",
    "        rand = np.random.randint(1, len(list_of_neighbors))\n",
    "\n",
    "        # Add or remove BTS objects if it's needed\n",
    "        if len(self.bts) < rand:\n",
    "            while len(self.bts) < rand:\n",
    "                new_gen_bts = BTS(np.random.uniform(0, GRIDS), np.random.uniform(0, GRIDS), np.random.uniform(\n",
    "                    config['user_satisfaction_levels'][0] * min_population, MAX_BANDWIDTH_FOR_A_BTS))\n",
    "                if new_gen_bts not in self.bts:\n",
    "                    self.bts.append(new_gen_bts)\n",
    "        else:\n",
    "            self.bts = random.sample(self.bts, k=rand)\n",
    "\n",
    "        u = np.random.normal(0, 1)\n",
    "\n",
    "        # Calculate standard deviation of x coordinates of BTS objects\n",
    "        std_dev_x_bts = np.std([b.x for b in self.bts]) * \\\n",
    "            np.exp(u/np.sqrt(len(self.bts)))\n",
    "\n",
    "        # Calculate standard deviation of y coordinates of BTS objects\n",
    "        std_dev_y_bts = np.std([b.y for b in self.bts]) * \\\n",
    "            np.exp(u/np.sqrt(len(self.bts)))\n",
    "\n",
    "        # Calculate standard deviation of bandwidth of BTS objects\n",
    "        std_dev_bandwidth_bts = np.std(\n",
    "            [b.bandwidth for b in self.bts]) * np.exp(u/np.sqrt(len(self.bts)))\n",
    "\n",
    "        for b in self.bts:\n",
    "            # location and bandwidth Gaussian mutation\n",
    "            b.x += np.random.normal(0, std_dev_x_bts)\n",
    "            b.x = max(b.x, 0)\n",
    "            b.x = min(b.x, GRIDS)\n",
    "\n",
    "            b.y += np.random.normal(0, std_dev_y_bts)\n",
    "            b.y = max(b.y, 0)\n",
    "            b.y = min(b.y, GRIDS)\n",
    "\n",
    "            b.bandwidth += np.random.normal(0, std_dev_bandwidth_bts)\n",
    "            b.bandwidth = max(\n",
    "                b.bandwidth, (config['user_satisfaction_levels'][0] * min_population))\n",
    "            b.bandwidth = min(b.bandwidth, MAX_BANDWIDTH_FOR_A_BTS)\n",
    "\n",
    "        # neighbors\n",
    "        self.assign_bts()\n",
    "\n",
    "    def recombinate(self, other):\n",
    "        # Recombinate the chromosome with another chromosome\n",
    "\n",
    "        # Deep copy the assigned neighbors\n",
    "        s_a_n = deepcopy(self.assigned_neighbors)\n",
    "        o_a_n = deepcopy(other.assigned_neighbors)\n",
    "\n",
    "        # Calculate the number of BTS objects in the offspring\n",
    "        number_of_bts = int(\n",
    "            (2 * len(self.bts) * len(other.bts)) / (len(self.bts) + len(other.bts)))\n",
    "        \n",
    "        # Calculate the cross over point\n",
    "        cross_over_point_self = np.random.randint(0, len(self.bts))\n",
    "        cross_over_point_other = np.random.randint(0, len(other.bts))\n",
    "\n",
    "        # One-point crossover the BTS objects\n",
    "        bts_offspring1 = []\n",
    "        bts_offspring1.extend(self.bts[:cross_over_point_self])\n",
    "        bts_offspring1.extend(other.bts[cross_over_point_other:])\n",
    "\n",
    "        random.shuffle(bts_offspring1)\n",
    "        bts_offspring1 = bts_offspring1[:number_of_bts]\n",
    "\n",
    "        bts_offspring2 = []\n",
    "        bts_offspring2.extend(other.bts[:cross_over_point_other])\n",
    "        bts_offspring2.extend(self.bts[cross_over_point_self:])\n",
    "\n",
    "        random.shuffle(bts_offspring2)\n",
    "        bts_offspring2 = bts_offspring2[:number_of_bts]\n",
    "\n",
    "        bts_offspring1 = list(set(bts_offspring1))\n",
    "        bts_offspring2 = list(set(bts_offspring2))\n",
    "\n",
    "        # Assigns a new BTS object to the neighbor if it is not already assigned\n",
    "        for neighbor in s_a_n:\n",
    "            if s_a_n[neighbor] not in bts_offspring1:\n",
    "                s_a_n[neighbor] = min(bts_offspring1, key=lambda b: np.sqrt(\n",
    "                    np.power(b.x - neighbor.x, 2) + np.power(b.y - neighbor.y, 2)))\n",
    "        # Remove the BTS objects that are not assigned to any neighbor\n",
    "        assigned = set()\n",
    "        for neighbor, bts in s_a_n.items():\n",
    "            assigned.add(deepcopy(bts))\n",
    "        bts_offspring1 = list(set(assigned))\n",
    "\n",
    "        # Assigns a new BTS object to the neighbor if it is not already assigned\n",
    "        for neighbor in o_a_n:\n",
    "            if o_a_n[neighbor] not in bts_offspring2:\n",
    "                o_a_n[neighbor] = min(bts_offspring2, key=lambda b: np.sqrt(\n",
    "                    np.power(b.x - neighbor.x, 2) + np.power(b.y - neighbor.y, 2)))\n",
    "\n",
    "        # Remove the BTS objects that are not assigned to any neighbor\n",
    "        assigned = set()\n",
    "        for neighbor, bts in o_a_n.items():\n",
    "            assigned.add(deepcopy(bts))\n",
    "        bts_offspring2 = list(set(assigned))\n",
    "\n",
    "        # Create the offspring\n",
    "        offspring1, offspring2 = Chromosome(s_a_n), Chromosome(o_a_n)\n",
    "\n",
    "        offspring1.bts = bts_offspring1\n",
    "        offspring2.bts = bts_offspring2\n",
    "\n",
    "        return offspring1, offspring2\n",
    "\n",
    "    def fitness_calculation(self):\n",
    "        # Calculate the fitness of the chromosome\n",
    "        X, Y = 1, -1\n",
    "\n",
    "        # Calculates satisfaction of each neighbor and adds it to the total satisfaction\n",
    "        self.satisfaction = 0\n",
    "        for neighbor, bts in self.assigned_neighbors.items():\n",
    "            ls_n = [n.population for n,\n",
    "                    b in self.assigned_neighbors.items() if b == bts]\n",
    "            self.satisfaction += neighbor.calculate_satisfaction(\n",
    "                bts, sum(ls_n))\n",
    "\n",
    "        # Calculates the cost of all BTSs\n",
    "        self.cost = sum(b.cost for b in self.bts)\n",
    "        # Calculates fitness using satisfaction and cost\n",
    "        self.fitness = (X*(self.satisfaction)) + (Y*(self.cost))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Evolutionary_Algorithm:\n",
    "    def __init__(self, population_size=50, max_generation=200, p_mutation=0.1, p_crossover=0.1):\n",
    "        # Initializes the class with default values for population size, maximum generation,\n",
    "        # mutation probability and crossover probability\n",
    "        self.population_size = population_size\n",
    "        self.max_generation = max_generation\n",
    "        self.p_mutation = p_mutation\n",
    "        self.p_crossover = p_crossover\n",
    "        self.population = []\n",
    "        self.offsprings = []\n",
    "        self.fitness_history = []\n",
    "        self.best = None\n",
    "\n",
    "    def initialize(self):\n",
    "        # Clears the population list and initializes it with new chromosomes\n",
    "        self.population.clear()\n",
    "\n",
    "        for __ in tqdm(range(self.population_size), colour='black', desc='Initializing'):\n",
    "            chromosome = Chromosome()\n",
    "            chromosome.initialize()\n",
    "            self.population.append(chromosome)\n",
    "\n",
    "        # Calculates the fitness of the chromosomes\n",
    "        for ch in self.population:\n",
    "            ch.fitness_calculation()\n",
    "\n",
    "        # Finds the best chromosome\n",
    "        self.best = deepcopy(max(self.population, key=lambda x: x.fitness))\n",
    "\n",
    "    def mutation(self):\n",
    "        # Mutates the offsprings with a probability of p_mutation\n",
    "        for chromosome in self.offsprings:\n",
    "            p_m = np.random.random()\n",
    "            if p_m <= self.p_mutation:\n",
    "                chromosome.mutate()\n",
    "\n",
    "    def recombination(self, p1, p2):\n",
    "        # Recombines two parents with a probability of p_crossover\n",
    "        if np.random.random() <= self.p_crossover:\n",
    "            offspring_1, offspring_2 = p1.recombinate(p2)\n",
    "            self.offsprings.append(offspring_1)\n",
    "            self.offsprings.append(offspring_2)\n",
    "        # If the parents are not recombined, they are added to the offsprings list\n",
    "        else:\n",
    "            self.offsprings.append(deepcopy(p1))\n",
    "            self.offsprings.append(deepcopy(p2))\n",
    "\n",
    "    def tournament_selection(self):\n",
    "        # Selects the best chromosome from a random tournament of 5 chromosomes\n",
    "        tournament = np.random.choice(self.population, 5, replace=True)\n",
    "        return max(tournament, key=lambda x: x.fitness)\n",
    "\n",
    "    def generate_next_generation(self):\n",
    "        # Generates the next generation\n",
    "\n",
    "        # Clears the offsprings list\n",
    "        self.offsprings.clear()\n",
    "\n",
    "        # Generates offsprings by recombining two parents selected by tournament selection\n",
    "        for i in range(self.population_size // 2):\n",
    "            parent1 = self.tournament_selection()\n",
    "            parent2 = self.tournament_selection()\n",
    "            self.recombination(parent1, parent2)\n",
    "\n",
    "        # Mutates the offsprings\n",
    "        self.mutation()\n",
    "\n",
    "        # Calculates fitness of the offsprings\n",
    "        for ch in self.offsprings:\n",
    "            ch.fitness_calculation()\n",
    "\n",
    "        # Adds offsprings to the population and selects the best chromosomes\n",
    "        self.population.extend(self.offsprings)\n",
    "        self.population = sorted(self.population, key=lambda p: p.fitness, reverse=True)[\n",
    "            :self.population_size]\n",
    "\n",
    "    def run(self, verbose=True):\n",
    "        # Initializes the population and runs the evolutionary algorithm for max_generation generations\n",
    "        self.initialize()\n",
    "\n",
    "        \n",
    "        for i in tqdm(range(self.max_generation), desc='Running Evolutionary Algorithm'):\n",
    "            self.generate_next_generation()\n",
    "            self.fitness_history.append(self.mean_fitness())\n",
    "            best_of_generation = max(\n",
    "                self.population, key=lambda x: x.fitness)\n",
    "\n",
    "            if best_of_generation.fitness > self.best.fitness:\n",
    "                self.best = deepcopy(best_of_generation)\n",
    "\n",
    "            if verbose:\n",
    "                print(\n",
    "                    f'Generation: {i+1}, Mean Fitness: {self.fitness_history[-1]}')\n",
    "                print('*************************')\n",
    "                print(f'Best Fitness so far: {self.best}')\n",
    "                print('*************************')\n",
    "                print(\n",
    "                    f'Mean Cost: {self.mean_cost()}, Mean Satisfaction: {self.mean_satisfaction()}')\n",
    "                print(\n",
    "                    f'Mean Number of BTS: {np.mean([len(ch.bts) for ch in self.population])}')\n",
    "                print(f'Mean Bandwidth of BTS: {self.mean_bandwidth()}')\n",
    "                print(\n",
    "                    \"------------------------------------------------------------------------------\")\n",
    "\n",
    "\n",
    "    def mean_fitness(self):\n",
    "        # Returns the mean fitness of the population\n",
    "        return np.mean([ch.fitness for ch in self.population])\n",
    "\n",
    "    def mean_cost(self):\n",
    "        # Returns the mean cost of the population\n",
    "        return np.mean([ch.cost for ch in self.population])\n",
    "\n",
    "    def mean_satisfaction(self):\n",
    "        # Returns the mean satisfaction of the population\n",
    "        return np.mean([ch.satisfaction for ch in self.population])\n",
    "\n",
    "    def mean_bandwidth(self):\n",
    "        # Returns the mean bandwidth of all BTSs in the population\n",
    "        bms = []\n",
    "        for ch in self.population:\n",
    "            bms.extend([c.bandwidth for c in ch.bts])\n",
    "        return np.mean(bms)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad4db27f06a749c88c5b0e060d73c537",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Initializing:   0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b01370f395ef4d6fbd409be16f0f3386",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running Evolutionary Algorithm:   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cfab35d9f9f847f68d70863b3bf0d1bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Initializing:   0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7522bdc636104ec4bfcd48e7ea7ffc2f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running Evolutionary Algorithm:   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "POPULATION_SIZE = 50\n",
    "MAX_GENERATION = 200\n",
    "\n",
    "fitness_history_per_exe = []\n",
    "time_history_per_exe = []\n",
    "best_per_exe = []\n",
    "\n",
    "# Run the evolutionary algorithm for MAX_EXE times\n",
    "for i in range(MAX_EXE):\n",
    "    # Create a new instance of the Evolutionary_Algorithm class\n",
    "    ea = Evolutionary_Algorithm(population_size=POPULATION_SIZE, max_generation=MAX_GENERATION, p_mutation=0.1, p_crossover=0.9)\n",
    "\n",
    "    # Record the start and end time of the execution and run the evolutionary algorithm\n",
    "    start_time = time.time()\n",
    "    ea.run(verbose=False)\n",
    "    end_time = time.time()\n",
    "\n",
    "    # Append the fitness history, execution time and best chromosome of this execution to their respective lists\n",
    "    fitness_history_per_exe.append(ea.fitness_history)\n",
    "    time_history_per_exe.append(end_time - start_time)\n",
    "    best_per_exe.append(ea.best)\n",
    "\n",
    "# Convert the fitness history and execution time lists to pandas dataframes\n",
    "fitness_history_per_exe = pd.DataFrame(fitness_history_per_exe)\n",
    "time_history_per_exe = pd.DataFrame(time_history_per_exe)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the mean fitness values for each generation\n",
    "x = fitness_history_per_exe.mean().index.to_list()\n",
    "y = fitness_history_per_exe.mean().values\n",
    "\n",
    "# Create a new figure and axis object and Plot the mean fitness values over generations\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y)\n",
    "ax.fill_between(x, y.min(), y.max(), alpha=0.3)\n",
    "plt.xlabel('Generation')\n",
    "plt.ylabel('Fitness')\n",
    "plt.show()\n",
    "fig.savefig('plot_0.1_0.9.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N_BTS: 6, Cost: 2102597.5216493937, Satisfaction: 6377810, Fitness: 4275212.478350606\n",
      "{Neighbor(0.5, 0.5, 196): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(0.5, 1.5, 887): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(0.5, 2.5, 783): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(0.5, 3.5, 254): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(0.5, 4.5, 678): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(0.5, 5.5, 510): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(0.5, 6.5, 56): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(0.5, 7.5, 310): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(0.5, 8.5, 217): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(0.5, 9.5, 127): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(0.5, 10.5, 74): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(0.5, 11.5, 714): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(0.5, 12.5, 133): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(0.5, 13.5, 356): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(0.5, 14.5, 366): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(0.5, 15.5, 455): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(0.5, 16.5, 651): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(0.5, 17.5, 350): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(0.5, 18.5, 477): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(0.5, 19.5, 673): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(1.5, 0.5, 697): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(1.5, 1.5, 679): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(1.5, 2.5, 566): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(1.5, 3.5, 71): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(1.5, 4.5, 121): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(1.5, 5.5, 19): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(1.5, 6.5, 341): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(1.5, 7.5, 565): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(1.5, 8.5, 41): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(1.5, 9.5, 499): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(1.5, 10.5, 228): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(1.5, 11.5, 290): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(1.5, 12.5, 687): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(1.5, 13.5, 923): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(1.5, 14.5, 936): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(1.5, 15.5, 619): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(1.5, 16.5, 192): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(1.5, 17.5, 809): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(1.5, 18.5, 910): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(1.5, 19.5, 347): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(2.5, 0.5, 513): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(2.5, 1.5, 549): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(2.5, 2.5, 830): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(2.5, 3.5, 960): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(2.5, 4.5, 747): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(2.5, 5.5, 207): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(2.5, 6.5, 686): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(2.5, 7.5, 126): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(2.5, 8.5, 907): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(2.5, 9.5, 39): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(2.5, 10.5, 450): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(2.5, 11.5, 49): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(2.5, 12.5, 173): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(2.5, 13.5, 29): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(2.5, 14.5, 32): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(2.5, 15.5, 235): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(2.5, 16.5, 193): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(2.5, 17.5, 98): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(2.5, 18.5, 288): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(2.5, 19.5, 937): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(3.5, 0.5, 259): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(3.5, 1.5, 983): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(3.5, 2.5, 25): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(3.5, 3.5, 316): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(3.5, 4.5, 70): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(3.5, 5.5, 925): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(3.5, 6.5, 196): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(3.5, 7.5, 529): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(3.5, 8.5, 995): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(3.5, 9.5, 556): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(3.5, 10.5, 555): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(3.5, 11.5, 801): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(3.5, 12.5, 738): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(3.5, 13.5, 320): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(3.5, 14.5, 865): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(3.5, 15.5, 520): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(3.5, 16.5, 352): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(3.5, 17.5, 687): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(3.5, 18.5, 925): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(3.5, 19.5, 497): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(4.5, 0.5, 654): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(4.5, 1.5, 174): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(4.5, 2.5, 69): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(4.5, 3.5, 500): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(4.5, 4.5, 90): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(4.5, 5.5, 996): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(4.5, 6.5, 286): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(4.5, 7.5, 255): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(4.5, 8.5, 501): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(4.5, 9.5, 334): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(4.5, 10.5, 364): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(4.5, 11.5, 236): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(4.5, 12.5, 242): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(4.5, 13.5, 693): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(4.5, 14.5, 780): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(4.5, 15.5, 655): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(4.5, 16.5, 493): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(4.5, 17.5, 319): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(4.5, 18.5, 615): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(4.5, 19.5, 559): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(5.5, 0.5, 197): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(5.5, 1.5, 142): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(5.5, 2.5, 639): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(5.5, 3.5, 101): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(5.5, 4.5, 815): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(5.5, 5.5, 680): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(5.5, 6.5, 552): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(5.5, 7.5, 62): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(5.5, 8.5, 950): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(5.5, 9.5, 721): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(5.5, 10.5, 480): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(5.5, 11.5, 512): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(5.5, 12.5, 966): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(5.5, 13.5, 899): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(5.5, 14.5, 415): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(5.5, 15.5, 975): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(5.5, 16.5, 911): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(5.5, 17.5, 694): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(5.5, 18.5, 194): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(5.5, 19.5, 451): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(6.5, 0.5, 235): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(6.5, 1.5, 196): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(6.5, 2.5, 50): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(6.5, 3.5, 986): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(6.5, 4.5, 277): BTS(0, 3.1002660193338, 165938.99163376424), Neighbor(6.5, 5.5, 612): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(6.5, 6.5, 277): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(6.5, 7.5, 549): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(6.5, 8.5, 827): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(6.5, 9.5, 533): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(6.5, 10.5, 182): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(6.5, 11.5, 875): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(6.5, 12.5, 540): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(6.5, 13.5, 687): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(6.5, 14.5, 270): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(6.5, 15.5, 400): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(6.5, 16.5, 688): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(6.5, 17.5, 259): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(6.5, 18.5, 856): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(6.5, 19.5, 105): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(7.5, 0.5, 504): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(7.5, 1.5, 34): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(7.5, 2.5, 270): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(7.5, 3.5, 71): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(7.5, 4.5, 127): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(7.5, 5.5, 902): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(7.5, 6.5, 546): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(7.5, 7.5, 430): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(7.5, 8.5, 310): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(7.5, 9.5, 641): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(7.5, 10.5, 342): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(7.5, 11.5, 259): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(7.5, 12.5, 116): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(7.5, 13.5, 257): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(7.5, 14.5, 377): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(7.5, 15.5, 499): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(7.5, 16.5, 693): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(7.5, 17.5, 70): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(7.5, 18.5, 922): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(7.5, 19.5, 630): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(8.5, 0.5, 941): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(8.5, 1.5, 490): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(8.5, 2.5, 652): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(8.5, 3.5, 767): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(8.5, 4.5, 289): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(8.5, 5.5, 168): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(8.5, 6.5, 382): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(8.5, 7.5, 622): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(8.5, 8.5, 362): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(8.5, 9.5, 208): BTS(4.142069529446905, 11.363800852695494, 438150.8600035899), Neighbor(8.5, 10.5, 372): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(8.5, 11.5, 203): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(8.5, 12.5, 209): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(8.5, 13.5, 130): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(8.5, 14.5, 65): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(8.5, 15.5, 657): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(8.5, 16.5, 194): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(8.5, 17.5, 954): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(8.5, 18.5, 267): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(8.5, 19.5, 32): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(9.5, 0.5, 121): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(9.5, 1.5, 563): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(9.5, 2.5, 606): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(9.5, 3.5, 521): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(9.5, 4.5, 142): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(9.5, 5.5, 29): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(9.5, 6.5, 386): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(9.5, 7.5, 186): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(9.5, 8.5, 304): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(9.5, 9.5, 288): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(9.5, 10.5, 343): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(9.5, 11.5, 945): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(9.5, 12.5, 70): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(9.5, 13.5, 107): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(9.5, 14.5, 485): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(9.5, 15.5, 564): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(9.5, 16.5, 790): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(9.5, 17.5, 580): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(9.5, 18.5, 9): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(9.5, 19.5, 101): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(10.5, 0.5, 93): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(10.5, 1.5, 608): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(10.5, 2.5, 595): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(10.5, 3.5, 60): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(10.5, 4.5, 205): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(10.5, 5.5, 682): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(10.5, 6.5, 708): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(10.5, 7.5, 897): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(10.5, 8.5, 747): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(10.5, 9.5, 571): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(10.5, 10.5, 506): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(10.5, 11.5, 918): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(10.5, 12.5, 571): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(10.5, 13.5, 846): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(10.5, 14.5, 542): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(10.5, 15.5, 221): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(10.5, 16.5, 270): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(10.5, 17.5, 390): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(10.5, 18.5, 301): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(10.5, 19.5, 558): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(11.5, 0.5, 587): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(11.5, 1.5, 486): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(11.5, 2.5, 185): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(11.5, 3.5, 212): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(11.5, 4.5, 37): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(11.5, 5.5, 276): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(11.5, 6.5, 338): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(11.5, 7.5, 987): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(11.5, 8.5, 29): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(11.5, 9.5, 753): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(11.5, 10.5, 116): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(11.5, 11.5, 966): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(11.5, 12.5, 923): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(11.5, 13.5, 905): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(11.5, 14.5, 896): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(11.5, 15.5, 120): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(11.5, 16.5, 94): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(11.5, 17.5, 781): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(11.5, 18.5, 727): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(11.5, 19.5, 924): BTS(6.239247507542247, 15.164085269675564, 366072.77661707875), Neighbor(12.5, 0.5, 289): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(12.5, 1.5, 770): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(12.5, 2.5, 493): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(12.5, 3.5, 730): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(12.5, 4.5, 559): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(12.5, 5.5, 82): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(12.5, 6.5, 528): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(12.5, 7.5, 635): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(12.5, 8.5, 94): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(12.5, 9.5, 217): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(12.5, 10.5, 264): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(12.5, 11.5, 246): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(12.5, 12.5, 183): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(12.5, 13.5, 574): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(12.5, 14.5, 901): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(12.5, 15.5, 283): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(12.5, 16.5, 562): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(12.5, 17.5, 349): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(12.5, 18.5, 191): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(12.5, 19.5, 691): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(13.5, 0.5, 64): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(13.5, 1.5, 935): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(13.5, 2.5, 597): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(13.5, 3.5, 649): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(13.5, 4.5, 215): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(13.5, 5.5, 503): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(13.5, 6.5, 841): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(13.5, 7.5, 771): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(13.5, 8.5, 985): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(13.5, 9.5, 383): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(13.5, 10.5, 333): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(13.5, 11.5, 169): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(13.5, 12.5, 647): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(13.5, 13.5, 322): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(13.5, 14.5, 801): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(13.5, 15.5, 314): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(13.5, 16.5, 860): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(13.5, 17.5, 277): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(13.5, 18.5, 732): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(13.5, 19.5, 363): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(14.5, 0.5, 687): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(14.5, 1.5, 198): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(14.5, 2.5, 472): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(14.5, 3.5, 770): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(14.5, 4.5, 134): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(14.5, 5.5, 814): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(14.5, 6.5, 526): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(14.5, 7.5, 652): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(14.5, 8.5, 125): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(14.5, 9.5, 564): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(14.5, 10.5, 582): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(14.5, 11.5, 978): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(14.5, 12.5, 621): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(14.5, 13.5, 968): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(14.5, 14.5, 616): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(14.5, 15.5, 883): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(14.5, 16.5, 718): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(14.5, 17.5, 984): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(14.5, 18.5, 581): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(14.5, 19.5, 415): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(15.5, 0.5, 891): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(15.5, 1.5, 491): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(15.5, 2.5, 678): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(15.5, 3.5, 960): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(15.5, 4.5, 190): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(15.5, 5.5, 375): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(15.5, 6.5, 550): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(15.5, 7.5, 709): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(15.5, 8.5, 695): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(15.5, 9.5, 551): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(15.5, 10.5, 324): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(15.5, 11.5, 478): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(15.5, 12.5, 765): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(15.5, 13.5, 55): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(15.5, 14.5, 46): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(15.5, 15.5, 358): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(15.5, 16.5, 29): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(15.5, 17.5, 214): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(15.5, 18.5, 793): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(15.5, 19.5, 54): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(16.5, 0.5, 970): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(16.5, 1.5, 220): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(16.5, 2.5, 176): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(16.5, 3.5, 516): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(16.5, 4.5, 260): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(16.5, 5.5, 416): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(16.5, 6.5, 48): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(16.5, 7.5, 503): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(16.5, 8.5, 293): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(16.5, 9.5, 953): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(16.5, 10.5, 605): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(16.5, 11.5, 202): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(16.5, 12.5, 996): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(16.5, 13.5, 198): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(16.5, 14.5, 757): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(16.5, 15.5, 573): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(16.5, 16.5, 219): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(16.5, 17.5, 386): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(16.5, 18.5, 131): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(16.5, 19.5, 555): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(17.5, 0.5, 763): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(17.5, 1.5, 811): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(17.5, 2.5, 934): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(17.5, 3.5, 703): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(17.5, 4.5, 645): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(17.5, 5.5, 518): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(17.5, 6.5, 118): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(17.5, 7.5, 386): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(17.5, 8.5, 297): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(17.5, 9.5, 260): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(17.5, 10.5, 303): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(17.5, 11.5, 739): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(17.5, 12.5, 595): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(17.5, 13.5, 272): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(17.5, 14.5, 966): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(17.5, 15.5, 725): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(17.5, 16.5, 615): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(17.5, 17.5, 699): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(17.5, 18.5, 731): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(17.5, 19.5, 59): BTS(11.006805610614965, 13.88826615614587, 362594.6124036408), Neighbor(18.5, 0.5, 490): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(18.5, 1.5, 253): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(18.5, 2.5, 933): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(18.5, 3.5, 32): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(18.5, 4.5, 926): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(18.5, 5.5, 693): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(18.5, 6.5, 398): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(18.5, 7.5, 531): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 8.5, 494): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 9.5, 657): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 10.5, 513): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 11.5, 389): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 12.5, 820): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 13.5, 516): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 14.5, 391): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 15.5, 748): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 16.5, 42): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 17.5, 65): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 18.5, 362): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(18.5, 19.5, 849): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 0.5, 683): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(19.5, 1.5, 10): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(19.5, 2.5, 391): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(19.5, 3.5, 195): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(19.5, 4.5, 259): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(19.5, 5.5, 712): BTS(13.531274292103726, 4.898285830303497, 698721.362553535), Neighbor(19.5, 6.5, 799): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 7.5, 206): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 8.5, 216): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 9.5, 712): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 10.5, 871): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 11.5, 193): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 12.5, 967): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 13.5, 466): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 14.5, 800): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 15.5, 489): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 16.5, 477): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 17.5, 997): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 18.5, 610): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586), Neighbor(19.5, 19.5, 486): BTS(18.570164711591772, 10.580093569218663, 244650.14700361586)}\n"
     ]
    }
   ],
   "source": [
    "# Find the best chromosome across all executions based on fitness value\n",
    "max_best_per_exe = max(best_per_exe, key=lambda x: x.fitness)\n",
    "print(max_best_per_exe)\n",
    "print(max_best_per_exe.assigned_neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('log_0.1_0.9.txt', 'w') as w:\n",
    "    w.write(f\"{max_best_per_exe}\\n\")\n",
    "    w.write(f\"--------------------------------------------\\n\")\n",
    "    w.write(f\"{max_best_per_exe.assigned_neighbors}\\n\")\n",
    "    w.write(f\"--------------------------------------------\\n\")\n",
    "    w.write(f\"{max_best_per_exe.bts}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "version": 3
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   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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