diff --git a/P0. Data preparation.ipynb b/P0. Data preparation.ipynb
index 58e5794..e905e56 100644
--- a/P0. Data preparation.ipynb	
+++ b/P0. Data preparation.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -137,7 +137,7 @@
        "4   166   346       1  886397596"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -184,12 +184,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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l6UFF1zGVohFl29unRnf2jdx9jbv/VNE5ulI0WNB+nV1f7P2SRrSzzCfKHt/fat4d8X0fSUd0MUtPebnscZv/BppZH0XXIu0Ud18u6Tht+CFspJltVrbIHWWPEyODA0Bn5fGLDIBimKMNl5WYoGiQm/Y0X79uJzOr1lPzza4Ey8gYM6s2Eu43FX1BlqT/K58RX17n+vjphxVdHzNvrip7/J12erG+28br8qL5fMXt2un1PavKvGblh4129NDmHhcPcHVj/HSIpKPbWtbMRquNkXc3UlPZ4+44najN62Ca2UBtuAbuWm34+2l2Wdnjs8wsN9umTPn1O6v9qDVB7f84UlV8NMCysknl2+cKbThq5VvxD2gA0CUUpQCCiEfKbD7/ziR9owMvK/8ieW6lQsfMfqANh1vmWV9Jf4y/LCeY2RGSvhM/fUPR+bet/Zc29Gz9sr0BY8xsRzM7z8y2q7ZcN1qgDYP+HCjpvEq9b2b2fW3omXpG0uXZxNsozb1qJumc1jMt8gNFg8S058myx+317GVtRtnjX5nZ3q0XiK8zeWm1lZjZcDM708wGVVlmW20ofF3Vr3vaUf/PzFLnp8cF5hxtGKDn4tY93u7+d234QeTDkua1k7+vmR1pZl9va5kecJM2XLv4JDNLHSpuZiW1c61hM/uSmf17q97P1suMkjQ8fvpE+Qja7v5M2XtsI+mGaqdgxH8fh9Tw+dYAMsBARwCCcff5ZvY3RYdvdqRn4hJFl5DZWtLnJd1pZpcruhTGjop6CEqKfsmf0COhu881ioqYR8xslqR/KBqs5TBFX9abC+7vxl8CE9x9oZl9TdIsRYc//s7Mvi3pWkWXvnhL0cAtuysqCveP1/mLHvtEyXzvmtmxku5WNKjNtyV9It5eyxQdEvsFRQPfSFGB/eVuvCRNd/qNpBMU9Vx/08zqFfVePytpB0Uj8A5X1JO1VtLIKuu6U9Fn7SfpdDNrLsiazxd80d2DHMLs7jeb2aWSjld0TuL98fO/KTrsuKSoHd4n6UpFf4NSetTf9ys6JH+qmd0dv/5RRQNYbS1pH0VttnW8/OXu/nQX4y9StL9fGF8382pFh7s2H0nQXMA9qQ0/hrV2Qrz8PooO9X3CzK5UdO3e5xVdRmewoh8TPhXnv7iLuTvM3VeY2R8kHRu/9/1m9htF+90ARYdUT5T0kqJrsrbVm7qrpKmKfsy6SdGPLs8o2ge3UzTo0ZHacKRGpUGovqdodOVDFPWaLzazaxWdWvCsov17kKIjYA5V1Pt+i6QfdeazA+j9KEoBhPY9Jc9RapO7r457BP9P0RfEA+NbufmKvoTmvSj9haIBQ06S9P0K813SD9z9V22twN0vjq8tOEvRF8D6+NaWFyRlVvS5+wNmdoiiHqjmL/OVegdflPT/Wo3wmRvuvsjMviHpV4qOMDoovpX7p6TPSPptO+t63szOV7TfD1D6fOo71IXzNbvBZEW5Pq/ob+yrSo6O/a6iXvxXtKEobT1acnOR2keV26rcn+L37KpXJP27or//w+Jba0slfcrdX620And/1cwOVPT39EVFPxK1dx3g1gMm9bRTFRXN9YoO0Z3aav5KRed5fq3KOpq3z3u14Rqylbwj6Ux3TxXe7v6OmR2u6PIxX1NUhH5eG/aJSrgOKYA2cfgugKDc/a9Kn99Vbfm/KPpC9r+SnlZ0btNqSbcp6kEY7+4duWZhcO5+sqSxkuYp+nL7dnz/R0kHuvu0DqxjnqSdFRUOcxX1eKzVhna5R9GhdkdIGuLuz3f7B6me7x5FPTOnKSq4Viv6svtCnO0MSbu4+w1Z5tpY7n6hoh9A/qyoJ+gdSc8p6gU8TVLJ3R/v4Lq+r6hH6/p4XbkZbdjd33H3oyUdpSjfakU9aE8rOrT6QHf/maLDNpu92Godf5W0m6Ji5U+Slig6l/bd+H6xoqMeDnb3L7p7t1yf0t0XKeqxPkfSw4qK5TWSHlD0w88wd3+inXW85u4TFP14MiN+7QuKRl5eI+kxRUc5nKZov+3IecTdxt1fVHRkyZQ42xpFl/NZrKgXct/4UORqfiRplKI2uV7Rub1rFX3GlxQNNnaupD3d/dwqWd52928oOhpjuqS/K9pf1ik67eBJSddpQ9sft/GfGEBRWHRaFwAAQMeY2VWSPhc/3SYulkLkaP4Sc4e7jw6RAQDQdfSUAgCADosHOxoXP30wVEEKAOg9KEoBAIAkycx2MbPtq8wfqmgQof7xpIsyCQYA6NUY6AgAADQ7QNL/mtlfFY0UvFTR+YbbKDoP8QuKBgCSpHslzQwREgDQu1CUAgCAcn0VXU6krUuKSNLtko5y9/VVlgEAoEMoSgEAQLO5kr4kaYyikWy3VXRNzLclrVI0wuoV8ajPAAB0i1yMvrvtttt6XV1d6Bg9ZvXq1Ro4cGDoGC3ylqcoaHcAAAAUVWNj4/PuXvHLcC56Suvq6tTQ0BA6BgAAAACgB5jZU23NY/TdDEybNi10hIS85SkK2h0AAABIy8Xhu6VSyXtzT6mZKQ/t3CxveYqCdgcAAEBRmVmju5cqzaOnFAAAAAAQDEUpAAAAACAYitIM5O3Q5LzlKQraHQAAAEijKAUAAAAABMNARxnI2wA3ectTFLQ7AAAAioqBjgAAAAAAudRuUWpml5jZc2b2cNm0P5rZovjWZGaL4ul1Zra2bN7/9GB2AAAAAECN69uBZS6V9CtJlzVPcPcvNj82s59JeqVs+aXuXt9N+XqFqVOnho6QkLc8RUG7AwAAAGkdOqfUzOokzXf3vVtNN0lPS/qkuz/W1nLt6e3nlAIAAABAkfXkOaUfl7TK3R8rm7azmT1gZneY2cerhJpsZg1m1rB69eouxsi3IUOGhI6QkLc8RUG7AwAAAGldLUonSppT9nylpB3dfbik0yT9wczeV+mF7j7T3UvuXho4cGAXY+TbypUrWx4/88wz+sQnPqE99thDe+21l37xi1+0zJs2bZqGDh2q+vp61dfX67rrrpMk3X333Ro2bJj2228/Pf7445Kkl19+WYcddlinRnMtz9MV11xzjRYvXtzy/KyzztLNN98sSRo9enSnr8t57bXXatiwYaqvr1epVNJdd90lSXrzzTe1//77a99999Vee+1Vc4fDttfu//rXv1q2fX19vd73vvdpxowZkrLbN7pLT+0bS5Ys0QEHHKBNN91U559/fmr++vXrNXz4cI0bN65l2qJFizRq1KiW/em+++7r1HsDAACgh7h7uzdJdZIebjWtr6RVkrav8rrbJZXaW//IkSO9N4uaObJixQpvbGx0d/dXX33Vd911V3/kkUfc3X3q1Kl+3nnnpV7/2c9+1h999FG/8cYb/bTTTnN399NOO81vv/32LufpiuOOO87//Oc/V5x38MEH+/3339+p9b722mv+7rvvurv7gw8+6Lvttpu7u7/77rv+2muvubv722+/7fvvv7/fc889nXqPEDam3detW+eDBg3ypqYmd89u3+guPbVvrFq1yu+77z7//ve/X7E9fvazn/nEiRN97NixLdMOPfRQv+6669zdfcGCBX7wwQd36r0BAADQeZIavI16sCs9pf8maYm7L2ueYGYDzaxP/PiDknaV9EQX3qNXGDFiRMvjwYMHtzzfYosttMcee2j58uVVX9+vXz+tXbtWb7zxhvr166elS5dq+fLlOvjgg9t8zfXXX6/dd99dH/vYx/TNb36zpeeoucet2d57762mpiZJ0pFHHqmRI0dqr7320syZM1uWGTBggM444wztu+++GjVqlFatWqW//e1vmjt3rk4//XTV19dr6dKlOv7443XllVemstx444064IADNGLECB199NFas2ZN1c87YMAARacrS6+//nrLYzPTgAEDJEnvvPOO3nnnnZZ5taB8P2jPLbfcol122UU77bRT1eW6e98o733M476x3Xbbab/99lO/fv1S85YtW6YFCxboP/7jPxLTzUyvvvqqJOmVV17hMGoAAICc6cglYeZIukfSbma2zMy+Es+aoOShu5J0kKSHzOxBSVdK+qq7v9idgWtRY2NjxelNTU164IEH9JGPfKRl2q9+9SsNGzZMJ5xwgl566SVJ0ve+9z1NnjxZM2bM0Mknn6wzzjhDP/zhD9t8vzfffFOTJk3SvHnzdOedd+rZZ59NzD/11FMrvu6SSy5RY2OjGhoadMEFF+iFF16QFBWGo0aN0oMPPqiDDjpIs2bN0kc/+lGNHz9e5513nhYtWqRddtml4jqff/55nXPOObr55pu1cOFClUol/fd//7ek6JDOuXPnVnzd1Vdfrd13311jx47VJZdc0jJ9/fr1qq+v13bbbadDDz000XZ519Z+UMkVV1yhiRMnJqZlsW+0JU/7RltOPfVU/fSnP9UmmyT/WZsxY4ZOP/107bDDDvrOd76jn/zkJxu1XgAAAPSsdotSd5/o7oPdvZ+7b+/uF8fTj3f3/2m17FXuvpe77+vuI9x9Xk8FryWTJ09OTVuzZo2OOuoozZgxQ+97X3Ta7de+9jUtXbpUixYt0uDBg/Xtb39bklRfX697771Xt912m5544gkNGTJE7q4vfvGLOuaYY7Rq1arEupcsWaKdd95Zu+66q8xMxxxzTGJ+pR4rSbrgggtaeryeeeYZPfZYNH5V//79W3rTRo4c2dJ71hH33nuvFi9erAMPPFD19fWaPXu2nnrqKUnSD37wA40fP77i6z772c9qyZIluuaaa3TmmWe2TO/Tp48WLVqkZcuW6b777tPDDz9c8fV5VGk/qOTtt9/W3LlzdfTRR7dMy2rfaEue9o1K5s+fr+22204jR45Mzbvwwgv185//XM8884x+/vOf6ytf+UqFNQAAACCUrg50hA6YNWtW4vk777yjo446Sl/60pf0uc99rmX6oEGD1KdPH22yySaaNGlSakAWd9c555yjM888U2effbbOPvtsHXPMMbrgggtS79nWYa19+/bV3//+95bnb775piTp9ttv180336x77rlHDz74oIYPH94yr1+/fi3r69Onj9atW9fhz+7uOvTQQ7Vo0SItWrRIixcv1sUXX9zh1x900EFaunSpnn/++cT0LbfcUqNHj9b111/f4XWF1no/aMtf/vIXjRgxQoMGDWqZltW+8e6777Y8z/u+Ue7uu+/W3LlzVVdXpwkTJujWW29tKbhnz57d8nd29NFHM9ARAABAzlCUZszd9ZWvfEV77LGHTjvttMS88tFZr776au29d/Jyr7Nnz9bYsWO11VZb6Y033tAmm2yiTTbZRG+88UZiud13311PPvmkli5dKkmaM2fDUdZ1dXUtjxcuXKgnn3xSUnSu3VZbbaXNN99cS5Ys0b333tvuZ9liiy302muvVV1m1KhRuvvuu1tGhn3jjTf06KOPVn3N448/3jJy7MKFC/X2229rm2220erVq/Xyyy9LktauXaubb75Zu+++e7s5a82cOXNSh+5mtW8sXLhQUn73jbb85Cc/0bJly9TU1KQrrrhCn/zkJ/X73/9eUnQpnjvuuEOSdOutt2rXXXft1HsAAACgZ/QNHaBW1E1ZUHV+0/SxHVrP3Xffrd/97nfaZ599VF9fL0n68Y9/rMMPP1z/+Z//qUWLFsnMVFdXp4suuqjldW+88YZmz56tG2+8UZJ02mmn6aijjlL//v0ThYUkvec979HMmTM1duxYbbvttvrYxz7WcpjrUUcdpWOPPVb19fXab7/99OEPf1iSNGbMGP3P//yPhg0bpt12202jRo1q97NMmDBBkyZN0gUXXNDmIcEDBw7UpZdeqokTJ+qtt96SJJ1zzjn68Ic/rLPOOkulUil1mOZVV12lyy67TP369dNmm22mP/7xjzIzrVy5Uscdd5zWr1+vd999V1/4whcSl/7oDd544w3ddNNNiW0vKbN947LLLsv1vvHss8+qVCrp1Vdf1SabbKIZM2Zo8eLFLYfAVzJr1iydcsopWrduXcvnBwAAQH5Yc49USKVSyTt73cKsdKUoXbFiRdARP2+//Xadf/75mj9/fi7yFFUe2731vgEAAAD0BDNrdPdSpXkcvpuBjRl1NQt5y1MUtDsAAACQRk9pB3Wlp9TMlId2bpa3PEVBuwMAAKCo6CkFAAAAAOQSRSkAAAAAIBiK0gy0Hkk1tLzlKQraHQAAAEjjnNIO6q5LwgAAAABA0XBOaWBmFjpCQt7yFAXtDgAAAKRRlAIAAAAAgqEoBQAAAAAEQ1GagXHjxoWOkJC3PEVBuwMAAABpFKUZmDdvXugICXnLUxS0OwAAAJBGUZqBI444InSEhLzlKQraHQAAAEijKM3A/PnzQ0dIyFueoqDdAQAAgDSKUqSYmY499tiW5+vWrdPAgQM3+pzI0aNHq/n6s4cffrhefvnl7owpSXr66af1qU99SnvssYf23HNPNTU1SZKOP/547bzzzqqvr1d9fb0WLVrU7e8NAAAAoOv6hg6A/Hnve9+rhx9+WGvXrtVmm22mm266SUOHDu3SOq+77rpuSpf05S9/WWeccYYOPfRQrVmzRptssuF3lvPOO0+f//zne+R9AQAAAHQPekoz4O6hIyR0JM+nP/1pLViwQJI0Z84cTZw4sWXe66+/rhNOOEH77befhg8frmuvvVaStHbtWk2YMEHDhg3TF7/4Ra1du7blNXV1dXr++eclSUceeaRGjhypvfbaSzNnzmxZZsCAATrjjDO07777atSoUVq1alXVjIsXL9a6det06KGHtrx+880372ArZC9v+wEAAACQBxSlGSgvvPKgI3kmTJigK664Qm+++aYeeughfeQjH2mZ96Mf/Uif/OQndf/99+u2227T6aefrtdff10XXnihNt98cz300EM644wz1NjYWHHdl1xyiRobG9XQ0KALLrhAL7zwgqSo2B01apQefPBBHXTQQZo1a5Ykae7cuTrrrLNS63n00Ue15ZZb6nOf+5yGDx+u008/XevXr2+Zf8YZZ2jYsGH61re+pbfeemuj2qgn5G0/AAAAAPKAojQDJ554YugICR3JM2zYMDU1NWnOnDk6/PDDE/NuvPFGTZ8+XfX19Ro9erTefPNNPf300/rrX/+qY445puX1w4YNq7juCy64oKU39JlnntFjjz0mSerfv3/LeasjR45sOT90/Pjx+sEPfpBaz7p163TnnXfq/PPP1/33368nnnhCl156qSTpJz/5iZYsWaL7779fL774os4999wOtU1Pytt+AAAAAOQBRSnaNH78eH3nO99JHLorRYehXnXVVVq0aJEWLVqkp59+WnvssYekaJCkam6//XbdfPPNuueee/Tggw9q+PDhevPNNyVJ/fr1a3l9nz59tG7duqrr2n777TV8+HB98IMfVN++fXXkkUdq4cKFkqTBgwfLzLTpppvq3//933Xfffd1qg0AAAAA9CyKUrTphBNO0FlnnaV99tknMf2www7TL3/5y5ZzJB944AFJ0kEHHaTLL79ckvTwww/roYceSq3zlVde0VZbbaXNN99cS5Ys0b333tvpfPvtt59eeuklrV69WpJ06623as8995QkrVy5UlJUQF9zzTXae++9O/0+AAAAAHoORWkG5s6dGzpCQkfzbL/99jrllFNS088880y98847GjZsmPbee2+deeaZkqSvfe1rWrNmjYYNG6af/vSn2n///VOvHTNmjNatW6dhw4bpzDPP1KhRozqUt9I5pX369NH555+vQw45RPvss4/cXZMmTZIkfelLX9I+++yjffbZR88//7z+67/+q0OfuSflbT8AAAAA8sDyMCJoqVTy5utZ5lXdlAVV5zdNH9vmvBUrVmjIkCHdHanT8panKGh3AAAAFJWZNbp7qdI8ekoz0NVrfHa3vOUpCtodAAAASKMoBQAAAAAEQ1EKAAAAAAiGojQDzYPv5EXe8hQF7Q4AAACkMdBRB3VloCMAAAAAKDIGOgps5MiRoSMk5C1PUdDuAAAAQBpFaQYWLlwYOkJC3vIUBe0OAAAApFGUAgAAAACCoSjNwODBg0NHSMhbnqKg3QEAAIA0itIMrFixInSEhLzlKQraHQAAAEijKM3AtGnTQkdIyFueoqDdAQAAgDQuCdNBXbkkjJkpD+3cLG95ioJ2BwAAQFFxSRgAAAAAQC5RlAIAAAAAgqEozUDeDk3OW56ioN0BAACANIpSAAAAAEAwFKUZKJUqns8bTN7yFAXtDgAAAKRRlAIAAAAAgqEoBQAAAAAEQ1GagalTp4aOkJC3PEVBuwMAAABp5u6hM6hUKnneRyatm7Kg6vym6WMzSgIAAAAAtcXMGt294iAr9JRmYMiQIaEjJOQtT1HQ7gAAAEAaRWkGVq5cGTpCQt7yFAXtDgAAAKRRlAIAAAAAgqEozcCIESNCR0jIW56ioN0BAACAtHaLUjO7xMyeM7OHy6ZNM7PlZrYovh1eNu97Zva4mf3LzA7rqeC1pLGxMXSEhLzlKQraHQAAAEjrSE/ppZLGVJj+c3evj2/XSZKZ7SlpgqS94tf8xsz6dFfYWjV58uTQERLylqcoaHcAAAAgrd2i1N3/KunFDq7vM5KucPe33P1JSY9L2r8L+XqFWbNmhY6QkLc8RUG7AwAAAGldOaf0ZDN7KD68d6t42lBJz5QtsyyeBgAAAABASmeL0gsl7SKpXtJKST+Lp1uFZb3SCsxsspk1mFnD6tWrOxkDAAAAAFDLOlWUuvsqd1/v7u9KmqUNh+guk7RD2aLbS1rRxjpmunvJ3UsDBw7sTIyasXz58tAREvKWpyhodwAAACCtU0WpmQ0ue/pZSc0j886VNMHMNjWznSXtKum+rkWsfXkbdTVveYqCdgcAAADS+ra3gJnNkTRa0rZmtkzSVEmjzaxe0aG5TZJOlCR3f8TM/iRpsaR1kk5y9/U9kryGjB8/Xu4Vj2IOIm95ioJ2BwAAANLaLUrdfWKFyRdXWf5Hkn7UlVAAAAAAgGLoyui7AAAAAAB0CUVpBi666KLQERLylqcoaHcAAAAgzfJwjlupVPKGhobQMaqqm7Kg6vym6WMzSgIAAAAAtcXMGt29VGkePaUZMKt0+dZw8panKGh3AAAAII2iFAAAAAAQDEUpAAAAACAYitIMjBs3LnSEhLzlKQraHQAAAEijKM3AvHnzQkdIyFueoqDdAQAAgDSK0gwcccQRoSMk5C1PUdDuAAAAQBpFaQbmz58fOkJC3vIUBe0OAAAApFGUAgAAAACCoSgFAAAAAARDUZoBdw8dISFveYqCdgcAAADSKEozMHPmzNAREvKWpyhodwAAACDN8tB7UyqVvKGhIXSMquqmLKg6v2n62DbnmVmuesnylqcoaHcAAAAUlZk1unup0jx6SgEAAAAAwVCUAgAAAACCoSjNwNy5c0NHSMhbnqKg3QEAAIA0itIMjBw5MnSEhLzlKQraHQAAAEijKM3A0KFDQ0dIyFueoqDdAQAAgDSKUgAAAABAMBSlAAAAAIBgKEozMGnSpNAREvKWpyhodwAAACDN3D10BpVKJW9oaAgdo6q6KQuqzm+aPjajJAAAAABQW8ys0d1LlebRU5qBvI26mrc8RUG7AwAAAGkUpRlYuHBh6AgJectTFLQ7AAAAkEZRCgAAAAAIhqI0A4MHDw4dISFveYqCdgcAAADSKEozsGLFitAREvKWpyhodwAAACCNojQD06ZNCx0hIW95ioJ2BwAAANK4JEwHdeWSMGamPLRzs7zlKQraHQAAAEXFJWEAAAAAALlEUQoAAAAACIaiNAN5OzQ5b3mKgnYHAAAA0ihKAQAAAADBUJRmoFSqeD5vMHnLUxS0OwAAAJBGUQoAAAAACIaiFAAAAAAQDEVpBqZOnRo6QkLe8hQF7Q4AAACkmbuHzqBSqeR5H5m0bsqCqvObpo/NKAkAAAAA1BYza3T3ioOs0FOagSFDhoSOkJC3PEVBuwMAAABpFKUZWLlyZegICXnLUxS0OwAAAJBGUQoAAAAACIaiNAMjRowIHSEhb3mKgnYHAAAA0ihKM9DY2Bg6QkLe8hQF7Q4AAACkUZRmYPLkyaEjJOQtT1HQ7gAAAEAal4TpoK5cEsbMlId2bpa3PEVBuwMAAKCouCQMAAAAACCXKEoBAAAAAMFQlGZg+fLloSMk5C1PUdDuAAAAQBpFaQbyNupq3vIUBe0OAAAApLVblJrZJWb2nJk9XDbtPDNbYmYPmdnVZrZlPL3OzNaa2aL49j89mL1mjB8/PnSEhLzlKQraHQAAAEjrSE/ppZLGtJp2k6S93X2YpEclfa9s3lJ3r49vX+2emAAAAACA3qjdotTd/yrpxVbTbnT3dfHTeyVt3wPZAAAAAAC9XHecU3qCpL+UPd/ZzB4wszvM7OPdsP6ad9FFF4WOkJC3PEVBuwMAAABp5u7tL2RWJ2m+u+/davoZkkqSPufubmabShrg7i+Y2UhJ10jay91frbDOyZImS9KOO+448qmnnurqZ+lRdVMWVJ3fNH1sRkkAAAAAoLaYWaO7lyrN63RPqZkdJ2mcpC95XNm6+1vu/kL8uFHSUkkfrvR6d5/p7iV3Lw0cOLCzMWqCmYWOkJC3PEVBuwMAAABpnSpKzWyMpO9KGu/ub5RNH2hmfeLHH5S0q6QnuiMoAAAAAKD36dveAmY2R9JoSdua2TJJUxWNtruppJvi3p9745F2D5L0AzNbJ2m9pK+6+4sVVwwAAAAAKLx2i1J3n1hh8sVtLHuVpKu6Gqq3GTduXOgICXnLUxS0OwAAAJDWHaPvoh3z5s0LHSEhb3mKgnYHAAAA0ihKM3DEEUeEjpCQtzxFQbsDAAAAaRSlGZg/f37oCAl5y1MUtDsAAACQRlEKAAAAAAiGohQAAAAAEAxFaQbcPXSEhLzlKQraHQAAAEijKM3AzJkzQ0dIyFueoqDdAQAAgDTLQ+9NqVTyhoaG0DGqqpuyoOr8pulj25xnZrnqJctbnqKg3QEAAFBUZtbo7qVK8+gpBQAAAAAEQ1EKAAAAAAiGojQDc+fODR0hIW95ioJ2BwAAANIoSjMwcuTI0BES8panKGh3AAAAII2iNANDhw4NHSEhb3mKgnYHAAAA0ihKAQAAAADBUJQCAAAAAIKhKM3ApEmTQkdIyFueoqDdAQAAgDRz99AZVCqVvKGhIXSMquqmLKg6v2n62IySAAAAAEBtMbNGdy9VmkdPaQbyNupq3vIUBe0OAAAApFGUZmDhwoWhIyTkLU9R0O4AAABAGkUpAAAAACAYitIMDB48OHSEhLzlKQraHQAAAEijKM3AihUrQkdIyFueoqDdAQAAgDSK0gxMmzYtdISEvOUpCtodAAAASOOSMB3UlUvCmJny0M7N8panKGh3AAAAFBWXhAEAAAAA5BJFKQAAAAAgGIrSDOTt0OS85SkK2h0AAABIoygFAAAAAARDUZqBUqni+bzB5C1PUdDuAAAAQBpFKQAAAAAgGIpSAAAAAEAwFKUZmDp1augICXnLUxS0OwAAAJBm7h46g0qlkud9ZNK6KQuqzm+aPjajJAAAAABQW8ys0d0rDrJCT2kGhgwZEjpCQt7yFAXtDgAAAKRRlGZg5cqVoSMk5C1PUdDuAAAAQBpFKQAAAAAgGIrSDIwYMSJ0hIS85SkK2h0AAABIoyjNQGNjY+gICXnLUxS0OwAAAJBGUZqByZMnh46QkLc8RUG7AwAAAGlcEqaDunJJGDNTHtq5Wd7yFAXtDgAAgKLikjAAAAAAgFyiKAUAAAAABENRmoHly5eHjpCQtzxFQbsDAAAAaRSlGcjbqKt5y1MUtDsAAACQRlGagfHjx4eOkJC3PEVBuwMAAABpFKUAAAAAgGAoSgEAAAAAwVCUZuCiiy4KHSEhb3mKgnYHAAAA0szdQ2dQqVTyhoaG0DGqqpuyoOr8puljM0oCAAAAALXFzBrdvVRpHj2lGTCz0BES8panKGh3AAAAII2iFAAAAAAQTLtFqZldYmbPmdnDZdO2NrObzOyx+H6rsnnfM7PHzexfZnZYTwUHAAAAANS+jvSUXippTKtpUyTd4u67Srolfi4z21PSBEl7xa/5jZn16ba0NWrcuHGhIyTkLU9R0O4AAABAWrtFqbv/VdKLrSZ/RtLs+PFsSUeWTb/C3d9y9yclPS5p/+6JWrvmzZsXOkJC3vIUBe0OAAAApHX2nNJB7r5SkuL77eLpQyU9U7bcsnhaoR1xxBGhIyTkLU9R0O4AAABAWncPdFRpeNGK15wxs8lm1mBmDatXr+7mGPkyf/780BES8panKGh3AAAAIK2zRekqMxssSfH9c/H0ZZJ2KFtue0krKq3A3We6e8ndSwMHDuxkDAAAAABALetsUTpX0nHx4+MkXVs2fYKZbWpmO0vaVdJ9XYsIAAAAAOit+ra3gJnNkTRa0rZmtkzSVEnTJf3JzL4i6WlJR0uSuz9iZn+StFjSOkknufv6HspeM9wrHsEcTN7yFAXtDgAAAKR1ZPTdie4+2N37ufv27n6xu7/g7oe4+67x/Ytly//I3Xdx993c/S89G782zJw5M3SEhLzlKQraHQAAAEizPPTelEolb2hoCB2jqropC6rOb5o+ts15ZparXrK85SkK2h0AAABFZWaN7l6qNK+7R98FAAAAAKDDKEoBAAAAAMFQlGZg7ty5oSMk5C1PUdDuAAAAQBpFaQZGjhwZOkJC3vIUBe0OAAAApFGUZmDo0KGhIyTkLU9R0O4AAABAGkUpAAAAACAYilIAAAAAQDAUpRmYNGlS6AgJectTFLQ7AAAAkGbuHjqDSqWSNzQ0hI5RVd2UBVXnN00fm1ESAAAAAKgtZtbo7qVK8+gpzUDeRl3NW56ioN0BAACANIrSDCxcuDB0hIS85SkK2h0AAABIoygFAAAAAARDUZqBwYMHh46QkLc8RUG7AwAAAGkUpRlYsWJF6AgJectTFLQ7AAAAkEZRmoFp06aFjpCQtzxFQbsDAAAAaVwSpoO6ckkYM1Me2rlZ3vIUBe0OAACAouKSMAAAAACAXKIoBQAAAAAEQ1Gagbwdmpy3PEVBuwMAAABpFKUAAAAAgGAoSjNQKlU8nzeYvOUpCtodAAAASKMoBQAAAAAEQ1EKAAAAAAiGojQDU6dODR0hIW95ioJ2BwAAANLM3UNnUKlU8ryPTFo3ZUHV+U3Tx2aUBAAAAABqi5k1unvFQVboKc3AkCFDQkdIyFueoqDdAQAAgDSK0gysXLkydISEvOUpCtodAAAASKMoBQAAAAAEQ1GagREjRoSOkJC3PEVBuwMAAABpFKUZaGxsDB0hIW95ioJ2BwAAANIoSjMwefLk0BES8panKGh3AAAAII1LwnRQVy4JY2bKQzs3y1ueoqDdAQAAUFRcEgYAAAAAkEsUpQAAAACAYChKM7B8+fLQERLylqcoaHcAAAAgjaI0A3kbdTVveYqCdgcAAADSKEozMH78+NAREvKWpyhodwAAACCtb+gAvUV7o/MCAAAAANLoKQUAAAAABENRmoGtDzs5dISEiy66KHSEQqLdAQAAgDSK0gxsUT8mdISEyZMnh45QSLQ7AAAAkEZRmoGnzh0XOkKCmYWOUEi0OwAAAJBGUQoAAAAACIaiFAAAAAAQDEVpBjbbZb/QERLGjcvX4cRFQbsDAAAAaRSlGdju81NDR0iYN29e6AiFRLsDAAAAaRSlGXjuyrNDR0g44ogjQkcoJNodAAAASKMozcDapfeHjpAwf/780BEKiXYHAAAA0ihKAQAAAADBUJQCAAAAAILp29kXmtlukv5YNumDks6StKWkSZJWx9O/7+7XdfZ9eoOdvpuvwzbdPXSEQqLdAQAAgLRO95S6+7/cvd7d6yWNlPSGpKvj2T9vnlf0glSSXlt0fegICTNnzgwdoZBodwAAACCtuw7fPUTSUnd/qpvW16u8eMOvQkdIOPHEE0NHKCTaHQAAAEjrrqJ0gqQ5Zc9PNrOHzOwSM9uqm94DAAAAANDLdLkoNbP+ksZL+nM86UJJu0iql7RS0s/aeN1kM2sws4bVq1dXWgQAAAAA0Mt1R0/ppyUtdPdVkuTuq9x9vbu/K2mWpP0rvcjdZ7p7yd1LAwcO7IYY+TXwqDNDR0iYO3du6AiFRLsDAAAAad1RlE5U2aG7Zja4bN5nJT3cDe9R0/oP+lDoCAkjR44MHaGQaHcAAAAgrUtFqZltLulQSf9XNvmnZvYPM3tI0ickfasr79EbLP/NcaEjJAwdOjR0hEKi3QEAAIC0Tl+nVJLc/Q1J27SadmyXEgEAAAAACqO7Rt8FAAAAAGCjUZRmYMC+h4WOkDBp0qTQEQqJdgcAAADSKEozsM2Yb4SOkDBz5szQEQqJdgcAAADSKEozsPLSU0JHSGAU2DBodwAAACCNojQDb69aGjpCwsKFC0NHKCTaHQAAAEijKAUAAAAABENRmoE+A7YOHSFh8ODBoSMUEu0OAAAApFGUZmD7ky4LHSFhxYoVoSMUEu0OAAAApFGUZuDluy4PHSFh2rRpoSMUEu0OAAAApJm7h86gUqnkDQ0NoWNUVTdlQadf+9S545SHdm5mZrnKUxS0OwAAAIrKzBrdvVRpHj2lAAAAAIBgKEoBAAAAAMFQlGbgA8fNCB0hIe+HSvdWtDsAAACQRlEKAAAAAAiGojQDz84+NXSEhFKp4vnF6GG0OwAAAJBGUQoAAAAACIaiFAAAAAAQDEVpBt5/4MTQERKmTp0aOkIh0e4AAABAmrl76AwqlUqe95FJ66Ys6NLrm6aP7aYkAAAAAFBbzKzR3SsOskJPaQaW/frLoSMkDBkyJHSEQqLdAQAAgDSK0gysX/Ni6AgJK1euDB2hkGh3AAAAII2iFAAAAAAQDEVpBvoP2iV0hIQRI0aEjlBItDsAAACQRlGagcHH/yJ0hITGxsbQEQqJdgcAAADSKEoz8ML1vwwdIWHy5MmhIxQS7Q4AAACkUZRmYM2DN4SOkDBr1qzQEQqJdgcAAADSKEoBAAAAAMFQlAIAAAAAgqEozcDQr88OHSFh+fLloSMUEu0OAAAApFGUZuDtVY+HjpDAKLBh0O4AAABAGkVpBlZf9cPQERLGjx8fOkIh0e4AAABAGkUpAAAAACAYilIAAAAAQDAUpRnY+rCTQ0dIuOiii0JHKCTaHQAAAEgzdw+dQaVSyRsaGkLHqKpuyoIeXX/T9LE9un4AAAAACMXMGt29VGkePaUZeOrccaEjJJhZ6AiFRLsDAAAAaRSlAAAAAIBgKEoBAAAAAMFQlGZgs132Cx0hYdy4fB1OXBS0OwAAAJBGUZqB7T4/NXSEhHnz5oWOUEi0OwAAAJBGUZqB5648O3SEhCOOOCJ0hEKi3QEAAIC0vqEDFMHapfe3u0x7l5zpzkvGzJ8/v9vWhY6j3QEAAIA0ekoBAAAAAMFQlAIAAAAAgqEozcBO383XYZvuHjpCIdHuAAAAQBpFaQZeW3R96AgJM2fODB2hkGh3AAAAII2iNAMv3vCr0BESTjzxxNARCol2BwAAANIoSgEAAAAAwVCUAgAAAACCoSjNwMCjzgwdIWHu3LmhIxQS7Q4AAACkUZRmoP+gD4WOkDBy5MjQEQqJdgcAAADSKEozsPw3x4WOkDB06NDQEQqJdgcAAADS+nblxWbWJOk1SeslrXP3kpltLemPkuokNUn6gru/1LWYAAAAAIDeqDt6Sj/h7vXuXoqfT5F0i7vvKumW+DkAAAAAACk9cfjuZyTNjh/PlnRkD7xHTRmw72GhIyRMmjQpdIRCot0BAACANHP3zr/Y7ElJL0lySRe5+0wze9ndtyxb5iV336rCaydLmixJO+6448innnqq0zmyUDdlQdD3b5o+Nuj7AwAAAEBnmVlj2dG1CV3tKT3Q3UdI+rSkk8zsoI6+0N1nunvJ3UsDBw7sYox8W3npKaEjJDAKbBi0OwAAAJDWpaLU3VfE989JulrS/pJWmdlgSYrvn+tqyFr39qqloSMkLFy4MHSEQqLdAQAAgLROF6Vm9l4z26L5saRPSXpY0lxJzddAOU7StV0NCQAAAADonbpySZhBkq42s+b1/MHdrzez+yX9ycy+IulpSUd3PWZt6zNg69AREgYPHhw6QiHR7gAAAEBap4tSd39C0r4Vpr8g6ZCuhOpttj/psh5/j44MxNQ8WNKKFSt6Og4qoN0BAACAtJ64JAxaefmuy0NHSJg2bVroCIVEuwMAAABpFKUZeOXuOaEjJJx99tmhIxQS7Q4AAACkdeWcUmQo9HVSAQAAAKAn0FMKAAAAAAiGojQDHzhuRugICQ0NDaEjFBLtDgAAAKRRlAIAAAAAgqEozcCzs08NHSGhVCqFjlBItDsAAACQRlEKAAAAAAiGohQAAAAAEAxFaQbef+DE0BESpk6dGjpCIdHuAAAAQJq5e+gMKpVKnveRSXvDdUKbpo8NHQEAAABAAZlZo7tXHGSFntIMLPv1l0NHSBgyZEjoCIVEuwMAAABpFKUZWL/mxdARElauXBk6QiHR7gAAAEAaRSkAAAAAIBiK0gz0H7RL6AgJI0aMCB2hkGh3AAAAII2iNAODj/9F6AgJjY2NoSMUEu0OAAAApFGUZuCF638ZOkLC5MmTQ0coJNodAAAASKMozcCaB28IHSFh1qxZoSMUEu0OAAAApFGUAgAAAACCoSgFAAAAAARDUZqBoV+fHTpCwvLly0NHKCTaHQAAAEijKM3A26seDx0hgVFgw6DdAQAAgDSK0gysvuqHoSMkjB8/PnSEQqLdAQAAgDSKUgAAAABAMBSlAAAAAIBgKEozsPVhJ4eOkHDRRReFjlBItDsAAACQRlGagS3qx4SOkDB58uTQEQqJdgcAAADSKEoz8NS540JHSDCz0BEKiXYHAAAA0ihKAQAAAADBUJQCAAAAAIKhKM3AZrvsFzpCwrhx+TqcuChodwAAACCNojQD231+augICfPmzQsdoZBodwAAACCNojQDz115dugICUcccUToCIVEuwMAAABpFKUZWLv0/tAREubPnx86QiHR7gAAAEAaRSkAAAAAIJi+oQMgO3VTFlR83Kxp+tgs4wAAAAAAPaVZ2Om7+TpsM295isLdQ0cAAAAAcoeiNAOvLbo+dISEvOUpipkzZ4aOAAAAAOQORWkGXrzhV6EjJOQtT1GceOKJoSMAAAAAuUNRCgAAAAAIhoGO0KLS4EflGAgJAAAAQHejpzQDA486M3SEhLzlKYq5c+eGjgAAAADkDkVpBvoP+lDoCAl5y1MUI0eODB0BAAAAyB2K0gws/81xoSMk5C1PUQwdOjR0BAAAACB3KEoBAAAAAMFQlAIAAAAAgqEozcCAfQ8LHSEhb3mKYtKkSaEjAAAAALlDUZqBbcZ8I3SEhLzlKYqZM2eGjgAAAADkDkVpBlZeekroCAl5y1MUjL4LAAAApFGUZuDtVUtDR0jIW56iWLhwYegIAAAAQO5QlAIAAAAAgul0UWpmO5jZbWb2TzN7xMxOiadPM7PlZrYovh3efXFrU58BW4eOkJC3PEUxePDg0BEAAACA3Onbhdeuk/Rtd19oZltIajSzm+J5P3f387ser3fY/qTLQkdIyFueolixYkXoCAAAAEDudLqn1N1XuvvC+PFrkv4paWh3BetNXr7r8tAREvKWpyimTZsWOgIAAACQO91yTqmZ1UkaLunv8aSTzewhM7vEzLbqjveoZa/cPSd0hIS85SmKs88+O3QEAAAAIHe6cviuJMnMBki6StKp7v6qmV0o6YeSPL7/maQTKrxusqTJkrTjjjt2NQZ6gbopC6rOb5o+NqMkAAAAALLSpZ5SM+unqCC93N3/T5LcfZW7r3f3dyXNkrR/pde6+0x3L7l7aeDAgV2JAQAAAACoUV0ZfdckXSzpn+7+32XTy4cY/aykhzsfr3f4wHEzQkdIyFueomhoaAgdAQAAAMidrhy+e6CkYyX9w8wWxdO+L2mimdUrOny3SdKJXXgPAAAAAEAv1umi1N3vkmQVZl3X+Ti907OzT9VO350fOkaLvOXpqFo/57RUKsndQ8cAAAAAcqVbRt8FAAAAAKAzKEoBAAAAAMFQlGbg/QdODB0hIW95imLq1KmhIwAAAAC5Q1GagS0/9qXQERLylqcopk2bFjoCAAAAkDtdGX0XHbTs11/W9iddFjpGi7zl6U2qDca07Ndf1rrXXsgwDQAAAJB/9JRmYP2aF0NHSMhbnqKg3QEAAIA0ilIAAAAAQDAcvpuB/oN2CR0hobN5av06oaHlbT8AAAAA8oCe0gwMPv4XoSMk5C1PUdDuAAAAQBo9pRl44fpfapsx3wgdo0WoPO31tOYhQ0/29r5w/S8lepMBAACABHpKM7DmwRtCR0jIW56ioN0BAACANHpKgY2Qh95eAAAAoDehpxQAAAAAEAxFaQaGfn126AgJectTFLQ7AAAAkEZRmoG3Vz0eOkJC3vIUBe0OAAAApFGUZmD1VT8MHSEhb3mKgnYHAAAA0hjoCMhQyEvSAAAAAHlETykAAAAAIBiK0gxsfdjJoSMk5C1PUdDuAAAAQBpFaQa2qB8TOkJC3vIUBe0OAAAApFGUZuCpc8eFjpCQtzxFQbsDAAAAaQx0hF6jvUGEslpHT74/AyEBAACgt6GnFAAAAAAQDD2lGdhsl/1CR0jIW56iyEO7d6QnmN5YAAAAZIme0gxs9/mpoSMk5C1PUdDuAAAAQBpFaQaeu/Ls0BES8panKGh3AAAAII3DdzOwdun9oSMk9FSe0IME5V3e9oO2MNgSAAAAskRPKQAAAAAgGHpKASBn6K0GAABFQk9pBnb67vzQERLylqcoaHcAAAAgjaI0A68tuj50hIS85SkK2h0AAABI4/DdDLx4w6+0Rf2Y0DFa5C1PURSl3Xv60NOuDqjFoa8AAAD5Qk8pAAAAACAYekqBGsJld7quI21IbyoAAEB26CnNwMCjzgwdISFveYqCdgcAAADSKEoz0H/Qh0JHSMhbnqKg3QEAAIA0Dt/NwPLfHJery4HkLU9R9JZ27+lDiDlEGQAAoFjoKQUAAAAABENPKYBM0RMKAACAcvSUZmDAvoeFjpCQtzxFQbsDAAAAafSUZmCbMd8IHSEhb3mKgnYvjvZ6g7t6yZmeXj8AAECW6CnNwMpLTwkdISFveYqCdgcAAADSKEoz8PaqpaEjJOQtT1HQ7gAAAEAah+8CQMF0x2BTvf0Q5LznAwCgN6GnNAN9BmwdOkJC3vIUBe0OAAAApNFTmoHtT7osdISEvOUpCtq9doQeqKg3KMJn7Gn01gIAioKe0gy8fNfloSMk5C1PUdDuAAAAQBpFaQZeuXtO6AgJectTFLQ7AAAAkMbhuwCwkTg0tefbgENX20cbAQB6C3pKAQAAAADB0FOagQ8cNyN0hIS85SkK2h1ZoSe353WkjdvrqSxCb3MeMvSkPFxeCQB6gx7rKTWzMWb2LzN73Mym9NT7AAAAAABqV4/0lJpZH0m/lnSopGWS7jezue6+uCfeL++enX2qdvru/NAxWuQtT1HQ7kD36WoPXBa9yXnvsa6FXsxayJh3oS9xxTbKhyJsp97+GbvjCJ0866me0v0lPe7uT7j725KukPSZHnovAAAAAECN6qmidKikZ8qeL4unAQAAAADQwty9+1dqdrSkw9z9P+Lnx0ra392/UbbMZEmT46e7SfpXN7z1tpKe74b1IBy2Ye/Adqx9bMPaxzasfWzD2sc27B3Yjt1jJ3cfWGlGT42+u0zSDmXPt5e0onwBd58paWZ3vqmZNbh7qTvXiWyxDXsHtmPtYxvWPrZh7WMb1j62Ye/Adux5PXX47v2SdjWznc2sv6QJkub20HsBAAAAAGpUj/SUuvs6MztZ0g2S+ki6xN0f6Yn3AgAAAADUrp46fFfufp2k63pq/W3o1sOBEQTbsHdgO9Y+tmHtYxvWPrZh7WMb9g5sxx7WIwMdAQAAAADQET11TikAAAAAAO3qNUWpmY0xs3+Z2eNmNiV0HlRmZpeY2XNm9nDZtK3N7CYzeyy+36ps3vfibfovMzssTGqUM7MdzOw2M/unmT1iZqfE09mONcLM3mNm95nZg/E2PDuezjasMWbWx8weMLP58XO2YY0xsyYz+4eZLTKzhnga27GGmNmWZnalmS2J/288gG1YO8xst/jvr/n2qpmdyjbMVq8oSs2sj6RfS/q0pD0lTTSzPcOmQhsulTSm1bQpkm5x910l3RI/V7wNJ0jaK37Nb+JtjbDWSfq2u+8haZSkk+JtxXasHW9J+qS77yupXtIYMxsltmEtOkXSP8uesw1r0yfcvb7skhNsx9ryC0nXu/vukvZV9DfJNqwR7v6v+O+vXtJISW9Iulpsw0z1iqJU0v6SHnf3J9z9bUlXSPpM4EyowN3/KunFVpM/I2l2/Hi2pCPLpl/h7m+5+5OSHle0rRGQu69094Xx49cU/ec7VGzHmuGRNfHTfvHNxTasKWa2vaSxkn5bNplt2DuwHWuEmb1P0kGSLpYkd3/b3V8W27BWHSJpqbs/JbZhpnpLUTpU0jNlz5fF01AbBrn7SikqeCRtF09nu+acmdVJGi7p72I71pT4sM9Fkp6TdJO7sw1rzwxJ/ynp3bJpbMPa45JuNLNGM5scT2M71o4PSlot6X/jQ+l/a2bvFduwVk2QNCd+zDbMUG8pSq3CNIYVrn1s1xwzswGSrpJ0qru/Wm3RCtPYjoG5+/r4UKXtJe1vZntXWZxtmDNmNk7Sc+7e2NGXVJjGNsyHA919hKJTkE4ys4OqLMt2zJ++kkZIutDdh0t6XfFhnm1gG+aUmfWXNF7Sn9tbtMI0tmEX9ZaidJmkHcqeby9pRaAs2HirzGywJMX3z8XT2a45ZWb9FBWkl7v7/8WT2Y41KD7M7HZF58WwDWvHgZLGm1mTolNWPmlmvxfbsOa4+4r4/jlF57HtL7ZjLVkmaVl8tIkkXamoSGUb1p5PS1ro7qvi52zDDPWWovR+Sbua2c7xrxwTJM0NnAkdN1fScfHj4yRdWzZ9gpltamY7S9pV0n0B8qGMmZmic2f+6e7/XTaL7VgjzGygmW0ZP95M0r9JWiK2Yc1w9++5+/buXqfo/7xb3f0YsQ1ripm918y2aH4s6VOSHhbbsWa4+7OSnjGz3eJJh0haLLZhLZqoDYfuSmzDTPUNHaA7uPs6MztZ0g2S+ki6xN0fCRwLFZjZHEmjJW1rZsskTZU0XdKfzOwrkp6WdLQkufsjZvYnRf+4r5N0kruvDxIc5Q6UdKykf8TnJErS98V2rCWDJc2ORwvcRNKf3H2+md0jtmGt4++wtgySdHX0W5/6SvqDu19vZveL7VhLviHp8rhj5AlJ/67431a2YW0ws80lHSrpxLLJ/HuaIXPnEGgAAAAAQBi95fBdAAAAAEANoigFAAAAAARDUQoAAAAACIaiFAAAAAAQDEUpAAAAACAYilIAAAAAQDAUpQAAAACAYChKAQAAAADB/H860d8Vc9RlcAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1152x576 with 1 Axes>"
       ]
@@ -226,12 +226,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1152x576 with 1 Axes>"
       ]
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -283,7 +283,7 @@
        "Name: user, dtype: float64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -301,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -339,7 +339,7 @@
        " 18: 'Western'}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -350,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -503,7 +503,7 @@
        "[3 rows x 24 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -514,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -524,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -616,7 +616,7 @@
        "4   5     Copycat (1995)         Crime, Drama, Thriller"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -635,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -644,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -658,6 +658,20 @@
     "toy_train.to_csv('./Datasets/toy-example/train.csv', sep='\\t', header=None, index=False)\n",
     "toy_test.to_csv('./Datasets/toy-example/test.csv', sep='\\t', header=None, index=False)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -676,7 +690,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/P1. Baseline.ipynb b/P1. Baseline.ipynb
index 9fbe285..3dbaf3a 100644
--- a/P1. Baseline.ipynb	
+++ b/P1. Baseline.ipynb	
@@ -195,7 +195,7 @@
     {
      "data": {
       "text/plain": [
-       "<3x4 sparse matrix of type '<class 'numpy.int64'>'\n",
+       "<3x4 sparse matrix of type '<class 'numpy.intc'>'\n",
        "\twith 8 stored elements in Compressed Sparse Row format>"
       ]
      },
@@ -229,7 +229,7 @@
       "text/plain": [
        "matrix([[4, 1, 3, 0],\n",
        "        [0, 2, 0, 1],\n",
-       "        [2, 0, 5, 4]])"
+       "        [2, 0, 5, 4]], dtype=int32)"
       ]
      },
      "metadata": {},
@@ -306,7 +306,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.13 µs ± 79.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
+      "658 ns ± 16.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
       "Inefficient way to access items rated by user:\n"
      ]
     },
@@ -314,7 +314,7 @@
      "data": {
       "text/plain": [
        "array([  0,   6,  10,  27,  49,  78,  95,  97, 116, 143, 153, 156, 167,\n",
-       "       171, 172, 173, 194, 208, 225, 473, 495, 549, 615], dtype=int32)"
+       "       171, 172, 173, 194, 208, 225, 473, 495, 549, 615])"
       ]
      },
      "metadata": {},
@@ -324,7 +324,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "149 µs ± 11.5 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
+      "67.8 µs ± 1.68 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
      ]
     }
    ],
@@ -364,7 +364,7 @@
       "text/plain": [
        "matrix([[4, 1, 3, 0],\n",
        "        [0, 2, 0, 1],\n",
-       "        [2, 0, 5, 4]])"
+       "        [2, 0, 5, 4]], dtype=int32)"
       ]
      },
      "metadata": {},
@@ -877,7 +877,7 @@
       "text/plain": [
        "matrix([[3, 4, 0, 0, 5, 0, 0, 4],\n",
        "        [0, 1, 2, 3, 0, 0, 0, 0],\n",
-       "        [0, 0, 0, 5, 0, 3, 4, 0]])"
+       "        [0, 0, 0, 5, 0, 3, 4, 0]], dtype=int64)"
       ]
      },
      "metadata": {},
@@ -1070,6 +1070,269 @@
     "- For each row of matrix M' representing the user u, we compute the mean of ratings and denote by b_u."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class selfBaselineIU():\n",
+    "    \n",
+    "    def fit(self, train_ui):\n",
+    "        self.train_ui=train_ui.copy()\n",
+    "        self.train_iu=train_ui.transpose().tocsr()\n",
+    "        \n",
+    "        result=self.train_ui.copy()\n",
+    "        \n",
+    "        #we can't do result=train_ui-to_subtract_rows since then 0 entries will \"disappear\" in csr format\n",
+    "        self.col_means=np.divide(np.asarray(result.sum(axis=0).ravel())[0], np.diff(self.train_iu.indptr),\\\n",
+    "                            out=np.zeros(self.train_iu.shape[0]), where=np.diff(self.train_iu.indptr)!=0) # handling items without ratings\n",
+    "        \n",
+    "        # again - it is possible that some mean will be zero, so let's use the same workaround\n",
+    "        col_means=self.col_means.copy()\n",
+    "        \n",
+    "        max_col_mean=np.max(col_means)\n",
+    "        col_means[col_means==0]=max_col_mean+1\n",
+    "        to_subtract_cols=result.power(0)*sparse.diags(col_means)\n",
+    "        to_subtract_cols.sort_indices() # needed to have valid .data\n",
+    "        \n",
+    "        subtract=to_subtract_cols.data\n",
+    "        subtract[subtract==max_col_mean+1]=0\n",
+    "        \n",
+    "        result.data=result.data-subtract\n",
+    "\n",
+    "\n",
+    "        self.row_means=np.asarray(result.sum(axis=1).ravel())[0]/np.diff(result.indptr)\n",
+    "        \n",
+    "        # in csr format after addition or multiplication 0 entries \"disappear\" - so some workaraunds are needed \n",
+    "        # (other option is to define addition/multiplication in a desired way)\n",
+    "        row_means=self.row_means.copy()\n",
+    "        \n",
+    "        max_row_mean=np.max(row_means)\n",
+    "        row_means[row_means==0]=max_row_mean+1\n",
+    "        to_subtract_rows=sparse.diags(row_means)*(result.power(0))\n",
+    "        to_subtract_rows.sort_indices() # needed to have valid .data\n",
+    "        \n",
+    "        subtract=to_subtract_rows.data\n",
+    "        subtract[subtract==max_row_mean+1]=0\n",
+    "        \n",
+    "        result.data=result.data-subtract\n",
+    "\n",
+    "        return result\n",
+    "    \n",
+    "    \n",
+    "    def recommend(self, user_code_id, item_code_id, topK=10):\n",
+    "        estimations=np.tile(self.row_means[:,None], [1, self.train_ui.shape[1]]) +np.tile(self.col_means, [self.train_ui.shape[0], 1])\n",
+    "        \n",
+    "        top_k = defaultdict(list)\n",
+    "        for nb_user, user in enumerate(estimations):\n",
+    "            \n",
+    "            user_rated=self.train_ui.indices[self.train_ui.indptr[nb_user]:self.train_ui.indptr[nb_user+1]]\n",
+    "            for item, score in enumerate(user):\n",
+    "                if item not in user_rated:\n",
+    "                    top_k[user_code_id[nb_user]].append((item_code_id[item], score))\n",
+    "        result=[]\n",
+    "        # Let's choose k best items in the format: (user, item1, score1, item2, score2, ...)\n",
+    "        for uid, item_scores in top_k.items():\n",
+    "            item_scores.sort(key=lambda x: x[1], reverse=True)\n",
+    "            result.append([uid]+list(chain(*item_scores[:topK])))\n",
+    "        return result\n",
+    "    \n",
+    "    def estimate(self, user_code_id, item_code_id, test_ui):\n",
+    "        result=[]\n",
+    "        for user, item in zip(*test_ui.nonzero()):\n",
+    "            result.append([user_code_id[user], item_code_id[item], self.row_means[user]+self.col_means[item]])\n",
+    "        return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "matrix([[3, 4, 0, 0, 5, 0, 0, 4],\n",
+       "        [0, 1, 2, 3, 0, 0, 0, 0],\n",
+       "        [0, 0, 0, 5, 0, 3, 4, 0]], dtype=int64)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After subtracting columns and rows:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "matrix([[-0.375     ,  1.125     ,  0.        ,  0.        , -0.375     ,\n",
+       "          0.        ,  0.        , -0.375     ],\n",
+       "        [ 0.        , -0.66666667,  0.83333333, -0.16666667,  0.        ,\n",
+       "          0.        ,  0.        ,  0.        ],\n",
+       "        [ 0.        ,  0.        ,  0.        ,  0.66666667,  0.        ,\n",
+       "         -0.33333333, -0.33333333,  0.        ]])"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Recommend best unseen item:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[[0, 30, 4.375], [10, 40, 4.166666666666667], [20, 40, 5.333333333333333]]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Print estimations on unseen items:\n"
+     ]
+    },
+    {
+     "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>user</th>\n",
+       "      <th>item</th>\n",
+       "      <th>est_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>4.375000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>40</td>\n",
+       "      <td>4.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20</td>\n",
+       "      <td>70</td>\n",
+       "      <td>4.333333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user  item  est_score\n",
+       "0     0    60   4.375000\n",
+       "1    10    40   4.166667\n",
+       "2    20     0   3.333333\n",
+       "3    20    20   2.333333\n",
+       "4    20    70   4.333333"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toy_train_read=pd.read_csv('./Datasets/toy-example/train.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
+    "toy_test_read=pd.read_csv('./Datasets/toy-example/test.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
+    "\n",
+    "toy_train_iu, toy_test_iu, toy_user_code_id, toy_user_id_code, \\\n",
+    "toy_item_code_id, toy_item_id_code = helpers.data_to_csr(toy_train_read, toy_test_read)\n",
+    "\n",
+    "print('Training data:')\n",
+    "display(toy_train_iu.todense())\n",
+    "\n",
+    "model=selfBaselineIU()\n",
+    "print('After subtracting columns and rows:')\n",
+    "display(model.fit(toy_train_iu).todense())\n",
+    "\n",
+    "print('Recommend best unseen item:')\n",
+    "display(model.recommend(toy_user_code_id, toy_item_code_id, topK=1))\n",
+    "\n",
+    "print('Print estimations on unseen items:')\n",
+    "estimations=pd.DataFrame(model.estimate(toy_user_code_id, toy_item_code_id, toy_test_iu))\n",
+    "estimations.columns=['user', 'item', 'est_score']\n",
+    "display(estimations)\n",
+    "\n",
+    "top_n=pd.DataFrame(model.recommend(toy_user_code_id, toy_item_code_id, topK=3))\n",
+    "\n",
+    "top_n.to_csv('Recommendations generated/toy-example/Self_BaselineIU_reco.csv', index=False, header=False)\n",
+    "\n",
+    "estimations=pd.DataFrame(model.estimate(toy_user_code_id, toy_item_code_id, toy_test_iu))\n",
+    "estimations.to_csv('Recommendations generated/toy-example/Self_BaselineIU_estimations.csv', index=False, header=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model=selfBaselineIU()\n",
+    "model.fit(train_ui)\n",
+    "\n",
+    "top_n=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))\n",
+    "\n",
+    "top_n.to_csv('Recommendations generated/Projects/Project1_Self_BaselineIU_reco.csv', index=False, header=False)\n",
+    "\n",
+    "estimations=pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))\n",
+    "estimations.to_csv('Recommendations generated/Projects/Project1_Self_BaselineIU_estimations.csv', index=False, header=False)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1079,7 +1342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1136,7 +1399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1153,7 +1416,7 @@
        "0.7524871012820799"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1183,24 +1446,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 1.5239\n",
-      "MAE:  1.2268\n"
+      "RMSE: 1.5230\n",
+      "MAE:  1.2226\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "1.2267993503843746"
+       "1.2226271020019277"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1233,6 +1496,34 @@
     "\n",
     "sp.accuracy.mae(predictions, verbose=True)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1251,7 +1542,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.8"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "2a3a95f8b675c5b7dd6a35e1675edaf697539b1f0a71c4603e9520a8bbd07d82"
+   }
   }
  },
  "nbformat": 4,