calc_plot_embeded.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.1449413 -48.66819\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pickle\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "with open(\"vocab.json\", \"r\") as f:\n",
    "    vocab = json.load(f)\n",
    "s = [\"monday\", \"tuesday\", \"wednesday\", \"thursday\", \"friday\",\n",
    "    \"saturday\", \"sunday\", \"orange\", \"apple\", \"banana\", \"mango\",\n",
    "    \"pineapple\", \"cherry\", \"fruit\"]\n",
    "words = [(i, vocab[i]) for i in s]\n",
    "\n",
    "with open(\"embedding\", \"rb\") as f:\n",
    "    word_embedding_matrix = pickle.load(f)\n",
    "\n",
    "model = TSNE(n_components = 2, random_state = 0)\n",
    "tsne_embedding = model.fit_transform(word_embedding_matrix)\n",
    "words_vectors = tsne_embedding[np.array([item[1][0] for item in words])]\n",
    "\n",
    "x = words_vectors[:,0]\n",
    "y = words_vectors[:,1]\n",
    "\n",
    "print(x[0],y[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1BQAAAABIu4ycERwg6eertodK+spae7+kr5K2AQAAAACZLEOCoDGmqKRmkqZdVdxK0uykz7Mltc6IvgAAAAAA6ZNRM4JvShoiKeGqsoLW2sOSlPS7QAb1BQAAAABIh3QHQWNMc0nHrLVb0nj8M8aYzcaYzcePH0/vcAAAAAAAN5ERM4I1JbU0xuyXNF9SA2PMHElHjTFBkpT0+1hqB1tr37PWhlhrQ/Lnz58BwwEAAAAA3Ei6g6C1dpi1tqi1toSkjpJWW2u7SFoiqXtSte6SFqe3LwAAAABA+t3J9wiOk/SYMWaPpMeStgEAAAAAmcwrIxuz1q6VtDbp8x+SGmZk+wAAAACA9LuTM4IAAAAAgLsQQRAAAAAAHIYgCAAAAAAOQxAEAAAAAIchCAIAAACAwxAEAQAAAMBhCIIAAAAA4DAEQQAAAABwGIIgAAAAADgMQRAAAAAAHIYgCAAAAAAOQxAEAAAAAIchCAIAAACAwxAEAQAAAMBhCIIAAAAA4DAEQQAAAABwGIIgAAAAADgMQRAAAAAAHIYgCAAAAAAOQxAEAAAAAIchCAIAAACAwxAEAQAAAMBhCIIAAAAA4DAEQQAAAABwGIJgBlu7dq2MMVq7dm1mDyXD3cvnBgAAADiJV2YP4F5TuXJlrV+/Xg8++GBmDwUAAAAAUkUQzGC5c+dWaGhoZg8DAAAAAK6LpaG3ISIiQsYY7dixQ/Xr11eOHDkUFBSkkSNHKiEhQVLqyyfr1aunWrVqadWqVapcubJy5Mihhx56SJGRkSn62L59u1q2bKmAgAD5+vqqZs2a+uabb9zqbNq0SW3btlXRokXl6+ur4OBgDR8+XLGxsW71kvtdvHixHnroIfn4+KhcuXJasGDBbZ/XjSxcuFChoaHKkSOH/P391a5dOx04cOAWv1UAAAAAfzWCYBq0bt1ajRo1UmRkpDp16qQxY8bon//85w2P+eWXXzRgwAANHDhQCxcuVFBQkNq2bau9e/e66mzdulU1atTQyZMn9f777+uzzz5TYGCgGjVqpC1btrjqHThwQBUrVtSUKVO0fPlyDRgwQDNmzFDPnj1T9Lt37171799fgwYN0sKFC1WmTBl17NhRa9asyZDzmjJlip544gk9+OCD+vTTTzV16lTt3LlTdevW1dmzZ2/2VQIAAADIDNbau+anSpUq9m42atQoK8mOHTvWrbx3797Wz8/Pnjp1yq5Zs8ZKsmvWrHHtr1u3rvXy8rK7d+92lR09etR6eHjYl19+2VXWoEEDW65cOXvp0iVXWVxcnC1Xrpxt1apVqmNKSEiwV65csR9++KE1xtgTJ0649SvJrl+/3q294OBgW6tWrds6L2ttinM7e/aszZ07t+3Zs6fbcfv27bPe3t72jTfeSHXMAAAAAFInabP9C7IXM4Jp0L59e7ftjh076ty5c9q5c+d1j7n//vt1//33u7YLFCigAgUKuJZQxsbG6uuvv1a7du3k4eGhuLg4xcXFyVqrRo0aad26da5j//zzT73wwgsqXbq0fHx85O3tra5du8paqz179rj1W6xYMbd7Fj09PdWuXTtt3LgxxbLP2z2v9evX688//1Tnzp1d442Li1PRokVVrlw5tzEDAAAAuHvwsJg0KFiwYKrbMTExKfYly5s3b4oyHx8fXbx4UZJ08uRJxcfHa8yYMRozZkyqbSQkJMjDw0M9e/bUqlWr9M9//lMVK1ZUzpw5tXHjRvXr18/V3vXGmlx2+fJlHT9+3G3/jc4rNceOHZMkNWrUKNX9AQEBqZYDAAAAyFwEwTQ4evSoSpUq5bYtSUWKFFFcXFya2vT395eHh4f69eunbt26pVrHw8NDFy9e1OLFixUREaEBAwa49u3YseO6Y02tLFu2bMqfP3+K8uudV2oCAwMlSbNmzVL58uVT7M+VK1eqxwEAAADIXATBG4jcFqPxK6J16HSsCvv7qtjhPyVJCxYs0NChQ1315s+fLz8/Pz300EP68ccf09RXzpw5Vbt2bW3fvl2VK1eWh0fqq3YvXbqk+Ph4eXt7u5XPmjUr1fq///67NmzY4FoeGh8fr08++USPPvpoij5udF6pqVGjhnLlyqW9e/eqe/fut3qqAAAAADIZQfA6IrfFaNjCHYq9Ei9Jijkdq+ifE5dCvv/++0pISFDVqlW1YsUKTZs2TREREfL3909XnxMmTFCdOnUUFhamXr16KSgoSCdOnNDWrVsVHx+vcePGKU+ePAoNDdXrr7+uoKAg5cuXTzNmzLju8s2CBQuqQ4cOGj16tPLnz6/Jkydr9+7dmjx5coq6t3teuXPn1vjx49WvXz8dP35cjz/+uPLkyaOYmBh9/fXXqlevnjp16pSu7wQAAABAxuNhMdcxfkW0KwQmi0t6uMrixYu1cuVKtWzZUnPmzNGIESP00ksvpbvPypUra9OmTQoMDFT//v3VuHFjDRgwQDt27FCdOnVc9T766CNVqVJF/fr1U48ePVSoUCFNnDgx1TbLlCmjSZMm6bXXXtPf/vY37dmzRx999JHq16+fom5azqtPnz5asmSJoqOj1bVrVz3++OMaNWqU4uLiVLFixXR9HwAAAADuDJP4hNK7Q0hIiN28eXNmD0OSVHJolK79Zk5/O1dnvvtIV65ckZfX3T+ZWq9ePcXFxenbb7+9Yb2IiAiNHj06y5wXAAAAcK8yxmyx1obc6X6YEbyOwv6+mT0EAAAAALgjCILXMTgsWL7enm5lXtd5gAsAAAAAZCUsDb2Ba58aOjgsWK0rpf4qBQAAAABIr79qaSg3hN1A60pFCH4AAAAA7jmsdQQAAAAAhyEIAgAAAIDDEAQBAAAAwGEIggAAAADgMARBAAAAAHAYgiAAAAAAOAxBEAAAAAAchiAIAAAAAA5DEAQAAAAAhyEIAgAAAIDDEAQBAAAAwGEIggAAAADgMARBAAAAAHAYgiAAAAAAOAxBEAAAAAAchiAIAAAAAA5DEAQAAAAAhyEIAgAAAIDDEAQBAAAAwGEIggAAAADgMARBAAAAAHAYgiAAAAAAOAxBEAAAAAAchiAIAAAAAA5DEAQAAAAAhyEIAgAAAIDDEAQBAAAAwGEIggAAAADgMARBAACQKfbv3y9jjGbNmpXZQwEAxyEIAgAAAIDDEAQBAAAAwGEIggAAONzevXvVtWtXlSxZUr6+vipVqpSeffZZnTp1yq1ejx49VLRoUX3//feqWrWqsmfPrhIlSmjSpElu9WbNmiVjjNatW6fWrVvLz89PgYGB6tevn2JjY286nq+//loNGzZUrly5lDNnToWFhWnnzp0Zes4A4HQEQQAAHO7QoUMqWrSo3nzzTa1YsUIjR47UV199paZNm6ao++eff6pDhw7q3r27IiMjVa9ePfXv3z/V+/y6dOmiMmXKaOHChXr++ef1/vvv69lnn73hWKKiotSwYUP5+flpzpw5mjdvns6ePavatWvr999/z6hTBgDH88rsAQAAgMxVp04d1alTx7Vdo0YNlSlTRrVr19a2bdtUqVIl176zZ8/qvffeU8eOHSVJTZo0UUxMjEaNGqXu3bvLGOOq27RpU7322muSpMaNG8sYo5EjR2r48OEqW7ZsqmMZMGCA6tatq8WLF7vK6tevr1KlSun111/Xm2++mZGnDgCOxYwgAAAOd/nyZb3yyisqV66cfH195e3trdq1a0uSoqOj3ep6enrqiSeecCvr2LGjDhw4oJiYGLfy9u3bp6iXkJCgjRs3pjqOPXv26JdfflHnzp0VFxfn+smRI4eqV6+udevWpfdUAQBJCIIAADjcsGHDFBERoS5duigqKkobN27UwoULJUkXL150qxsQECBvb2+3soIFC0pSiiCYXH6zesmOHTsmSerVq5e8vb3dfj7//HP98ccfaTxDAMC1WBoKAIADRW6L0fgV0Tp0OlaHps1W3WZPaMSIEa79586dS/W4U6dO6cqVK25h8OjRo5KkIkWKuNU9evSoypcvf9N6yQIDAyVJY8eOVaNGjVLsz5Yt262cGgDgFjAjCACAw0Rui9GwhTsUczpWVlLcpYvafOBPRW7730zdzJkzUz02Pj5en332mVvZ/PnzVbx48RQBb8GCBSnqeXh46NFHH0217eDgYJUoUUI//fSTQkJCUvw8/PDDaThbAEBqmBEEAMBhxq+IVuyVeNd29lJVdOY/X2lgxL+Vo18zLVy4UN9//32qx+bKlUtDhgzRiRMndP/99+ujjz7SqlWrXK+MuNqyZcs0ePBgNW7cWBs3btTo0aPVrVu36z4oxhijd955R61atdLly5fVvn175cuXT0ePHtX333+v4sWLa+DAgRn3RQCAgzEjCACAwxw67f4uv7yN+si3zKP6bcV0dejQQWfPntVHH32U6rG5c+fW/PnzNXv2bLVq1Upr1qzRxIkT1b179xR158yZo927d6tNmzZ6/fXX9fTTT+vdd9+94diaNm2qdevW6fz58+rdu7fCwsI0ZMgQHTlyRNWrV0/7SQMA3DAjCACAwxT291XMVWHQM0ce5W/1gor4++q7oQ1c5dbaVI+vUaOGNm3adPN+Chd2ew3EtUqUKJFqH9WrV9fnn39+0/YBAGnHjCAAAA4zOCxYvt6ebmW+3p4aHBacSSMCAPzVmBEEAMBhWldKfKhL8lNDC/v7anBYsKscAHDvM9db9pEZQkJC7ObNmzN7GAAAAACQKYwxW6y1IXe6H5aGAgAAAIDDpDsIGmOKGWPWGGN+Nsb8ZIwZkFSe1xiz0hizJ+l3QPqHCwAAAABIr4yYEYyTNMha+4CkUEn9jDEPShoq6Str7f2SvkraBgAAAABksnQHQWvtYWvt1qTPZyX9LKmIpFaSZidVmy2pdXr7AgAAAACkX4beI2iMKSGpkqQfJBW01h6WEsOipAIZ2RcAAAAAIG0yLAgaY/wkfSbpH9baP2/juGeMMZuNMZuPHz+eUcMBAAAAAFxHhgRBY4y3EkPgXGvtwqTio8aYoKT9QZKOpXastfY9a22ItTYkf/78GTEcAAAAAMANZMRTQ42k6ZJ+ttZOuGrXEkndkz53l7Q4vX0BAO5tERERMsZo165dCgsLU86cOVW8eHHNnDlTkvThhx+qXLly8vPzU/369fXLL7+4jp0/f74aNGig/Pnzy8/PT5UqVdLs2bNT9GGM0YgRI/TWW2+pZMmSypUrl+rWrauffvrJrV58fLxGjBihoKAg5ciRQw0aNNCuXbtkjFFERIRb3eXLl6t69ery9fVVnjx51Lp1a0VHR2f8FwQAQAbJiBnBmpK6SmpgjPkx6aeppHGSHjPG7JH0WNI2AAA31a5dOzVr1kyRkZGqUqWKnnrqKQ0fPlyTJ0/WuHHjNHPmTEVHR6tTp06uY3799Ve1bdtWc+fOVWRkpFq0aKHevXtrypQpKdqfM2eOoqKiNHHiRM2cOVMHDhxQq1atFBcX56ozatQovfLKK+rWrZsWL16ssLAwtWzZMkVby5cvV7NmzeTn56ePP/5YkydP1s6dO1WrVi3FxMTcmS8IAIB08kpvA9babyWZ6+xumN72AQDOM3jwYHXr1k2SFBISoqVLl2rq1Knat2+fcufOLUk6fPiwBgwYoN9++0333Xefhg8f7jo+ISFB9erV0+HDhzV58mT17dvXrX1vb299/vnn8vb2dpW1a9dOGzduVI0aNXTq1Cm9+eab6tu3r1599VVJ0mOPPSZvb28NGjTIra0RI0aoVKlS+uKLL+Tllfif1erVq6ts2bJ6/fXXNWHCBAEAcLfJ0KeGAgCQER5//HHX54CAABUoUEChoaGuEChJ5cqVkyT9/vvvkqQ9e/boySefVJEiReTt7S1vb29NmzYt1SWayaEuWYUKFSRJBw4ckCTt2LFD58+fV7t27dyOa9u2rdv2+fPntXXrVnXo0MEVAiWpZMmSqlmzpr7++us0nT8AAHcaQRAAcNcJCAhw286WLVuqZZJ08eJFnTt3To899pi2b9+ucePG6ZtvvtGmTZv01FNP6dKlSynaz5s3r9u2j4+Pqy0pcbZRkgoUcH/zUcGCBd22T506JWutgoKCUvRRqFAhnTx58qbnCgBAZkj30lAAADLb+vXr9dtvv+mbb75RrVq1XOVX3/N3O5KD3bFjx1S+fHlX+dGjR93qBQQEyBijI0eOpGjjyJEjCgwMTFP/AADcacwIAgAyTeS2GNUct1olh0ap5rjV2nX4ll9D6+bChQuS5Lbc89SpU1q8OG0PrK5QoYJy5sypTz75xK382u2cOXOqSpUq+uSTTxQfH+8q/+233/T999+rbt26aeofAIA7jRlBAECmiNwWo2ELdyj2SmKAijkdq+ifU33l7E3VqFFDuXPnVr9+/TR69GidP39e//rXv5QvXz6dOXPmttsLCAjQP/7xD73yyivKlSuXGjVqpK1bt2r69OmSJA+P//076pgxY9SsWTM1b95c4eHhOnfunEaNGqU8efKkeLAMAAB3C2YEAQCZYvyKaFcITBaXkJCmtvLnz69FixYpPj5ebdu21bBhw9S7d2916dIlzeMbPXq0hg0bptmzZ6tly5b64osvNGvWLElSnjx5XPWaNGmiqKgonT59Wu3bt1ffvn31wAMP6Ntvv1XhwoXT3D8AAHeSsdZm9hhcQkJC7ObNmzN7GACAv0DJoVFK7b9ARtK+cc3+6uHckk8++UTt27fXunXrVLt27cweDgDgHmSM2WKtDbnT/bA0FACQKQr7+yrmdGyq5XeDH374QVFRUapWrZqyZ8+uLVu2aNy4cQoNDXV7IA0AAFkRQRAAkCkGhwW73SMoSb7enhocFpyJo/ofPz8/rVu3Tu+8847+/PNPFShQQO3bt9fYsWNljMns4QEAkC4EQQBApmhdqYikxHsFD52OVWF/Xw0OC3aVZ7by5ctr7dq1mT0MAADuCIIgACDTtK5U5K4JfgAAOAlPDQUAAAAAhyEIAgAAAIDDEAQBAAAAwGEIggAAAADgMARBAAAAAHAYgiAAAAAAOAxBEAAAAAAchiAIAAAAAA5DEAQAAAAAhyEIAgAAAIDDEAQBAMBdq169eqpXr15mDyOF/fv3yxijWbNmZfZQACBNCIIAAAAA4DAEQQAAAABwGIIgAAC4JZs3b5YxRt9++62rbNKkSTLGaMSIEa6yPXv2yBijZcuWSZL27dunzp07K3/+/PLx8VHFihW1aNGiFO3Pnz9f5cqVk4+Pj8qXL59qnbVr18oYoyVLlui5555Tvnz5lD9/fnXp0kWnT592qxsXF6exY8e62ixcuLAGDRqkixcvutV56aWXVLp0aWXPnl358uVTrVq13M7xwoULCg8PV2BgoPz8/NSyZUsdPHgwxdg2bdqktm3bqmjRovL19VVwcLCGDx+u2NhYV53nnntOBQsW1JUrV9yOPXfunHLlyqVhw4Zd7+sHgAxFEAQAALekcuXK8vf31+rVq11lq1evlq+vb4oyT09P1a5dW7///ruqVaum7du364033tCSJUtUuXJlPfHEE1qyZInrmFWrVqlTp066//77tXDhQg0ePFgDBgxQdHR0qmMZMGCAjDGaN2+eRo4cqc8++0wDBgxwq9OlSxf961//UqdOnRQVFaVhw4Zp+vTp6ty5s6vOq6++qjfeeEP9+/fXihUrNHPmTDVs2FAnT5501enTp4+mTZumgQMHauHChQoODlanTp1SjOnAgQOqWLGipkyZouXLl2vAgAGaMWOGevbs6aoTHh6uY8eOpQi5c+fO1fnz5/X000/f7DIAQMaw1t41P1WqVLEAAODu1bJlS1uvXj1rrbXx8fE2ICDADhw40Hp5edmzZ89aa63t0KGDrVatmrXW2qeeesrmy5fPnjhxwq2dRo0a2UceecS1XaNGDfvAAw/Y+Ph4V9mGDRusJFu3bl1X2Zo1a6wk261bN7f2+vXrZ318fGxCQoK11tp169ZZSXb27Nlu9ebMmWMl2W3btllrrW3WrJlt06bNdc93165d1sPDw44dO9atvG/fvlaSnTlzZqrHJSQk2CtXrtgPP/zQGmPczr9u3bq2QYMGbvUrVapkw8LCrjsOAM4habP9C7IXM4IAAOCW1a9fX+vXr9fFixf1448/6vTp0xoyZIh8fHz0zTffSEpcvtmgQQNJ0vLly9W0aVPlyZNHcXFxrp+wsDBt375df/75p+Lj413LKj08/vdXk2rVqqlEiRKpjqNZs2Zu2xUqVNClS5d09OhRV7/ZsmXTE0884dZv48aNJUnr1q2TJFWtWlXLli3Tiy++qG+//VaXL192a/eHH35QQkKC2rdv71besWPHFGP6888/9cILL6h06dLy8fGRt7e3unbtKmut9uzZ46oXHh6uNWvWuMo2bdqkbdu2qU+fPjf+8gEgAxEEAQDALWvQoIEuXbqk77//XmvWrNEjjzyiggULqlatWlqzZo1++uknHT16VPXr15ckHTt2TB988IG8vb3dfgYPHixJ+uOPP3TixAlduXJFBQsWTNFfamWSlDdvXrdtHx8fSXLd/3fs2DFdvnxZfn5+bv0WKFDA1a8kDR8+XKNHj9aSJUtUu3ZtBQYGqmfPnjpx4oQk6fDhw6mOI7Vx9ezZU1OmTFH//v21cuVKbdq0Se+8847buCSpTZs2KlSokKZOnSpJmjJligoXLqwWLVqk/qUDwB3gldkDAAAAWUeFChWUL18+rV69Wtu2bXPN/DVo0EALFixQsWLFlC1bNtWsWVOSFBgYqNq1a+uFF15Itb3ChQvLy8tL3t7ertm8qx09elT33XffbY8zMDBQ2bNnd81SptavJHl7e+uFF17QCy+8oCNHjujzzz/XwIEDdeHCBX388ccKCgpyjaNUqVJu47raxYsXtXjxYkVERLjdq7hjx44UfXt7e6t379569913NWTIEM2fP1+DBg2Slxd/LQPw1+H/cQAAwA1FbovR+BXROnQ6VoX9fXX/I49q5cqV+vnnnxUeHi4pMQgOGzZMuXPnVrVq1ZQjRw5JUpMmTbR+/XqVL19evr6+1+2jatWq+vTTTxUREeFaHvrDDz9o//79aQqCTZo00auvvqozZ86oYcOGt3RMoUKF1Lt3by1btkw7d+6UlLg81cPDQwsWLNDQoUNddefPn+927KVLlxQfHy9vb2+38uu9cL5Pnz4aO3as2rVrp0uXLvGQGAB/OYIgAAC4rshtMRq2cIdir8RLkmJOx+qid0kd3bjM9WRQKfGJorlz59aaNWs0cuRI1/H//Oc/9eijj6pOnTp67rnnVKJECZ06dUo7d+7Ur7/+qhkzZkiSRo8ercaNG6t169bq06ePjh8/rlGjRqlQoUJpGne9evX05JNPqm3btho4cKAeffRReXh4aP/+/Vq2bJleffVVlS1bVq1atdIjjzyiypUrKyAgQNu2bdPy5ctd9+slPyF05MiRSkhI0B9//KEZM2akeFVFnjx5FBoaqtdff11BQUGKj4/XU089pfz586c6viJFiqhFixZatGiRfH199dJLL103NALAnUAQBAAA1zV+RbQrBCbzLPKQJCkkJES5c+eWJHl4eKhOnTpasmSJ6/5ASSpevLg2b96siIgIDR8+XMePH1dgYKAeeughde/e3VWvUaNGmjt3riIiIvS3v/1NZcqU0ZtvvqmJEyemeexz5szRpEmTNGPGDL388svy8fFRiRIlFBYW5rrHr06dOvrkk0/0zjvv6MKFCypevLiGDBmiF1980dXO1KlT5efnp3//+986c+aMihYtqpdffln9+vVz6++jjz7Ss88+q379+il79ux64okn9Le//c3tdRVXa9eunRYtWqRcuXKl+RwBIK1M4hNK7w4hISF28+bNmT0MAACQpOTQKKX2NwUjad+4ZqnsuXd9/fXXqlevnr766ivXvZHXstbqypUrypYt203b69y5s7777jtJiTOYzAgCkCRjzBZrbcid7oenhgIAgOsq7J/6fX3XK79X9ejRQ/Xq1ZMkNWzYUMYY9ejRQyVKlFCXLl00Y8YMlStXTtmyZVNUVJT2798vY0yKcDdx4kQFBQXJ29tb8+bNU6tWrVL0dfz4cfXp00dly5ZVjhw5VKxYMXXq1EkxMTGuOp9++qmMMdq+fXuK4+vVq6fq1atn6PkDuPcQBAEAwHUNDguWr7enW5mvt6cGhwWnuc3IyEhNmDDBrcwYo4iIiDS3mVGuF+BeeuklvfXWW5Kkd955R+vXr9dLL70kSVqzZo0mTJigUaNGafny5Xr44YdTbXv69On6xz/+oSNHjsjLy0uhoaH67LPPdOrUKbd6J0+eVPbs2TV27FgtX75c48eP1549e1SzZk3Xayhat26twoULu15BkSw6Olpff/017yQEcFPcIwgAAK6rdaUikuT21NDBYcGu8rSIjIzUqlWrNHDgwIwa5h1XunRpPfDAA5KkBx98UKGhoa59p06d0pYtW9webLN//3634xMSEhQREaGwsDAtX77cVf7xxx+neDl9cHCw272R8fHxqlmzpooXL64vvvhCbdq0kZeXl55++mm98cYbGj9+vHLmzCkp8X5Gf39/dejQIcPOHcC9iRlBAABwQ60rFdF3Qxto37hm+m5og3SFwHtRaGjoTZ9uevDgQR08eFDt27d3K3/iiSdSfX/g5MmT9cgjj8jPz09eXl4qXry4pMQZv2TPPPOMLly4oI8++khS4rsMZ8+erW7dut3wVR0AIBEEAQBwtN27d6tNmzYqUKCAsmfPruLFi6tdu3aKi4vTrFmzZIxJMbsVEREhY4xbmTFGI0aM0FtvvaWSJUsqV65cqlu3rn766Se3et26ddPs2bMVExMjY4yMMa6Xu48ePdrV1969e9W1a1cFBATIGKNSpUrp2Wef1alTpxQXF6exY8eqQIECMsaoYMGCGjRokGvZpLVWpUqVUvny5VW6dGllz55d+fLlU61atfTtt9+6xnLhwgWFh4crMDBQfn5+atmypQ4ePJjiO9q0aZPatm3rmrnr2rWrhg8frtjYWElSUFCQnnvuORUsWFBXrlxxO/bixYvKlSuXaxlp8tNKk3l5eSkwMNCtbNKkSQoPD1ejRo20cOFCbdy4URs2bHC1l6xw4cJq1aqVpkyZIkn65JNPdPLkSZaFArglBEEAABysefPmiomJ0eTJk7VixQqNGzdOPj4+SkhIuO225syZo6ioKE2cOFEzZ87UgQMH1KpVK8XFxbnqJL9uwtfXV2+++ab69OkjT0/PFG0dOnRIRYsWVVhYmCRp5MiR+uqrr9S0aVN16dJF//rXv/TUU08pW7ZsCg0N1fTp012vafjyyy+1b98+/frrr+rfv79WrFihmTNnqmHDhjp58qSrjz59+mjatGkaOHCgFi5c6Hpn4LUOHDigihUr6h//+IekxFm8GTNmqGfPnpISQ3B4eLiOHTumRYsWuR27fv16nT9/3vXC+KNHj7rtj4uL0x9//OFWNn/+fDVs2FCvv/66GjdurKpVq6pAgQKpfufh4eHasmWLtmzZoqlTp6p27dp68MEHU60LAFfjHkEAABzqxIkT2rNnjxYvXqyWLVu6ylMLQ7fC29tbn3/+uby9vV1l7dq108aNG1WjRg2dOnVKs2bNUtmyZXX+/HkNGDBAkvTqq69q6NChbm3VqVNHderUcT1ApkuXLipTpoxq166tDRs2uJZAHjlyRN9++63efvttde3aVT/++KOmTp2qnDlzqnHjxq4+JKlFixauz9HR0Zo3b55efvllV9+NGzfWuXPnXDNskdtiku6NzK7C/jXU2P+Yq52QkBB169ZNRYsWlZR432DdunU1depUt+Wfa9euVePGjVWjRg0VK1ZMCxYs0FNPPeXa/9lnn7kFZSlxpjI5MCebOXNmqt95gwYN9MADD2jgwIH67rvvNHfu3OtdHgBww4wgAAAOFRgYqFKlSmno0KF6//33tWfPnnS199hjj7mFwAoVKkhKnFGTpB07duj8+fMqUaKE23HXPixFki5fvqxXXnlFb7/9tqTEkFm7dm1Jkqenp5544gnFxcXpmWee0S+//KLs2bNLkpYuXaqlS5eqVq1aWrZsmV588UV9++23unz5slv7P/zwgxISElLcs5c8lq2/ndKwhTsUczpW8ZcuaGfkuxrzf+GSpEaNGqlr166udwYmCw8P15o1a9y+xwMHDqhPnz7y8PDQqFGjtGLFCvXs2VMrVqzQO++8o0GDBqUIfU2aNNGKFSv0yiuvaNWqVRo+fLjmz59/3e+9b9++WrdunfLly6cnnnjiuvUA4GoEQQAAHMoYo5UrVyokJETDhg1T2bJlVapUKU2ePPm6x8yYMUOTJk2SJPn7+7vty5s3r9u2j4+PpP/d13b48GFJcoW2ZHXq1HHbXrt2rerWratRo0a5XsWwceNGLVy4UFLiUzT9/Pzk7e2tmjVrSkqceZSk1atXy8vLS7Nnz9bo0aO1ZMkS1a5dW4GBgerZs6dOnDjhNpZr79lL3v5i52HFXomXJP2x7E2d3faFfMsmvptvypQpeueddyQl3o+YrE2bNipUqJDbKx38/f1dM5G9evXSm2++qdWrV6tVq1aaOXOm5s+fr4CAALcxjBw5Un369NEbb7yhNm3a6D//+Y9WrFih60k+9x49eri+cwC4GYIgAAAOVqpUKX3wwQc6fvy4tm3bpgYNGig8PFxffPGFK7Alz6YdOnRIzzzzjPLnzy9JWrVq1W31FRQUJMn9gSeS9O6777o+X758WWvXrtWGDRvUtWtXlS9fXpJUtWpVV/D08vLSpk2bXD8vvfSSPD09FRUVpT179qhdu3YqWLCgXnjhBe3YsUOHDx/WG2+8oc8++0z9+vVzG8u19+wlb5+6kDjTZ+Mu68KeDcpd7W8KbByuEi98rj59+rieyjl//nzNmTNHUuKsZe/evTVr1izlyJFDOXLk0N///ne3p4IOGDBAv/32my5evKjNmzerVq1a2r9/v9t7C319fTV58mQdP35cZ8+e1eeff66SJUvKWpvquxY///xzGWN4SAyA20IQBADAQSK3xajmuNUqOTRKNcetVuS2GEmJs4MVK1Z0veh9586duu+++1yfJWnPnj2Kj4/X+fPnJUkhISEp2rfWpliGufW3U6o5brW6LDwsj2zZ9d89v7qeuHl1+9d+9vT01JdffunaTr5PLi4uTmfOnFFISIhrNjN37tx69dVXFRMTo759+7r1X6hQIfXu3VuNGjVytV+tWjV5eHhowYIFbnWTl2AG5Ehc4mrjr0g2QcYjMcwV9k8MgNe+cD5Znz59dObMGbVr106XLl1yPSTmTvjvf/+rpUuXatSoUWrdurXKlClzx/oCcO/hYTEAADhE5LYYDVu4w7Xkcd/u/6rzlOfVtfOTals/RPHx8Zo1a5a8vLzUoEEDPfLIIypdurQGDx6st956S19//bUkuV6x0KNHD61du1a1atWSlLhk8tVXX9WCBQsUEBCg+vXrS5I+2XJQPg+WlUd2P/kUe1gHf9koSerfv7/i4uL03nvvSZICAgLUq1cvnT59WpI0bdo019j79u2r77//XlJiiGvbtq0GDhyoRx99VB4eHqpSpYpWrVqlsmXLqkaNGmrVqpUeeeQRVa5cWQEBAdq2bZuWL1/umjVLfkLoyJEjlZCQoKpVq2rlypVatmyZJOnxh4K0Ms5TscqpbIWD9eemRfLNE6gGjR5W27ZtFRMTk+p3XKRIEbVo0UKLFi1SixYtVKxYsYy5eKkIDw/X999/rxo1arjupQSAW8WMIAAADjF+RbQrBEqSZ84AmVz5NHvq22rZsqWefPJJHTp0SJ9//rmqVKkiLy8vLV68WMWKFdPGjRtdSzObNm0qSa53461Zs0aSVLt2bS1fvtx1X1+yK/H/exWFb9nQpM69NWnSJLf7ETt27Khy5cq5llKWKVNG2bJlkySdPXvW9eL0Z555RhEREfr000/VqlUrtW3bVvv27ZMk1wxcnTp19OWXX6pXr15q0qSJJk+erCFDhujf//63q7+pU6eqV69eeu2119SmTRvt2rVL8+bNkyRVvi9AY/9WQUX8fZW/5RDlLlpWp1ZN0dQx/6dChQpp4sSJ1/2ek+/Zu9NLNdeuXetaSpv8LkYAuFXm6pucM1tISIjdvHlzZg8DAIB7UsmhUUrtv/pG0r5xzW56/KpVq/TYY49pzZo1qlevniSpRIkSOnbsmH799VcVKlTIVXft2rWqX7++Cj75irIX/18wPLdjlf5Y9qaK9J2ug5OfcrVRr14913LLiIgIjR49WleuXHG7v+5GXnzxRU2cOFGHDh1K8RTOv1rnzp313Xff6ddff5WHB//mDuD2GGO2WGtTrr3PYCwNBQDAIQr7+yrmdGyq5ekRGhrqFgKvls/PR+dSKS+YO3sqpbdv27Ztio6O1sSJE/XMM89kagjcsGGDfvzxR3388ceaMGECIRDAXY0gCACAQwwOC3a7R1CSfL09NTgsOF3tJj+BMzUdqxbT3AOebn1KUp86pdLVZ7I2bdro6NGjCgsL0+jRozOkzbSqXr26/Pz81L17d4WHh2fqWADgZgiCAAA4ROtKRSQl3it46HSsCvv7anBYsKs8rYwxKcqSXz3x6H159FBIeVefforVH5Ial099BvF27d+/P0PayQh30+02AHAzBEEAABykdaUitxX8IrfFuEJcjhP/veXjrn71xMCBjV19Nmw4Qb/d5Njkl6LHxsYqV65ct9wnAODWEQQBAECqrn3dxIlzie8H/HbPcSU9K+a6goKCVLduXY0dO1b58uVTgQIFNGfOHP3yyy837ffBBx+UJL3++ut6/PHH5enpmeo7CwEAacddzAAAIFXXvm4i2fxNv9/S8XPmzFFoaKj69++vHj16qHjx4hoxYsRNj2vevLnCw8P17rvvqnr16qpateptjx0AcGO8PgIAAKQqva+bAADcvr/q9RHMCAIAgFRd77US6X3dBAAg8xEEAQBAqgaHBcvX29OtLCNeNwEAyHw8LAYAAKTqTr1uAgCQ+QiCAADgum73dRMAgKyBpaEAAAAA4DAEQQAAAABwGIIgAAAAADgMQRAAAAAAHIYgCAAAAAAOQxAEAAAAAIchCAIAAACAwxAEAQAAAMBhCIIAAAAA4DAEQQAAAABwGIIgAAAAADgMQRAAAAAAHIYgCAAAAAAOQxAEAAAAAIchCAIAAACAwxAEAQAAbiAyMlITJkxwK1u7dq2MMVq1alUmjerWGGMUERGR2cMAcBciCAIAANxAakEQALI6giAAAMBd6tKlS5k9BAD3KIIgAADAdfTo0UOzZ89WTEyMjDEyxqhEiRKu/RcuXNBzzz2nfPnyKX/+/OrSpYtOnz7t2r9//34ZYzRr1iy3dpOXlq5du9ZVVq9ePdWqVUtLly5VpUqV5OPjo3fffVeSNHHiRD3wwAPy9fVVQECAQkJCtGjRItex8fHxGjFihIKCgpQjRw7Vq1dPP/30U4rz2bt3r7p27aqSJUvK19dXpUqV0rPPPqtTp0656rz22mvy8fHR8ePH3Y611qpUqVJ68skn0/BNArjbeGX2AAAAAO5WL730ko4fP65NmzZpyZIlkiQfHx+dOXNGkjRgwAA1b95c8+bNU3R0tIYMGSJPT0/Nnj07Tf3t3r1b/fv310svvaRSpUopb968mjt3rgYNGqSRI0eqdu3aio2N1X/+8x+dPHnSdVxERIReeeUVDRw4UI0bN9bmzZvVsmXLFO0fOnRIRYsW1ZtvvqmAgAD9+uuveuWVV9S0aVOtX79ekvTUU0/ppZde0syZMzVkyBDXsV9++aX27dunGTNmpOncANxdCIIAAADXUbp0aeXPn1/ZsmVTaGioqzx5Jq9OnTqaNGmSJKlx48aKjo7WtGnTNGvWLBljbru/EydO6Msvv1TFihVdZe+9954efvhhjRw50lXWtGlT1+dTp07pjTfe0DPPPKPXXnvNNRZPT08NHTrUrf06deqoTp06ru0aNWqoTJkyql27trZt26ZKlSopb9686tChg9577z0NHjzYdR5Tp05VcHCw6tWrd9vnBeDuw9JQAACANGrWrJnbdoUKFXTp0iUdPXo0Te2VKFHCLQRKUtWqVfXjjz/q73//u1atWqULFy647d+xY4fOnz+v9u3bu5V37NgxRfuXL1/WK6+8onLlysnX11fe3t6qXbu2JCk6OtpVLzw8XL/88ou++uorSdLhw4e1dOlS9enTJ03nBeDuQxAEAABIo7x587pt+/j4SJIuXryYpvaCgoJSlHXr1k2TJ0/WDz/8oLCwMOXNm1d/+9vftH//fkmJIU2SChYs6HbctduSNGzYMEVERKhLly6KiorSxo0btXDhwhRjfvTRRxUSEqIpU6ZIkqZNmyYvLy917949TecF4O5DEAQAALhDsmfPLilxJu5qf/zxR6r1U1tOaoxRnz59tHHjRp04cUKzZ8/Wxo0b1aFDB0n/C4/XzkKmNis5f/58devWTSNGjFCDBg1UtWpV+fv7pzqWZ599VosXL1ZMTIymTZumdu3apQi+ALIugiAAAMBVIrfFqOa41So5NEo1x63W4bNxio2NTVNbBQsWlI+Pj3bu3OlWHhUVlab2AgIC1KFDB7Vv397V5sMPP6ycOXNqwYIFbnXnz5+f4vgLFy7I29vbrWzmzJmp9vXkk08qV65c6tSpkw4cOKC+ffumacwA7k48LAYAACBJ5LYYDVu4Q7FX4iVJMadjFXvOTydPntTkyZMVEhLimuW7FcYYdejQQdOnT1fZsmUVHBysqKgot9dG3MwzzzyjXLlyqXr16ipQoIB2796tDz/8UI0bN5Yk+fv76/nnn9fLL7+sXLlyqXHjxtq0aZOmT5+eoq0mTZpo9uzZqlChgsqUKaOFCxfq+++/T7VfX19f9ejRQ2+88YYqVKigGjVq3PKYAdz9CIIAAABJxq+IdoXAZD7lH1O+I7s1fPhwnT59Wvfdd1+K9wLeyMSJE5WQkKCIiAglJCSoffv2mjRpkpo3b35Lx9esWVMzZ87Uhx9+qDNnzqhw4cLq0qWLRo8e7aoTEREha62mTZumt99+W9WqVdPSpUtVvnx5t7YmTZoka61efPFFSYlPH/3oo4/06KOPptp3u3bt9MYbb/CQGOAeZKy1d7YDY5pImijJU9I0a+2469UNCQmxmzdvvqPjAQAAuJ6SQ6OU2t+MjKR945qlsufe9uKLL2rixIk6dOiQcufOndnDARzBGLPFWhtyp/u5ozOCxhhPSe9IekzSQUmbjDFLrLX/vZP9AgAApEVhf1/FnE55P2Bhf99MGE3m2bZtm6KjozVx4kQ988wzhEDgHnSnHxbzqKS91tpfrbWXJc2X1OoO9wkAAJAmg8OC5evt6Vbm6+2pwWHBmTSizNGmTRv17NlTjRo1cluCCuDecafvESwi6fertg9KqnaH+wQAAEiT1pWKSEq8V/DQ6VgV9vfV4LBgV7lTJL+jEMC9604HwZQvw5H70ntjzDOSnpGk4sWL3+HhAAAA3FjrSkUcF/wAOM+dXhp6UFKxq7aLSjp0dQVr7XvW2hBrbUj+/Pnv8HAAAAAAAHc6CG6SdL8xpqQxJpukjpKW3OE+AQAAAAA3cEeXhlpr44wxz0laocTXR8yw1v50J/sEAAAAANzYHX+hvLV2maRld7ofAAAAAMCtudNLQwEAAAAAdxmCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMOkKwgaY8YbY3YZY/5jjFlkjPG/at8wY8xeY0y0MSYs3SMFAAAAAGSI9M4IrpT0kLX2YUm7JQ2TJGPMg5I6SiovqYmkd40xnunsCwAAAACQAdIVBK21X1pr45I2N0gqmvS5laT51tpL1tp9kvZKejQ9fQEAAAAAMkZG3iP4lKQvkj4XkfT7VfsOJpUBAAAAADKZ180qGGNWSSqUyq4XrbWLk+q8KClO0tzkw1Kpb6/T/jOSnpGk4sWL38KQAQAAAADpcdMgaK1tdKP9xpjukppLamitTQ57ByUVu6paUUmHrtP+e5Lek6SQkJBUwyIAAAAAIOOk96mhTSS9IKmltfbCVbuWSOpojPExxpSUdL+kjenpCwAAAACQMW46I3gTb0vykbTSGCNJG6y1fa21PxljFkj6rxKXjPaz1sansy8AAAAAQAZIVxC01pa5wb6XJb2cnvYBAAAAABkvI58aCgAAAADIAgiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAQDqsXbtWERERSkhI+Mv67NGjh0qUKPGX9Yd7D0EQAAAASIe1a9dq9OjRf2kQBNKLIAgAAADcZay1unz5cmYPA/cwgiAAAAAca/fu3WrTpo0KFCig7Nmzq3jx4mrXrp3i4uJ08eJFPf/883rooYfk5+enQoUKqUWLFtq1a5fr+IiICI0ePVqS5O3tLWOMjDGSEmcKjTFau3atW5+zZs2SMUb79+93lZUoUUJdunTRjBkzVK5cOWXLlk1RUVGSpK+++kqVK1dW9uzZVbp0aU2dOjXVcxk1apQqV66sPHnyKF++fGrQoIE2bNjg2n/kyBFly5ZNEydOTHFsRESEcuTIoVOnTqXpe0TW45XZAwAAAAAyS/PmzeXv76/JkycrX758iomJ0bJly5SQkKBLly7p7NmzGjFihIKCgnTy5Em9++67Cg0N1a5du1SoUCH17t1bBw8e1PTp0/Xtt9/K09MzzWNZs2aNfvzxR40aNUoFChRQiRIl9PPPP6tp06YKCQnR/PnzdenSJUVEROjcuXMp+oqJidHzzz+vokWL6vz585ozZ47q1KmjzZs36+GHH1ahQoXUunVrTZ06VQMGDHAdFx8fr+nTp6t9+/YKCAhI8/iRtRAEAQAA4EgnTpzQnj17tHjxYrVs2dJV3qlTJ0lStmzZNG3aNFd5fHy8wsLCVLBgQX300Ueu0FW0aFFJUrVq1eTllfa/Xp86dUpbtmxRoUKFXGWdO3dWrly59OWXXypnzpySpBo1aqh06dIqXLiw2/HXjrVJkyYqX768pk+f7poFDA8PV/369fXNN9+odu3akqSoqCgdPHhQffv2TfPYkfWwNBQAAACOFBgYqFKlSmno0KF6//33tWfPnhR1FixYoGrVqsnf319eXl7KmTOnzp07p+jo6AwfT2hoqFsIlKT169eradOmrhAoScWKFVPNmjVTHL9q1SrVr19fgYGB8vLykre3t3bv3u021nr16unBBx90W146depUPfzwwwoNDc3wc8LdiyAIAAAARzLGaOXKlQoJCdGwYcNUtmxZlSpVSpMnT5YkLV26VB06dNADDzygefPm6YcfftCmTZuUP39+Xbx4McPHExQUlKLs8OHDKliwYIrya8u2bt2qpk2bys/PT9OnT9eGDRu0adMmPfLIIynG+uyzz+rTTz/VH3/8od9++03Lly9nNtCBWBoKAAAAxypVqpQ++OADWWu1fft2vf322woPD1eJEiU0f/58lSlTRrNmzXLVv3Llik6ePHlLbWfPnl2SUjz9848//ki1fvJDZq4WFBSko0ePpii/tuyzzz6Tl5eXFi5cKG9vb1f5qVOn5O/v71a3W7duGjZsmGbNmqVTp07J19dXnTt3vqVzwr2DGUEAAAA4RuS2GNUct1olh0ap5rjVitwWIykxhFWsWFETJkyQJO3cuVMXLlxIcc/fhx9+qPj4eLcyHx8fSVJsbKxb+X333edq62rLli275fFWr15dy5Yt0/nz511lv//+u7777ju3ehcuXJCnp6dbmFy9erUOHDiQos3cuXOrc+fOmjp1qmbMmKFOnTopd+7ctzwm3BuYEQQAAIAjRG6L0bCFOxR7JTHI7dv9X3We8ry6dn5SbeuHKD4+XrNmzZKXl5caNGig3LlzKzIyUs8//7yaN2+uLVu26K233koxw/bggw9Kkl5//XU9/vjj8vT0VEhIiIKCglS3bl2NHTtW+fLlU4ECBTRnzhz98ssvtzzmESNG6JNPPlHjxo01ePBgXb58WaNGjUqxNLRJkyZ688031aNHD/Xs2VO7d+/WmDFjVKRIkVTbDQ8Pd90nyLJQZ2JGEAAAAI4wfkW0KwRKkmfOAJlc+TR76ttq2bKlnnzySR06dEiff/65qlSpoqefflovvviiPv74Y7Vo0UJRUVFaunSp8uTJ49Zu8+bNFR4ernfffVfVq1dX1apVXfvmzJmj0NBQ9e/fXz169FDx4sU1YsSIWx7zAw88oGXLlunChQvq0KGDhg4dqn/84x9q2LChW72wsDC99dZb+u6779S8eXPNmDFDH3zwgcqUKZNquw8//LDKli2rkJAQVa5c+ZbHg3uHsdZm9hhcQkJC7ObNmzN7GAAAALgHlRwapdT+5msk7RvX7K8eTqbavXu3ypUrp/fff1+9evXK7OHgKsaYLdbakDvdDzOCAAAAcITC/r63VX4vOnjwoNauXaunn35aQUFBrncmwnkIggAAAHCEwWHB8vX2dCvz9fbU4LDgTBrRX2/atGlq0KCBjh49qnnz5snX1zkhGO5YGgoAAADHiNwWo/EronXodKwK+/tqcFiwWldK/YEqQGb4q5aG8tRQAAAAOEbrSkUIfoBYGgoAAAAAjkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAAAA4DEEQAAAAAByGIAgAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYTIkCBpj/s8YY40x+a4qG2aM2WuMiTbGhGVEPwAAAACA9PNKbwPGmGKSHpN04KqyByV1lFReUmFJq4wxZa218entDwAAAACQPhkxI/iGpCGS7FVlrSTNt9Zestbuk7RX0qMZ0BcAAAAAIJ3SFQSNMS0lxVhrt1+zq4ik36/aPphUBgAAAADIZDddGmqMWSWpUCq7XpQ0XFLj1A5LpcymUiZjzDOSnpGk4sWL32w4AAAAAIB0umkQtNY2Sq3cGFNBUklJ240xklRU0lZjzKNKnAEsdlX1opIOXaf99yS9J0khISGphkUAAAAAQMZJ89JQa+0Oa20Ba20Ja20JJYa/ytbaI5KWSOpojPExxpSUdL+kjRkyYgAAAABAuqT7qaGpsdb+ZIxZIOm/kuIk9eOJoQAAAABwd8iwIJg0K3j19suSXs6o9gEAAAAAGSNDXigPAAAAAMg6CIIAAAAA4DAEQQAAAABwGIIgAAAAANymS5cuZfYQ0oUgCAAAAMDRli9frurVq8vX11d58uRR69atFR0d7dpfr1491apVS0uXLlWlSpXk4+Ojd999V5L09ttvq3r16sqbN6/8/f0VGhqqqKgot/b3798vY4ymTp2qkSNHKigoSP7+/mrRooUOHjx47XA8jDGTjTF/GGPOGmMWGWNqGGOsMabH1RWNMXWNMV8l1TtvjFlhjHnoVs6ZIAgAAADAsZYvX65mzZrJz89PH3/8sSZPnqydO3eqVq1aiomJcdXbvXu3+vfvr7///e9asWKFGjZsKCkx5PXu3VuffPKJPv74Y4WEhKh58+b64osvUvQ1duxY7d27VzNmzNDEiRO1fv16de7c+dpq90l6StJrkv4mKVrS3GsrGWOaSfpK0jlJXSR1kpRL0jfGmGI3O+878h5BAAAAAMgKRowYoVKlSumLL76Ql1diPKpevbrKli2r119/XRMmTJAknThxQl9++aUqVqzodvxrr73m+pyQkKCGDRtq9+7dmjJlih5//HG3uvfdd5/mzZvn2j5+/LgGDx6sQ4cOqXDhwsmzkHklvWCt/XdStZXGmByS/n7N0CdK+tpa2yq5wBizRtKvkgZJ+seNzpsZQQAAAACOdP78eW3dulUdOnRwhUBJKlmypGrWrKmvv/7aVVaiRIkUIVCStmzZoubNm6tgwYLy8vKSt7e3Vq5c6ba0NFmzZs3ctitUqCBJOnDggCTphx9+SN71yTWHfnr1hjHmfkmlJc01xngl/0i6IGm9pDo3O3eCIAAAAABHOnXqlKy1CgoKSrGvUKFCOnnypGs7tTq///67GjZsqJMnT2rSpEn6/vvvtWnTJjVp0kQXL15MUT9v3rxu2z4+PpLkqnv48OHkXceuOfToNdsFkn5Pl3Tlmp/mkgJTnq07loYCAAAAcKSAgAAZY3TkyJEU+44cOaLAwP/lKWNMijrLly/XmTNntGDBAhUtWtRVfuHChTSN56qwWUDSvqt2Fbym6h9Jv4dJWpVKU5dv1hczggAAAAAcI3JbjGqOW62SQ6PUeNIPKv3Aw/rkk08UHx/vqvPbb7/p+++/V926dW/YVnLg8/b2dpXt3r1b3333XZrGVq1ateSP7a7Zde12tKT9kspbazen8vOfm/XFjCAAAAAAR4jcFqNhC3co9kpi6Is5Hav4R9rq0Mej1Lx5c4WHh+vcuXMaNWqU8uTJo0GDBt2wvUaNGsnLy0vdunXToEGDdPjwYY0aNUrFixdXQkLCbY8vODhYkk5KGmOM8ZC0RVIDSS2SqiRIkrXWGmP6SVpsjMkmaYGkE0qcOawh6YC1dsKN+mJGEAAAAIAjjF8R7QqByTyLV1Jwt5d1+vRptW/fXn379tUDDzygb7/9VoULF75he+XLl9fcuXP122+/qWXLlvr3v/+tcePGqU6dmz6r5UZ+kzRD0hBJiySVl9Qvad+Z5ErW2mVKfChMTknTJK2Q9G9JhZT4wJgbMtba9AwyQ4WEhNjNmzdn9jAAAAAA3INKDo1SaunHSNo3rlkqe/56xpgt1tqQa8oGS3pVUglr7YGM6IeloQAAAAAcobC/r2JOx6ZafhfJY4wZKulHJS4FrS3p/yQtyKgQKLE0FAAAAIBDDA4Llq+3p1uZr7enBocFZ9KIUhUvqbWk+ZKiJHWV9JakHhnZCTOCAAAAAByhdaUikhLvFTx0OlaF/X01OCzYVX6XOGetDb3TnRAEAQAAADhG60pF7rbglylYGgoAAAAADkMQBAAAAACHIQgCAAAAgMMQBAEAAADAYQiCAAAAAOAwBEEAAAAAcBiCIAAAALR//34ZYzRr1qzMHgqAvwBBEAAAAAAchiAIAAAAAA5DEAQAAPgLREREyBijXbt2KSwsTDlz5lTx4sU1c+ZMSdKHH36ocuXKyc/PT/Xr19cvv/ziOvbKlSsaMWKESpQooWzZsqlEiRIaMWKErly54qqTvLRz6tSpGjlypIKCguTv768WLVro4MGDbmO5cOGCwsPDFRgYKD8/P7Vs2TJFHUnatGmT2rZtq6JFi8rX11fBwcEaPny4YmNjXXWee+45FSxY0G0sknTu3DnlypVLw4YNy5DvD0DGIggCAAD8hdq1a6dmzZopMjJSVapU0VNPPaXhw4dr8uTJGjdunGbOnKno6Gh16tTJdUz37t01btw4devWTZ9//rl69uypV199Vd27d0/R/tixY7V3717NmDFDEydO1Pr169W5c2e3On369NG0adM0cOBALVy4UMHBwW79JTtw4IAqVqyoKVOmaPny5RowYIBmzJihnj17uuqEh4fr2LFjWrRokduxc+fO1fnz5/X000+n9ysDcCdYa++anypVqlgAAIB70ahRo6wkO3v2bFfZyZMnraenp82bN689c+aMq3zixIlWkt2/f7/dsWOHlWRHjRrl1t6YMWOsJLt9+3ZrrbX79u2zkmydOnXc6o0fP95KsjExMdZaa3ft2mU9PDzs2LFj3er17dvXSrIzZ85MdfwJCQn2ypUr9sMPP7TGGHvixAnXvrp169oGDRq41a9UqZINCwu7tS8HgIukzfYvyF7MCAIAAPyFHn/8cdfngIAAFShQQKGhocqdO7ervFy5cpKk33//XevWrZMkdenSxa2d5O2vv/7arbxZs2Zu2xUqVJCUOLsnST/88IMSEhLUvn17t3odO3ZMMdY///xTL7zwgkqXLi0fHx95e3ura9eustZqz549rnrh4eFas2aNq2zTpk3atm2b+vTpc7OvA0AmIQgCAAD8hQICAty2s2XLlmqZJF28eFEnT56UJAUFBbnVKVSokCS59ifLmzev27aPj4+rLUk6fPiwJKlgwYJu9a7dlqSePXtqypQp6t+/v1auXKlNmzbpnXfecWtPktq0aaNChQpp6tSpkqQpU6aocOHCatGiRcovAMBdgSAIAABwF0sOdkeOHHErT94ODAy8rfaSA+XRo0fdyq/dvnjxohYvXqzBgwdrwIABqlu3rkJCQuTr65uiTW9vb/Xu3VuzZs3SsWPHNH/+fPXq1UteXl63NTYAfx2CIAAAwB0SuS1GNcetVsmhUZr+7b40tVG3bl1J0vz5893K586dK0mqU6fObbVXrVo1eXh4aMGCBW7l17Z/6dIlxcfHy9vb2638ei+c79Onj86cOaN27drp0qVLPCQGuMvxzzQAAAB3QOS2GA1buEOxV+IlSWcvJr5eYfG2GD1R9b5bbqd8+fJ68sknFRERobi4ONWoUUPr16/XmDFj9OSTT+rhhx++rXElPyF05MiRSkhIUNWqVbVy5UotW7bMrV6ePHkUGhqq119/XUFBQcqXL59mzJihmJiYVNstUqSIWrRooUWLFqlFixYqVqzYbY0LwF+LGUEAAIA7YPyKaFcIvNrrX+6+7bZmz56tF154QTNmzFDTpk01ffp0vfDCC5o9e3aaxjZ16lT16tVLr732mtq0aaNdu3Zp3rx5Kep99NFHqlKlivr166cePXqoUKFCmjhx4nXbbdeunSTxkBggCzCJTyi9O4SEhNjNmzdn9jAAAADSreTQKKX2tywjad+4Zqnsyfo6d+6s7777Tr/++qs8PJhvANLCGLPFWhtyp/thaSgAAMAdUNjfVzGnY1Mtv9ds2LBBP/74oz7++GNNmDCBEAhkAQRBAACAO2BwWLDbPYKS5OvtqcFhwZk4qjujevXq8vPzU/fu3RUeHp7ZwwFwCwiCAAAAd0DrSkUkJd4reOh0rAr7+2pwWLCr/F5yN91qBODWEAQBAADukNaVityTwQ9A1scCbgAAAABwGIIgAAAAADgMQRAAAAAAHIYgCAAAAAAOQxAEAAAAAIchCAIAAACAwxAEAQAAAMBhCIIAAAAA4DAEQQAAAABwGIIgAAAAADgMQRAAAAAAHIYgCAAAAAAOQxAEAAAAAIchCAIAAACAwxAEAQAAAMBhCIIAAAAA4DAEQQAAAABwGIIgAAAAADgMQRAAAAAAHIYgCAAAAAAOY6y1mT0GF2PMcUm/ZfY40iCfpBOZPQikG9cx6+MaZn1cw6yPa5j1cQ2zPq5h1naftTb/ne7krgqCWZUxZrO1NiSzx4H04TpmfVzDrI9rmPVxDbM+rmHWxzXErWBpKAAAAAA4DEEQAAAAAByGIJgx3svsASBDcB2zPq5h1sc1zPq4hlkf1zDr4xriprhHEAAAAAAchhlBAAAAAHAYgmAGMMb8nzHGGmPyXVU2zBiz1xgTbYwJy8zx4fqMMWOMMf8xxvxojPnSGFP4qn1cwyzAGDPeGLMr6TouMsb4X7WPa5hFGGPaGWN+MsYkGGNCrtnHdcwijDFNkq7TXmPM0MweD27OGDPDGHPMGLPzqrK8xpiVxpg9Sb8DMnOMuDFjTDFjzBpjzM9J/z86IKmc64gbIgimkzGmmKTHJB24quxBSR0llZfURNK7xhjPzBkhbmK8tfZha21FSZ9LGilxDbOYlZIestY+LGm3pGES1zAL2inpb5LWXV3Idcw6kq7LO5Iel/SgpCeTrh/ubrOU+GfrakMlfWWtvV/SV0nbuHvFSRpkrX1AUqikfkl/9riOuCGCYPq9IWmIpKtvtmwlab619pK1dp+kvZIezYzB4castX9etZlT/7uOXMMswlr7pbU2Lmlzg6SiSZ+5hlmItfZna210Kru4jlnHo5L2Wmt/tdZeljRfidcPdzFr7TpJJ68pbiVpdtLn2ZJa/5Vjwu2x1h621m5N+nxW0s+SiojriJsgCKaDMaalpBhr7fZrdhWR9PtV2weTynAXMsa8bIz5XVJnJc0IimuYVT0l6Yukz1zDewPXMevgWt07ClprD0uJIUNSgUweD26RMaaEpEqSfhDXETfhldkDuNsZY1ZJKpTKrhclDZfUOLXDUinj8ayZ5EbX0Fq72Fr7oqQXjTHDJD0naZS4hneVm13DpDovKnF5zNzkw1KpzzXMRLdyHVM7LJUyruPdiWsFZCJjjJ+kzyT9w1r7pzGp/ZEE/ocgeBPW2kaplRtjKkgqKWl70h+0opK2GmMeVeK/gha7qnpRSYfu8FBxHde7hqmYJylKiUGQa3gXudk1NMZ0l9RcUkP7v3ficA3vMrfxZ/FqXMesg2t17zhqjAmy1h42xgRJOpbZA8KNGWO8lRgC51prFyYVcx1xQywNTSNr7Q5rbQFrbQlrbQkl/gewsrX2iKQlkjoaY3yMMSUl3S9pYyYOF9dhjLn/qs2WknYlfeYaZhHGmCaSXpDU0lp74apdXMN7A9cx69gk6X5jTEljTDYlPuRnSSaPCWmzRFL3pM/dJV1vxh53AZM4IzFd0s/W2glX7eI64oaYEbwDrLU/GWMWSPqvEpeq9bPWxmfysJC6ccaYYEkJkn6T1FfiGmYxb0vykbQyaXZ+g7W2L9cwazHGtJE0SVJ+SVHGmB+ttWFcx6zDWhtnjHlO0gpJnpJmWGt/yuRh4SaMMR9JqicpnzHmoBJXxYyTtMAY00uJT0Vvl3kjxC2oKamrpB3GmB+TyoaL64ibMP9bRQUAAAAAcAKWhgIAAACAwxAEAQAAAMBhCIIAAAAA4DAEQQAAAABwGIIgAAAAADgMQRAAAAAAHIYgCAAAAAAOQxAEAAAAAIf5f1yLgnenot0BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "ax.scatter(x, y)\n",
    "for i, txt in enumerate(s):\n",
    "    ax.annotate(txt, (x[i], y[i]), fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8745, 300)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_embedding_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}