rnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.linspace(0,50,501)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b13b2bf7f0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ulYLgRThr5RprJfcMg0mY2qjnfhF5SUTOicjHOrz/SRF5yv95WURW2t6rt733iAl5wqD5oHcYHNpjcV22p5TiWqG8o1cOMDXcx3wMicGF9UpHTlrjSEzenP7O7SWhGjrKcu2ZtwzWzpNJ+3muoJRibhfDkPLzW3GUrO6WkwFdLehe9/V4m95hXOpxsbBecSaTDVxfOxcSIpIGPgW8H28P5ydF5BGl1PP6HKXUv2g7/58Cb237iKJS6t6ocnQLXdXTKcHbftx1xLBerlGuNXadgKeH+liIoSppfq2848AAb3Asb1ao1Rs7RhU2cGW1RD6b6lgSCu1JyzK3tXVftY3mAsodJrrp4T5S4j5iWN5lcZtGXFV582vl6/pdtePwSJ5rayUaDdWxmtCmXMN9mS0dctuhx8XCevm6NiP7CSZG7X3AOaXUeaVUBfg88MAu538E+H0D32sEixueZZ8cynV8P5dJMTWUc+6daI9jN8MwNZyLZceohfXyHgYrh1KwtOHWa5oreAneTiWh0LqXixtu75mOBHbiy7PpFFNDfc49c607u0WlcVE2C+tlJneLSkf6qNYVS5tudWxhvczUHk4REIvDZhImDMMx4GLb60v+sesgIjcDp4A/azucF5GzIvKEiHzIgDyhoB/gToYB4lnkpgftXkroeh2DUoqF9b0jBsA5zXVthzUMGlP+M3Yd5i+ul0kJTAzurGOTQ33ODVav6n694U3407vIdSQm+s1zinaWa2o41zxvP8OEYejknu1UkP0g8EWlVHuTkxNKqTPAjwK/JiK3dvwSkY/6BuTs/Px8NInbsLBeZiSfoS/TOTSEmAyDP2h3VcKhPgqlmtP9IlaLVap1tWvEMBkTz7qwXtnVYI32Z8mkxPmCrYWNChODuY4loRpTQzn392tj76h0cijHRqVOqepOx5Y2Kii1u1N0yHcAXC9y2yu/Njno0YJxbqJlAiYMwyXgeNvrm4DZHc59kG00klJq1v99HvhztuYf2s97WCl1Ril1Znp6OqrMTSxs7P6gwavOcE2LaI9jejfKxh84iw4nFK3wu0cMnjFzPgHvQXGJCJND7um3xfUykx3arbRjcjDnPGLQz2dXulI/S4f6r5/PbvdMt69xqfuwt46lU8LEYI75fZ58NmEYngROi8gpEcnhTf7XVReJyB3AOPCNtmPjItLn/z0FvAt4fvu1NrGwtvuDBpgYyrG4XnG6MnV+vYLsQT+0KiDcTSjzAQzW1LB7ucq1Omul2q73C7zJxvVksrhe2ZWuAZ9KimGSSwmM9Wd3PGeiOQG7e5aLzfzabtSbe4OlF7ruNV9MDfUlVJJSqgb8LPBl4AXgC0qp50Tkl0Tkg22nfgT4vNo6u94FnBWR7wCPA59or2ZyAS+ZtPugnRrso1JvsO6w/n1hvcz4wM5rBaA1cFyGra2IYed7NtyXIZdJOaVGlje8uvG9JuCpYfeDdnGjsmsiFTy5Nyt1NivudGxxvcLEYN+uVT3NCdjhswySXxvIpenLpJxG8jqi2y1ahhvDMEQuVwVQSj0KPLrt2C9se/1vO1z3deAeEzJ0i8UAVJL2QhfXKwznd/auTGJhrbyrVw7xRAx6sp8e2jnJKyLOS2mD0A8AU4M55/sFL6yXmdwjkmlWTK1XGJgwMiz3hMeX7+0UQTxUUqduxxoiXn8npwZrbe9IBjzD8frr8e4vHhUHeuVzte6FhntNJhMx8ax7RTKtmmm3OYZceue1AhpTQzmnVUlLe5Qda+iIwRUtqCmuPSfgGHRscWNvGnUyhnzRwnolkI5NOM7LBIlkQBcSxNN40xQOtGFocpkBqCRwW5e/V/UDeK2kh/syTqkkXa6301oBDS+cdh/m7+WZTw7mKNcabDja/atlsPZKPrvn8vcqvYQWZePaKZoMoGMTjotCguTXwNP9UtWdjtnAgTYMCwGqMqAtYnA+aHeXC9y3xZhf232Bj4ZrnlUb+T2pJMcLkLRceybFY+DyvaT47vdLUzZu6crynpEfePcsltzHHvesGcnv45LVxDCwN2eovVBXXtNGucZmpR7MMAzlnCrgkl+Tvxemhj1vruFo96/FjQqZlOxJP7SqWdzcs+A65k8mjuTarHg6FnQCdprkDRAtg/sS34W1CoO5NP25ndc8QXwLPE3igBuGvRf4gEfZDObSzryTJv0QYAIeH8g53StipVhhfCCAYRjqo95QrBTdyLYYkH5oDto1N88yaCTTn3OrY4sBdR98Lt+xZx5MLo+ycVXJtbSxe5sODe04LTte+2QSB9owaGoo0MMeyrHkyDtZ9vu/jAcwDBODOaf9YlY2qowN7F2ZpQeHK0/Ti2SCUVzgLmJo5j4CeebuKJugkQzotR9u5FJKeRRXAN13Tb8tbVYZD6D7etwuO+7jZBIH2jCsFKtk08LgHqEh+IPD4SQHMDG4txKODeRY2XSz+K5ab7BWrgWKGMYG3A6OIKWX0GawHHrmuUyKob69S1BdcuZBIxnw6coNNzq2WalTqTcCOUWuKd6VzUogubTxWI5h10dTONiGYbPC2MDe9AP4fKajQaupobEAE/DEYJZqXTlZfKflCuI1TQy4DacXN/ZeKwBet9zBXNrZoF3wvd+gOuYqYtBRZpB80eRQjoqjSq5mtBxAxyaH3FZyLW8Go1H7s2lymVRCJe1XrGxWd20H0A6XpXGtwRHcM3eRZ1jx5QpisDTd5Cr/sbQejEqCVpTlAsubwZL14Mm16igno///0UC0oLsJWK9gD6L7riOG5YA0qogwMZBLqKT9iqAeALTaIrsIp5c3vD5JowGMlvbMXRit5c3gg9Ylz1qu1dmo1ANRbwDjg1lngzaMjo0PuJNrZbNKJiUMB6C4XFIjYfNr4CbHUKl5LXEmAj7LsYFsQiXtV6xsBvMAoEXZuAmnq4z2Z3dt06zhcgJebkYMe9+zwVyabFqcDA7tZQcxpOAZNleDdnWzGsgrBy9iKFUbTlpcL/u6H4TiakV/7nQsCJU0kPMoGxdyrRR93Q8Y/U0M5hIqab8ijGEY69eUjZvBEdQzaXlzDgZHCG9ORLwJ2MHgKBS9/MpIQMPgmkoKMslBKxJz9SyDUILQog5d0Fxh8msiwlh/1olcmuIKPi4TKmnfIkyYP+qQM1/erISIZHSS14VcwZPP3nluBkf4iMFNmN9oKFaL1cA65jIvE8Zg6TycK7nav3MvjA1kncoV+J4lVNL+RKlap1xrBA/zfUV15Z0EnUxG8llS4o5KymVS9O+wEfp2uBq0Bf+ZhIkYCqUqdcursgulKg0VzPv15HIZ/VUDyzXq0DCsbFYZzmd2bTffjrH+XJPmsQkd+Qa9ZxODXlTqauW/aRxYwxCm8gfcVv8sB6yXBkilhDFHnvnKhrfAJwgvDe4ihkIpfMSglH0j36RFQuQ+2q+ziTARQyadYrgv42QCDtpyRWPUWcTgU0khKswaqqWb+w0H1jCEHbTNMN+FdxJi0IIftjqhkoJTb6Crf3oz+Qz2PfNWhU1vyaWUYnkzeFQK3gS86oxGDS6XsxxDiMILaC1O3a90khHDICL3i8hLInJORD7W4f2fEJF5EXnK//nptvceEpFX/J+HTMgTBK0H3VvhdLFSp1QNtvJTw1XNdJhkPeg+TvZXzOoJayTgJkquqmzCJFK98xzpWLVOpdYINwEPZJ30vVoJ2HZCw1mOYaNCfzZNPjCN6rYljGlENgwikgY+BXwAuBv4iIjc3eHUP1BK3ev/fNq/dgL4ReC7gfuAXxSR8agyBUFr0AZTwnw2TT6bsu6dhKW4wFNCN+sYQkYMAzlqDcWa5VXZhVK1udo0qFxgP2Ef9llqHbNtsMIWEYDH5bvKY4XV/WK1br3Ed3mzGorimmjSggfUMOBN6OeUUueVUhXg88ADAa/9IeAxpdSSUmoZeAy434BMe6LV3iFM2Gq/zDFs9QN4YasrnjVMxND0gC1PwKvF6p7tttsx4WjtR5gWIhou1lishKRF9LkuqKSwUamO5AsOHLaw0TIc4IgBOAZcbHt9yT+2HX9PRJ4WkS+KyPGQ1xpHWM4QPCW0PQF3Y7DGB+x3WFVKhap9B3eceaFYC5xfAHfVPyub3gr2MPuEu1hjEZbi8s61TyWFXV0M7bk/+4YhlBM56LYljGmYMAydSlS2k8p/DJxUSr0F+ArwuRDXeieKfFREzorI2fn5+a6F1VgtVslnU4E5Q/ATcI6opDCDdqQ/S6Vmd8XserlGraHCeb+Dbibg1WI1lGEY6suQSdlflR1mBbvGuAPOvCu6st9++WXY1cXQvvDUclQaYgU7wHBfhnRKnBSr2IAJw3AJON72+iZgtv0EpdSiUkp34PpN4O1Br237jIeVUmeUUmemp6cjC728Ec4DADcVEHoVb5iJbtTBGovuvEw3K2ZXi9XAiWfwV8w68MzDepmgF0b1YI5hIEtDwbrFTXHCVgqCu0KCQimc8yEijOQzzpoimoYJw/AkcFpETolIDngQeKT9BBGZaXv5QeAF/+8vAz8oIuN+0vkH/WPWsRLSywQ3FRC67jkMZ+7CMHTjZerJ2roxDTlowY1nvloMx5eDppIs05UhF2tBm45ZlE3rSTc5BptUklIqdFQKnmyrRTe7y5lG8NlnByilaiLys3gTehr4rFLqORH5JeCsUuoR4H8VkQ8CNWAJ+An/2iUR+WU84wLwS0qppagyBcFKV96c/VWWq/7mQUFXF4MrwxDey3QxmYBOPnczaO1TNtMBdgdsx7jP5SulAi8kDIvVYrXZgC4o2hd4Hp+wIlZrBXuI6E/TOzaTz6Vqg2pdhZIL3OiYLUQ2DABKqUeBR7cd+4W2vz8OfHyHaz8LfNaEHGGwvFnl9KGhUNeM9meb3S/D5CbCoODTImEmBRcTcJi9GDR0+wybg6PeUKyVaqENw0h/lquFkiWpPKxsVrn90HCoa8b6c9Qb3sZLYZLWYdBNhOVigWcrWu6Cy7eo+91E8d75+9cwHOiVz2EmOWgNDpsPu9DFJOckYtgIX0YL9r2m9VL4nIw+30nuI7TByjSvtYWwORlo8f42E/basQnL5Y/2Z60arLAr6zVG+7PWy2ht4UAahlbpZdjBYb8ColtaRF9rC8tdDFp9vu1JDmAkH86bsy1Xw/f6e/FZFoq10N5vs5DAYpK34Bv54ZDPcsxyGXk3FBfsbyrpQBqGjUo9dOkluKmA8Kik8CEruPAyg3e91LA9OMI20NMY6c+yXq5ZK79cK9dQKrzB0s+yYDFpWSiFjxhctIQp+LmPbFgds1xG3g3FBS3dd7Hro2kcSMMQtoWuhosKiEIpfMSQ9rdotG0YwtRxa9jmWaOE+UrBWsnOBBy2FbiGk4ihixxDLpNiMJe2qvvdVP6A/Yghio7VG252fTSNA2kYmmVxXSbgbCZ5C13wv+BNQDb5zG7lss2zrnY5AWtP3tYE3O1kou+x1Xu2Gd75APultN1EMmC/WrC5Q2AXdCW42cPFNA6kYehmdXH7+baUUCkVur2DhgvKpmvDYMkrh9YE2o03B/YGbZN+CEvZ6PJLS338G35Tw7CTHGgdszsBh819gP1WNZGjv33YFuNAGoZuVheDt8F9xmJpXLnWoFJvdD04ei1hCZ5c6+UatXrDglTRwvz2602jtQ91uHs2lMuQEntyrVf83EdXEYPlCbjriCHLWsmujnWV+0gihv2FbhOWXisFe83EWhU2vRkxdBfJZPzr7UQNq8Uq6ZQwkAu3rsS2Z95txJBKCcN5e8+yW+8X7DfSi5JjAHs61q3BclEUYgsH0jC0eOkuPXNLXlO3tIi+xm7E0CWVZHnthzZYYVcJ227XEWUCtvksozgftvtLFboo1Yb2Vdl2ZIsSLXvXJ4ZhX6BQrJJJhWs7oWEz0dVtWRzYLdmr1htsVOpdyWV7Al4tds+Xe9fbMwwi3srcsLCZsO+W4oIWl2+j/DJS7sNy6+1uFgSCfafIJg6mYfBLQrvpRTPWb29/5W4Xa4E3aMuWWm/rks5enIC7pR8G/HyRvUimxnBfhlSIltsaNiOGbiku8OSqNRSlqnkuP1Luw3KSt1sa1Xa+yCYOpmHo0ssEXWVj15vrRglHLIatUWkRsOuZdyOXbqXQa3KB583bpri60jHBQFv9AAAgAElEQVRdSmtB/5v7dveijnWxtgi8fNF+7Zd0MA1Dlw8a7C7YikQlWRwcUb1M6D3DAPY9827uF9gt8e123Yd3jb21H1F0zHaSN6ojmRiGfYJu6QfwlHCtVKNuoZVCtz1ZwLJhaPLSvRXJQPdhPsCwZS4/io7ZpLi6zX3YXHwXNffhfYZ5uRoNFUnHEsOwj9BthQ20lHDdgke3WqzSnw3XJ1/DRcTQzeDIZ9P0ZVJW5Op2AxUNm0lerxlid17mSN7eVq2FYpWhLnMfTSNvg0qKQHFl0ykGcnbau0fJfUBiGPYVvNbW3YeGYM8z70W5opT3gr0S32K13tUGKhq9TCWBPSPfrVyaTrHR4C8KlaSvszMmI8pluSWMLRgxDCJyv4i8JCLnRORjHd7/ORF5XkSeFpGvisjNbe/VReQp/+eR7dfaQJSIwWaPnZ6dTCIODlsJ+yjJeu86u0neKF6m/gzTKESkUcFOxBClwAHs6ViUnAzs34gh8g5uIpIGPgW8H7gEPCkijyilnm877dvAGaXUpoj8Y+D/Av6B/15RKXVvVDmColStU641og9aS0rYdVLcssHqZnWxhq3BETWSGcl7SV7T22jW9LqPnjTy3Uelw82IwYaOdZ/7AHs6FiX3AVtbb9vaqtUGTEQM9wHnlFLnlVIV4PPAA+0nKKUeV0pt+i+fAG4y8L1dIUrlD9hdtBIlyZVJpxiy1HpbV2V0q9i2DUOUHIONtshrzV3lujRYPUol9WXS5LMpKxVThWK163UfoEt8e4/i0ms/NvdZ620ThuEYcLHt9SX/2E74KeB/tL3Oi8hZEXlCRD6000Ui8lH/vLPz8/NdC9ttC10Nmyt5o5TFgUWvKUJ5L9j05qIbBjD/LE3QD2CPsonyLEfydjjzyHJZ4vJNOB/tn7NfYMIwdDLxHWs5ReTHgDPAf2g7fEIpdQb4UeDXROTWTtcqpR5WSp1RSp2Znp7uWtjIEYNF/jcKlQT2BkeUnAzYK7+M0vcH7LVFNuFlgp2VvFGquMDXMRsGK0IkA/YqzEzkPuBgGoZLwPG21zcBs9tPEpH3Af8G+KBSqqyPK6Vm/d/ngT8H3mpAph0RNZFqq5VCo6FYi0Algb1kaqHUfU0+2Fv7EaWMFuxRNlHWfUCLyzdNjUTNfYAXaduoSopssPJZ1so2dCx67gMOpmF4EjgtIqdEJAc8CGypLhKRtwL/L55RuNZ2fFxE+vy/p4B3Ae1Ja+MoROR/Rewsc9+o1Gio7g0WaK/JzqDtNvkGrcGxZtjT1M8g7ObxGrYGbSsq7U6ubNrbRtO0XM2eVxGepbWIIUJSHOzpWNTcx4E1DEqpGvCzwJeBF4AvKKWeE5FfEpEP+qf9B2AI+K/bylLvAs6KyHeAx4FPbKtmMo6o9APYaVkQtcIGdJVN71FJNrn8ob4MmZAbqGjY4vKj5j70tcblikhx6WvtVCX1po5FzX3s19bbkctVAZRSjwKPbjv2C21/v2+H674O3GNChqCIyhnqa23RD5H53x5NPoP5hVFR2k6AvXYdJpyP3tWxjLWqpKhjEuxEf1ENqfc59ra3tYEDt/K5UKqSy6TId7EXg8ZI3jyXb8KbG+3PslGpG93isFyrU6o2IlVLNVfMWqCSokwmw30ZxEJb5KjrPsCOYYhaLQWtiMHkngx6v4+oERaYdz6i5j6GLK79sImDZxiKtUiTL3hKuNaTg9b8NpotXjrCoB2w45l7FFf3BiuVEob7MhbkirbuA+xU2UTNfXjXenX5RYN9nKLs96Fhj0qKlvtIax2z1KrfFg6gYYiWSAU7dfkmeGkb1IgRuSyt/YiyIFBjxEK+KCr1Br7zYVouExRX3rxnbobetbPyPyqVBJriTaiknoaxB10yG04Xml6TCT7ToGEwIZelJG9UKgnsGfnIBstCU7io5b3QmoBNPsuoi8jar7WhYyacjwNXlbTfYGLQjvZnqdbNhtNacYaicPkWeFYT1VKDuTTplFhIPpuZgG0kn6M7HxnWyzWj+aLVooHch4U9GaIuOgXoz6bJps2uL6rWG2x2udd5O0byCZXU8/BabkefTMBs2KrrpdNd1kuDHZ7VBP0gIsYHh4mEJegqG9OeeTReGlr3e71skrKJnvuwEf212tRE1TGznrmJ3Afsz9bbB88wRExYgp0KCBO8tI0w34Q3p6+3Y7CiP0sbkYwJutL7rB7TMQt7MphIioP5hH2T4hrovXyRbRwow6B3/DLBS4N5z9xUJGM2+Rzdm9PXm6Uf/Jr8iIPWFpdvbAI26pmb4cuh93IMYNP56D0ds40DZRiK1Tq1Rvc7fmnYqICI2lkVvD5O6ZQYjxiyaSGfjaYqphdGmZxMitU6lZoZLl+v+zCRxwKzRt5E7sPGngyFYpVMSuiPsLYIzFM25qJl8/ki2zhQhiHqphsaNgatidJLETFOjWgvM+omI7bC/OjenKcLpnrsRG3rrmGFyzeQ+7CxJ4OOsIzoWC86HxbyRbZxsAyDgXK99utNRgwmKC4wvyq7UIq+IBDMh9Mm1leA+Y2XTOZkwAJdaehZmqYroz5HMN9d2BiNamlVtk0cLMNgyMsctlSVZGTQGm6+tlqsMmzCYFmQS39uFJjuZWOOl7aT5DUxAdt4llEjLGg5H6bWF5lMird/3n7AgTIMpiYT08vca4ZKL8GGN2dq0GYoVRuUa2bWfpiK/kyvFi8YaG0NMJjLkBJzk0mz55WhqLTXqqWgtVWrqW00V4tefi1y7sPifuy2cKAMQ6tRnYGJzmAFhKnJRH+GDf43Klr98s3ItlqM3gwRzHtzpnjpVEoYNmjkTeU+wHzEYCpaNk3xarmi5j5sdfG1iYNlGCLurNUOkxUQpugHMN/iwUTTQTDPmZvky8GsXO2fGwUmjbyp3AeYj0pXi9EXnYIFHTOwGBbstYSxCSOGQUTuF5GXROSciHysw/t9IvIH/vvfFJGTbe993D/+koj8kAl5doLZCdhcOG2KFgEb6wWiNx0E82ssvISlmQhLf54J9OoEbKJRnYadqDT6szRdLWiqIMRWS3CbiGwYRCQNfAr4AHA38BERuXvbaT8FLCulbgM+Cfyqf+3deFuBvgm4H/h1//OsoFCq0p9Nk8tEt4cmPXPTkUy51qBkoI9Tya/vN5OwNNsS3ERzM2j12DHlzRWKNXLpFH2GdMw0xWWyKslEklfrWO9SSdEN1mAuTcrCvh82YSJiuA84p5Q6r5SqAJ8HHth2zgPA5/y/vwi8Vzzi7gHg80qpslLqNeCc/3lWEHXv4naYLL800ahOo1WXH30CNk1xgbnBYcqbM91jx1RNPugJ2FQkE22v83aMGtyTwXS0DGZLj43pmOG8jG2YMAzHgIttry/5xzqe4+8RvQpMBrzWGEzx5WDWmzOxe5uGST7TNC0CJqt/zEQMYDZfZNT5MFiXbzb3YY4aMUlxtQoJzJUeG9MxS3tl24IJw9DJNdoeY+50TpBrvQ8Q+aiInBWRs/Pz8yFF9DA5lOP2I8NdXbsdo/1ZNit1qgaWuZtarAVmE3CrhitZwGz1jykj73V+NTeZmJPLgvNh0sgbkM2kjg3nzW3VqpQy6kja2ivbFky4NpeA422vbwJmdzjnkohkgFFgKeC1ACilHgYeBjhz5kxX5Oav/PA93VzWEe0T8NRQX6TPMtEnvymXQc/c5GTSl0mRS6eMeJneoO3NiKFQMrOKFzy5tPORTUfz4UyV93pymeuXZFLHUilhyNBWreVag0q9YSz6s7EhlE2YiBieBE6LyCkRyeElkx/Zds4jwEP+3x8G/kx5matHgAf9qqVTwGngrw3IZB0mKyC8XeWi9cnXGDWY5DVJP2ie1cTgWC/XaCgzERaYNQxrhhKWYDpfZND7NRgxmIyW9ecYGZOG5dpvVFJkDVZK1UTkZ4EvA2ngs0qp50Tkl4CzSqlHgM8A/0VEzuFFCg/61z4nIl8AngdqwD9RSpnbFs0iTHZYNdUrBkxHDDphaTKcNkE/mEvWg3nKxqTBAu9ZTgzmIn2WqZJQsJRjMJj7M0OjmpXLpI65gBFNUUo9Cjy67dgvtP1dAn5kh2t/BfgVE3K4hMlEl6kKGzCbY9CDdtiYB2zKmzNrsHRHWqVUpKjN1H4f7XKBOc/cZE4GTBU4mFv1D+aqBU1SXN7nmG0jYhsHauWzSZgsv/SoJDMKmPfXaZiaTPoM8dKgWymYMaT680xgpD9Dpd6gVI1WSFCqNqjWo+/30ZLLrGduypAOm4xKi1Xy2RR9GTM6Zqpa0IbzYXLfD9tIDEOXMFkzbXLQgrn6d1N13Bqm+F8bYT5E94BNdePUMKpjhto7AOQyKfqzaXPRsqHnCDaoJMP02z6hkxLD0CVMNsbyesWYUUAwx+Wb2FWuHV5XTnNhvklvDqI/S9N8uck9vE2t4tXwqBFD0bJJp8jQ2g/jVJKFbXdtIjEMXSKfTdOXSRmsSuo9z9z8oPXC/KitFEwuimr/HFMRQ68VEiilzD9LQ8lU087HaH/WSHt3e0Z+f+QZEsMQASbKL5u9YoxTSb2VsARv0FbrKjKXXyhWEYHhPrNloVGfpench6k9vHXuwyhd2W+GrjTV80rDVF5mtWiurxokEcOBgolEl2nvV3+WkXUMBhdrgTnOXPPSqVT0dR9gbjIxuecB6D5O0atZTOdkvM8yRFdayGNBdB0rGKZ3bWwHbBOJYYgAE4kuk5sHaRjj8g32/QFznLnJfkRgrizUNC+tP6vXkuLeZ/VmVGqSFuxFuVwhMQwRMJKPnuhaNdhyW8MEl9+syTfqZRpK8hqOZPQ6jdXN3ko+g5l8kQ25vBxDtEjGy30YLrwwGJXaiJb3y1qGxDBEwKgBntX00nv9WdV6tLbIxWqdWkNZCfNNRAwm71dfJk0+G33tR6FUM8pLg5kJ2HRSHFpVSVGcj41KnXrDbO7DWIWZYYorn00Z3ffDNhLDEAFmqaTe8k5afLn5cNpUjsEkTBj51U2zFBeYKb80nRQHTy9qDcVmpXvnw1aE1f7Z3cJ0tZSIGKsWdIHEMESApmwaje69plbyube4fCu8tG6lYCDKMullgpnyS9O8NJipMDOdFAcznLmdnIy5CjMbOpYknw8ARvuzKAXrle4nOjsVI9G9JityGfLmrAxaQ9GfyUkODCWfLUQMJqpsTLedgBYtGEWuRkOxZuFZDhuqFnSBxDBEQJMaiZC0LJRqRvsRgRku30buI5tOMZBLR5KrVK1TNrzuA0yVHpulH8Dz8qMu2CqUqgzk0pH3dNgqV3Tdt+F8QHRacKPitXU3H/2ZqRZ0gcQwRICJCgiPlzbvZUJEuSwYBogeTtugH0AP2uhJXhuRDETbk8FWTgaireS1QaNCdB0z3dZdYz/t+5wYhggw4pnbmEwMcPk2Epbe50WbgFsJS/OD1khS3IIhhWj0m+nFWmCGy7cZMfSiU5Qknw8ITGxxaIMvN9EW2UbC0vu8aF7TqgVeGjy51iIUEujtRnvSM7eQFDdR/WMr+otKC1pzivKtfT96HZEMg4hMiMhjIvKK/3u8wzn3isg3ROQ5EXlaRP5B23u/LSKvichT/s+9UeRxDRMJOBuGodUWOZpcg7k0GYO8NET35mzkPvTnNZTHL3eDjUrd46V71DM3nkg1QaMWqwz3ZUgbam2iETX6s5EUh9a+H+V9sCdD1FH/MeCrSqnTwFf919uxCfy4UupNwP3Ar4nIWNv7/1opda//81REeZyi5TVF5X/NTiZgJpw2PTAgOs9qk+KC7j1zGzX57Z/Xa1FpOiUM90Xrl2TDYEHvOh/7qZFeVMPwAPA5/+/PAR/afoJS6mWl1Cv+37PANWA64vf2BAZzGVIStWTP1gQckcu3UK4H0ZO8NlbxQvQqG2vJegN5LJtGPqruW9Gx/izr5VrXtKDNHAPsj35JUQ3DYaXUHID/+9BuJ4vIfUAOeLXt8K/4FNMnRaQvojxOkUpJJA+40VCslc32/dGIzuXbG7RRuHw9cdvj8nvMMERcxV5vKNYM7t7Wjqittz2DZT5aHslnUKr7Sq7VYpWUeI6fUbn2UYfVPQ2DiHxFRJ7t8PNAmC8SkRngvwA/qZTSJNvHgTuBdwATwM/vcv1HReSsiJydn58P89VWESVsXSvVUMo8LQLRKRtbkYzm8rtdFFgome2TrxF18d3Kph2KK2qPnTVLERZEr8svFO04RVFzfzpaNtXWXSNqteA3Xl3kR3/zCS4ubZoUqyP2NIlKqfft9J6IXBWRGaXUnD/xX9vhvBHgvwP/u1LqibbPnvP/LIvIbwH/ahc5HgYeBjhz5kzPpPWj1EzbqsoAb3Ccu7be9fXWKK42nrUbr98aLRIxmWqLl/b2ZOi+zNFWJKM/88Ji95OUrWdpIvqzRb1B93JdXNrk668umhRpR0R1ux4BHvL/fgj40vYTRCQH/BHwO0qp/7rtvRn/t+DlJ56NKI9zRKlNtjloo7YEt7EoCtpLfLsP801X/kD0stDmsxywM9FFlqsHo1LbE3C3+m/b+Yg8X1jQse2Iahg+AbxfRF4B3u+/RkTOiMin/XP+PvB9wE90KEv9PRF5BngGmAL+XUR5nCMKlWR70HbL5VfrDTYqdbuDo8sJxRb9MJSPtiZF89JDhnlp8Hvs9KCORdH9Sq1BsVq34nxEpZJsO0VRGAZbOrYdkb5BKbUIvLfD8bPAT/t//y7wuztc/54o398L8Noi96A3l2/V5Q+HVHKdtLPhmZvw5o6O5U2KBEQvv9TJetO8NESL/mzr2GalTrXeCN2HyXaEBd0b+UKxytHRfpMiAe37fkSJlu3o2HYkK58jIko4baPrpUaUunzbXiZE88xteHMQrfzSFv0A0XTM7rPsPsqyHS23f0dYrBbtVHFBtDbqNnVsOxLDEBGj/VkqtQalLnZLczEBd1OXb9vLhAgLySytr4Bo5ZdWDUM+mlxgdwLu5lnaLLwYzKVJp6Qrw6Bbm/SqkU8Mwz5BlGqW1WKVdEoYzJlrua0Rhcu3GclE4fJ1Tb69CTgalWRvMokmVy6dIp81P9SjcPk2DVZzt7Qu7lmp2qBSb1ihUSFaS3Cb0fJ2JIYhIqJQI3oy8YqyzCJKXb7NQZtOCcNdcuZrFr1M/blReGmb9EO3UamWq9d0zFZ5r4aXl+ktGhWi54uSiGGfIIrXVCiZ39hFIwplY39wdOfNabnGLMkVtfTYJv0A3UV/tlYXg5mIwZYH3O2ztNVyRSNqTtKW87EdiWGIiCiJLpuTSZRIpsn/WkzydhNO69XFY5bquD2DFV4upZSjZ9mdkbdp4KFLg7VpfwLuNYoLuk8+29ax7UgMQ0REWWVpqx8RtLj8bgeHLV4avGqWbu7XStGyYejPsF6uUauHa4u8WalTayir9AN0GZVaWvcB0SMGG61NNLqlBW314tLQixXD7slQrNap1u3p2HYkhiEimoO2i+ofm9UPUerybfLS0L3XtLJZAWC0P2daJP9zvWcRtvmadS8zMpVkRy7dx6k7GtWu99tt8tn+s8xQbyg2KuHyRbbl2o7EMEREi0rqomTPcmjYLWVjYyvIdnTtzdmOGLqkRlzQDxCtwMEGmtU/PUZxQWtVdljP3HqOoctnaWvzoJ2QGIaIyKZTDObC75amOUObyaThLssvbQ/abrn8FQe8NITn8l14mRC+kKDRUNY9826jPxc6Vq0riiErufSzHLZVFNJl9JdEDPsQ3SS6bPPS0H0vG9v10qP92a64/JXNKkN9mdDtF8LIBeE5816NGNbK9tq6a3RbZbNqOSrtNmG/WvR0zPSWthrd7q+RGIZ9iG5K42yHrNA9ZWPdy/QnhLBc/kqx4kSuXvPm8lmvx05Yg2V7rYD+7O6S4naj5ShG3vb90t8TBq0tbe030IPEMBhBNxGDCw9gJJ/tahcrF2E+dDEBb1at5RcgCv9rryGcxlh/juWNSqhrnOhYhKjUhZEPb0zt9UmC9rbzvTdftCMxDAbQzWY9tsviQO/7HE6uRkP53pzd5DOED6dXipYNQwT+13Y75LGBLMshK99cTCbjA9lm7icoavUG65a2tNXodh1PweKCQIhe4BC2U3K3SAyDAYz2h/fMXQza0f4sa+Ua9RB7MqxXajSUfbkg/OBY2awwZqlUFbpvvuaiHfL4QK5ZrhsULfrBZiTj5RjC6JgeK71K2dh01oa73N6zUKwynM+QdtByGxLDYATd8KwFB4OjG2pERzI2J+Buw/zVYtUqXeNto5npKjFoO8QfH8yy3KVhsCnb2EAOpULqmOV2GO2f3Ws5hkw6xVBfhpViuGe5vFlhfMDemNyOSIZBRCZE5DERecX/Pb7DefW23dseaTt+SkS+6V//B/42oPsO3ayYbS3WshjmD3qfvRJicOjJxwWXH2bQKqVY2axa65Ok0U2VjQvDMDaQC03ZuDEM4XXMVe4DuohKixWrug++IxnyWa5sVhl3sKWnRtSI4WPAV5VSp4Gv+q87oaiUutf/+WDb8V8FPulfvwz8VER5YkE3K2ZXNj1e2la9NHiTCRDK09STz/igPRvdnExCDI4Nv7zXyaAN6WWubDqIGAayrIRcsLVarJJJCQMW2rpr6OcRRsdc7F2sV/6HeZalap1StdEcN7bQTfS3sllhdL9EDMADwOf8vz8HfCjoheL1W3gP8MVuru8ldMNnLm9WGBvIWeWltXcdhpvWCmvTO9E9csLIpc+1SXFBd4UELrpejg/kvP0oysGdD5tt3TX0JBrGA3ahYxC+Yqoll2XDMJALXUiwvM8ihsNKqTkA//ehHc7Li8hZEXlCRPTkPwmsKKW0pl8Cju30RSLyUf8zzs7Pz0cU2yy6oUZWLJdeQkvBlzfCyQX2+hGBx+WPD4Tzmmx3VtUYH+yOsnFBJQGshHqW9mmRpvMRgjNvPUvLRj5ku45mtGz7ng3kQjsfrnMMe/IYIvIV4EiHt/5NiO85oZSaFZFbgD8TkWeAQofzdoyTlVIPAw8DnDlzJlwDFMvQIXEYPtPFgx7vgkpykWOA8F6Tq8lkfCDLUoj1Aq7aIesJeHmzwonJgUDXLG9U3elYCIOl76/tfNFoyHJtrfs2KS7w/u8wY7JWb7BWqlkfk+3Y0zAopd6303siclVEZpRScyIyA1zb4TNm/d/nReTPgbcCfwiMiUjGjxpuAma7+B9iR3dUUpVjY3lbIgFe/iIl4bj8lc0qwxbbTmiMDWTDUUlFNwZrbCDXLL8MUhq4Vq5RaygmHPDSEN7IH58IZkS6hdeFN1zyeWWzwkjeXtsJjZF8lguLm4HPb0UM9p2P1WJwHbO9QVUnRH0yjwAP+X8/BHxp+wkiMi4iff7fU8C7gOeVl0V7HPjwbtfvB3RHJVWse7+plDA2kAtJ2VQYG7SvgF1HDA6SvEoFf5aa2rGZrIc2Kikkl2+bFkmnhJF8OCO/vFm1fr8gfOttVzmGsCW+yw4KQrYjqmH4BPB+EXkFeL//GhE5IyKf9s+5CzgrIt/BMwSfUEo977/388DPicg5vJzDZyLKEwu6adjlYtCC75mHjGRsJ3hBl1+Gr2SxneSdGAxHvy05SqSGpQWVUn7C0sWzDLf6edmBUwThK8zc5bHCRX+rzWi5h3IMu0EptQi8t8Pxs8BP+39/Hbhnh+vPA/dFkaEXEHbDEldlceB52OEoG/tJcWi1UlBKBaqaWdms0J9Nk8/aK72EthLfjQpM732+7l9k25sb1ZRNwAm4WK1TqTWceJljA7mQVFKVySH7co30Z9ms1KnWG4Go0eUNRzrmO15B75nO3+wnKikBrQ1LghoGVyGr/o5wVUluqh/GB3LUQpRfuqjigpbnH5Tm0s/Sdo4hLGWz7KjCBsI7H64qbML2S3JVEtpaxxP0WbqbLzQSw2AIYVbM6onajRKGo2yWN+yXOELb4AhotFYcbYQelrLRFTZujGnwRno6knERlYZtpOfKyIctCnGR94PwlVxNistB7k8jMQyGEGYnq+ZirR6bTOoNRaFUczs4gvKsriKGwTYqKQBWNqveKluLK9g1whQSNCMZR1RSULkqNa+zqu0IC8L35HJHo4bT/ZVipbmS2xUSw2AI4agkXWXgZqIrVuuUAmxxqOV3EcmETcCtFO12VtUYzKXJpiWwMV3arDBmubOqRpgkr0sqSXcXDtIrrOkUOapKguBborqiuMKWkS/7PcJsrmDfjsQwGEKYXdxccoZ6cARRQpdyhS2/dNGPCLx8URj6bXmj4qyMcDxMxOCYSoJgyVTXBgvCUEluIoZUystJBl0t7mIF+3YkhsEQvAcdlkpyF7YGUcJmO4weC6e90ssKEw4qWUDTb8EpG1c9bMJFDG5WFwNMDvUBBFox7tL5aK4vCvAsGw3lrPACwq3jcbGCfTsSw2AIE4Ne/5Mg4fTyZpWBXJq+jN2yOGirsgmQ6NJepgv+V5dfBhkchVKNal0x6dIzD5gYdDloxwdyrJdrVANRNlUnq4uB5nNZXA/ifLhzinS0tBTgWa6VvA2qXHnmYVb+u1r30Y7EMBjC1JC3mjHIRLfs89IuoL3sIN7c4kYZwEmNeZjyy8V1d3JBSMpms+IkwQttlE0AHVvacCeXjhi0/uyGFpVkX7ZcJsVofzagXG5LQicG+wIZUoDFjQrTw4lh2JeYGAw+OBbXK0wN99kWCYDJEHIt+Iqqr7GNicEciwEMljZqruTy+uXvPflqisuVN9fKywSjbFzJpQ1QEOfDZXkveM5EEB1z6RSB50gGkavRUCxtVJzpvkZiGAxBK1QQL2Bxo+yQFvEom4Ugcq1XGMyl6be4sUs7JgdzzWhgN2jZ3XnmXsTQ2GMfY4/WUUw4qi9v5WX2Nloud/zS3xNUxwYc6tjUYF8gHZtf82SfGnLksA3lWNrYW8d0sz1XBksjMQyGMOU/uIUASri4XnGmgJl0iomBYBPw4ka5SQu4wNRQsHBae3PuBm0f9Ybas5rFVeFVjdQAABanSURBVCtwDc1/B6IF18vNKNY2MumU3648mI65eo7gR6W9qGODwXRMy+XKKdJIDIMh6FBvr0GrlGJxveJ0Ap4cCjY4ljYqTj2TyaFcIEO6tK77EbnxgIMaef2+q+hvKiCXr5RiYb3ClENeOugEvLBebt5fFwhMJTmOSpsMwx7PUkdhLo0pJIbBGEb7s6RTsufgKJRqVOoNt4NjsC9wjsEllzk11Mfy5t6VXIsbFYbzGSdVXADT/iCc39MwuKcfABbWgunYtFPnoy/wBOzWKepjebNCfQ/KZnG9zEg+Qy7jZkrUOrMX/abnk4RK2qdIpYTxgdyeE7DrChv9XYHCacfe3FTAiqnFDXfUG9AsDNhr0OqIYdpRIUHWp2yCRjIu71nwfJFbKqlVLbjXs3RXEALthmGP+UInxZPk8/7F1FBubw9gw31oODXUt6f326x+cGoYgnpNZacca5Oy2eOeza+5N/JTQ33N790J+n2nhsFPpu6Guq9jLp2PiYBrLBbWy0w5nHyDFqssrFcQcbNSvB2JYTAIzzPfw5tbc+8BTA7mWCvVKNd27pdUKFWpNZRbuQJ6TV65nrvJZMynBYN45qP9WWcUF3iTfdCIwVUkA1659soeCzyXNys0lOtIJpiRX3TsFI0P5BDZW66ljTLjAzknCxXbEenbRGRCRB4TkVf83+Mdznm3iDzV9lMSkQ/57/22iLzW9t69UeSJGxODfXt6TQvNiMEllbR3YnwhBi5zKkQCzqVcqZQwOZjbk8ufX3NLvYFHc+1pGJoRg8s81t4LPOOguFo6tndU6lKudEqYGMg154OdsLju1inSiGqGPgZ8VSl1Gviq/3oLlFKPK6XuVUrdC7wH2AT+tO2Uf63fV0o9FVGeWDEZoDJDewguqZEgYWsz9xFHxLDLBNxoeIvIXHOskwE9c5deOXiJ8b2opIV1r02zy/46kwHyRfo5u82v7R0x1OoNlh3tKteOIAzDomOnSCOqYXgA+Jz/9+eAD+1x/oeB/6GU2oz4vT2JqaEca+Xari2uF9e9pmsuQ8Mg5Zfao3KphCP5DLl0ioVdIga9wMd1HfdUgFJaL2JwaximhnNsVOoUKzvr2IKfk3HRClxDP5/ddcx9xDDWnyUlu0cMzZX1jp/l5GDf3gUOjtcWaUSdnQ4rpeYA/N+H9jj/QeD3tx37FRF5WkQ+KSI73gER+aiInBWRs/Pz89GktoRWz5jdKBv3D7qVTA0QMTg0DCLirWXYJWJYiEEu8DzzvauSKs4jhiDVLK4rfwAODecBdo1m9Hsuy2hTKWFicPeikGbZsWPnI2jE4FouCGAYROQrIvJsh58HwnyRiMwA9wBfbjv8ceBO4B3ABPDzO12vlHpYKXVGKXVmejrALu0xQCv8tUJpx3Pi4AyDJHnn/eoHF51V2zE1tPsaiyv+vZwZ7XclEuBx+fPrZZTqXP9erNRZL9ecT8DaEF3bbQJed1v5A3Bk1DMMV3bT/Y0K2bQ0d1ZzhenhPPNru8nlRzIxGPndDFa5Vme1WI0lYtjzCSml3rfTeyJyVURmlFJz/sR/bZeP+vvAHymlmtkpHW0AZRH5LeBfBZS7J6EHx9VdBsf8epm7j464EgnwdiUbyKV3nUyurpaYHupzXv2w1+rnK6vevTwyknclEuBRSXobyuH89aWCcVT+QMv52DViWCtz69SgK5EAGOrLMNSXaT6vTlhYKzM52Od0JzKAIyN9zO0iV7Ps2LHDNj3cx3q5xmalxkDu+qn4WsGTS88rLhF1FngEeMj/+yHgS7uc+xG20Ui+MUE8TfkQ8GxEeWKFfoA7KaFSirnVIjOOJzkR4choftdBO1coMRODAh4a7uNqYReD5RvZQyNxUTadPTq9LsQlLQJ7U0leO4yyc+8XvGe0l1Pksk2HxpHR/l3l0uPVdVQ6s8d8MReTUwTRDcMngPeLyCvA+/3XiMgZEfm0PklETgLHgb/Ydv3vicgzwDPAFPDvIsoTKyYGcuTSqR3D6dVilVK1EYsHMDOaZ261uOP7V1aLMcnVz8J6mUqtc/373GqJ8YEs+ay7tQIAh/3BuJMxbfLljidgnWvZictfK9co19y2XNE4MpLflUqaWyk5n3zBk2thvbLjOp651SJjA1lnHV819L3YScdaNOo+MwxKqUWl1HuVUqf930v+8bNKqZ9uO+91pdQxpVRj2/XvUUrdo5R6s1Lqx5RS61HkiRuplHBopG/HB609gKNjcQyO/t0jhtVSLJ7J0bE8Su1Mv10tlJqTtEu0vLnOxjSO1cXgtcWYGsrteL9mVzx549GxPFd30bHZ1SJHY3KKoEXNbMeVmHR/r4jhiq97+5FKSrANM7tQNnMxPuiZ0TxX18odm4mtl2uslWocicGb016TntC240qhFMv90hPrTnJdXimSSYnziAE82S6v7KBjK/E5H4dH81xbK3fcY0Dr2ExMcsHOifHZlVI8hlTLtYPzMbdaYqgv0zHHZRuJYTCMw7uE082IIY5wejRPvaE6UhBXVuMLWY+O7eU1lWPx5vLZNBODOWZ3kOvycpGZsTxph2sFNI6O9nN5ufNSoMu+ITsWU8RQa6iO5dpzvlxx6NjM6O60YFzORz6bZnwgu4vulzjsOLemkRgGwzgy4kUMncoc51ZKpGPyMnejRpqVPzHlGMCjGbajWm+wuFGOhUoCz2jtFjHEMfkCHBvvZ3als47NrhTJpsV5UhxaeZlONNflGCmu3fJFpWqdpY1KLBQXePq/o2EoxJOTgcQwGMeR0TzlWqPjzkxzqyUOD/fF4mXulujSxiIOb26wL8NIvnOZ47W1MkrFY7DA88x3NAzLRY6NDTiWyMPRsX6K1XrHvkSzK14RgctVzxpHdvHM52KMSkfyGQZy6Y6R/JWYKpI0vKKQnSOGuHQ/MQyGsVvJ6lxMlT+we6JLe3jxeeaeB7wdcUYy4Mk110Guar3B1bUSx8Zjihh2yX/MrpRioSqBJu3RaQKeWymSknh0TESakfx2zMboFAF+Gfn1z7FWb3BtrRybXIlhMIyZXRJdV1ZLsSTfwNsvuC/TuZQ2rpJQjZ1KabXBiiPHAB6VtFauUSht9cw9qhBuiotK8r/3cgfDECfFNT3kRcOdnuXsaolDw3myjhdQahwZ7Zz7a0YMMd2zmdE8y5vV6/qrLax7u87F5awlhsEwdEh6eXnr4FBKMRvD4jYNEWFmtDNnPrdaiqUiSePIDjzrG0tegjUuz3yniqlL/rONSy6dsN+uY/WG4kohngobgEw6xdGxPBeXOumYl6yPCzOj/dfdL4h3ERm0dGy7/sdJ70JiGIzjyEieXCbVnNQ0PK+gEZtnAt5EdrHD4LiwuMGJifjkOjqaZ2mjcl3H0DeWNhkfyDISQ7ketBKl2+mkOBOp4HUyzWdT1xmsa2sl6g0V6wR8YmLgOt2HeCkugJsnB7hSKF3nmV9aLjIxmHO+uE1DOxeXtlWZ6Xt482Q8eazEMBhGKiWcmBjg9YWNLcdf81+fmornQQOcnBzkwuJWueoNxcWlIicn3fbWaccJX/m3TyhvLG5yIka5NCWzfdBqzzMub05E/LUMWw1Dc3FbjBPwiYkBLm57jvWG8iiumCIsaE2w22V7fWEjtskXaI677fPF6wubiMBN44lhuGFwcwevSRuGOCfgk5ODrGxWWWnbGH1utUil3uDmGOW6ZWoIaN0jjQtLG5yYiG/QHhruoz+b5vw2uS4tbzI93BdbTga8CeN6HfNen3TcQK8dJyYGWdyosNaWl7m8XKRSa3BLjHJp/X59cZthWNzgVIy6f3ikj3w21Xx2GhcWNzg62h+bjiWGwQJunhzkwuLmlhWgry9skE4Jx2Oc6PSE0T44Lvh/n4zTa/KjqHbDUKrWubxcjFWuVEq4ZXqQ8/NbDcOr8+vcOh3fZAJw6/Qgr86vb9GxV+fXyaaF4zF65qc6PMvzC16nm1umh2KRCVr63R4xFyt15lZLsRpSEekYyb++GG8kkxgGCzh9eIhitb4l1D+/sM5N4/2xVWUAnPIHwLlrrZZUr1xdA+DWQ/EN2uF8lunhPs7Pt+Q6P79BQ8Hth4djkwu8yezVNrmUUrxybZ3Th+KV67ZDQ5SqjS069uq1dU5ODjpvnd6O0/7zevnq1mcJcEuMxnRsIMfUUI6XfX0Hb/KFeCMs8MbluQ46Fuf9SgyDBejJ7MUrLSV8cW6NO4/EO5mcnBygL5PixblC89iLV9YYH8hyKIbV2O24/fAQL7UN2leurfnH471nt04PcnmlyGalBnjN89ZKNW6L0ZAC3OZ73+0Tyrn5eCcT8GjUXDrVfH4AL19dY2wgG8um9u2448gwL7WNyRf8cXBXzOPyziMjvLG0yUbZ07HZ1RJrpRp3HnG7b0s7EsNgAXf4ivbSFU/xNis1Xlvc4O6Z0TjFIpNOcceRYV64stUw3HFk2PnmKdtx98wIL15Zo1r3GvC+dGWNTEqaUU5ceNPRUZSC52e9e/a8P5mcPhyvYdCThp7c1ss1XlvoDR27ZXpwywT83GyBNx0diV3Hbj88zMtXW/TbC3MFcplU7Dp218wwStF0jLTjFqcjmRgGCxjqy3BycoCnL60C8MLcGkrhfOe2TrjryAjPzxZQSlGpNXjxSoG7ZuKX601HR6nUGk3a5qmLK9w5M0wuE6+KvuUmb6J95rL3LJ+6uIIIvOWmsTjFYnQgy6mpQZ56YwWAZy+vohS85Xi8hgG8e/bUxZWmjr10ZY03H41frrtnRihW680o67nZAnccHo6VegOa4+85X8eevVxABG7fr4ZBRH5ERJ4TkYaInNnlvPtF5CUROSciH2s7fkpEvikir4jIH4hIvLGmQZw5OcHZC8sopfjma4sA3Hs83skE4O0nx1nerPLy1XWeubxKqdrgvpMTcYvVvDdPvr5Mrd7gqYsrnLk5frkOj+Q5NNzH3/gT8LffWOH2Q8MM9bndt7gTvqttAn7qoiffPcfin4DffvM4K5tVzi9s8OzsKpV6I3ZDCvAOX8//+rUlKrUGf/PGMm+/eTxmqeCm8X4Oj/TxzdeWAPjr1xe568hIbOt3IHrE8Czwd4Gv7XSCiKSBTwEfAO4GPiIid/tv/yrwSaXUaWAZ+KmI8vQM7js5wdJGhRevrPH1c4vccXg4lq6q2/HOWyYB+PqrCzxx3jNY952KfwK+eXKAY2P9/OXL8zx1cYXNSp0zJ+MftAD/0+kp/vKVedbLNf76taWeuF8A33PLJNfWyrwwt8bjL17jziPDzjcO6oQz/gT8V68s8BcvzZMS+Fu3TsYsladjh4b7+Mari3z7jWVK1UZPyCUivPOWSZ44v0ixUudbF5b57lvi1bGoO7i9oJR6aY/T7gPOKaXOK6UqwOeBB/x9nt8DfNE/73N4+z7fEHjPXYfIpIT/9Gev8I3zi/zAndNxiwTA8YkBbj88xBfOXuIPv3WJt50YY7IHJhMR4d13TvO1V+Z5+GvnyWdT/MAdh+IWC4D33nmYlc0q/8f/9yzFap0PvPlI3CIB8L67DyMCv/EXr3L2wjLvu+tw3CIBcOv0EHccHuYP/+YSf/ydWe49PsZ4zIln8HTs/jcf4bEXrvLpv3qNfDbF9/SAYQB4z12HWViv8PN/+DSlaoP33x3vs3RBrh0DLra9vuQfmwRWlFK1bcdvCEwN9fGBe2Z49Jkr1BuKH3/nybhFauIn/tYpXpgrcH5ho6fk+sl3naJUbfCnz1/lh996rCfoGoD33X2I4xP9/NG3L3P74aGeiRimhvr4O285yh9/Z5ZMSnjwvuNxi9TEj73zZp6+tMr5hQ3+0ffeErc4TXzkvhNUag0ee/4qP/L247HSNe24/01HODKS55HvzHLboaFmZB8X9hx5IvIVoJOL9G+UUl8K8B2dShHULsd3kuOjwEcBTpw4EeBr48e//7v3cGi4j/fceSi2jped8OA7jlOq1kmnhAfuPRq3OE3cOj3Eb/3kO/jGq4v8i/fdHrc4TfRl0nzmoXfwxW9d4u+fOR57srIdv/zAm7l1eogzJ8dja5/QCT/23SdYK1UZH8hxf49EWOAlen/zx8/wnYsr/OMfuDVucZrIZVL87k/fx+984wL/6Htvib2CSzrtAhX6Q0T+HPhXSqmzHd57J/BvlVI/5L/+uP/WJ4B54IhSqrb9vN1w5swZdfbsdV+VIEGCBAl2gYh8Sym1Y6GQhgvX50ngtF+BlAMeBB5RnkV6HPiwf95DQJAIJEGCBAkSWETUctUfFpFLwDuB/y4iX/aPHxWRRwH8HMLPAl8GXgC+oJR6zv+Inwd+TkTO4eUcPhNFngQJEiRIEB1GqCTXSKikBAkSJAiPXqKSEiRIkCDBPkJiGBIkSJAgwRYkhiFBggQJEmxBYhgSJEiQIMEWJIYhQYIECRJswb6sShKReeBCl5dPAQsGxdkPSP7ng4Hkf77xEfX/vVkptWfjtn1pGKJARM4GKde6kZD8zwcDyf9848PV/5tQSQkSJEiQYAsSw5AgQYIECbbgIBqGh+MWIAYk//PBQPI/3/hw8v8euBxDggQJEiTYHQcxYkiQIEGCBLvgQBkGEblfRF4SkXMi8rG45bEBEfmsiFwTkWfbjk2IyGMi8or/uzc2UzYAETkuIo+LyAsi8pyI/DP/+I38P+dF5K9F5Dv+//x/+sdPicg3/f/5D/w29zcURCQtIt8WkT/xX9/Q/7OIvC4iz4jIUyJy1j9mXbcPjGEQkTTwKeADwN3AR0Tk7nilsoLfBu7fduxjwFeVUqeBr/qvbxTUgH+plLoL+B7gn/jP9Ub+n8vAe5RS3wXcC9wvIt8D/CrwSf9/XgZ+KkYZbeGf4bXv1zgI//O7lVL3tpWpWtftA2MYgPuAc0qp80qpCvB54IGYZTIOpdTXgKVthx8APuf//TngQ06Fsgil1JxS6m/8v9fwJo1j3Nj/s1JKrfsvs/6PAt4DfNE/fkP9zwAichPwPwOf9l8LN/j/vAOs6/ZBMgzHgIttry/5xw4CDiul5sCbSIFDMctjBSJyEngr8E1u8P/Zp1SeAq4BjwGvAiv+xlhwY+r3rwH/G9DwX09y4//PCvhTEfmWv+89ONDtjOkP7GF02l07Kcm6QSAiQ8AfAv9cKVWIezN121BK1YF7RWQM+CPgrk6nuZXKHkTkbwPXlFLfEpEf0Ic7nHrD/M8+3qWUmhWRQ8BjIvKiiy89SBHDJeB42+ubgNmYZHGNqyIyA+D/vhazPEYhIlk8o/B7Sqn/5h++of9nDaXUCvDnePmVMRHRzt6Npt/vAj4oIq/j0cDvwYsgbuT/GaXUrP/7Gp4DcB8OdPsgGYYngdN+FUMOeBB4JGaZXOER4CH/74eAL8Uoi1H4PPNngBeUUv9321s38v887UcKiEg/8D683MrjwIf9026o/1kp9XGl1E1KqZN4Y/fPlFL/kBv4fxaRQZH/v507xGkgiqIw/N8UiwGP6AJYAQKFIFgcy0DVNCHpNpCQ1MAeWACCrgDHElAH8YY0DwOC0tD5PzXj3k1ecm7uzUztfz4DZ8CKP7jbo/rArarOaV3GBLhNstjykX5dVd0Dp7S/ML4Bc+ARWAJHwCtwmeTrgvpfqqoT4Al4YT17ntH2DLta8zFt6TihNXfLJDdVNaV10wfAM3CV5H17J92MYZR0neRil2seansYXveAuySLqjpkw3d7VMEgSfremEZJkqQfMBgkSR2DQZLUMRgkSR2DQZLUMRgkSR2DQZLUMRgkSZ0PopVXBxTee2IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data=y,index=x,columns=['Sine'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Sine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.099833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.198669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0.295520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>0.389418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.479426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>0.564642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>0.644218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>0.717356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.783327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.841471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.1</th>\n",
       "      <td>0.891207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <td>0.932039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.3</th>\n",
       "      <td>0.963558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.4</th>\n",
       "      <td>0.985450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>0.997495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.6</th>\n",
       "      <td>0.999574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.7</th>\n",
       "      <td>0.991665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.8</th>\n",
       "      <td>0.973848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.9</th>\n",
       "      <td>0.946300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.909297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.1</th>\n",
       "      <td>0.863209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.2</th>\n",
       "      <td>0.808496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.3</th>\n",
       "      <td>0.745705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.4</th>\n",
       "      <td>0.675463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>0.598472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.6</th>\n",
       "      <td>0.515501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.7</th>\n",
       "      <td>0.427380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.8</th>\n",
       "      <td>0.334988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.9</th>\n",
       "      <td>0.239249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.1</th>\n",
       "      <td>0.023888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.2</th>\n",
       "      <td>-0.076037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.3</th>\n",
       "      <td>-0.175201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.4</th>\n",
       "      <td>-0.272615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.5</th>\n",
       "      <td>-0.367305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.6</th>\n",
       "      <td>-0.458325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.7</th>\n",
       "      <td>-0.544766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.8</th>\n",
       "      <td>-0.625764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.9</th>\n",
       "      <td>-0.700509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.0</th>\n",
       "      <td>-0.768255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.1</th>\n",
       "      <td>-0.828324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.2</th>\n",
       "      <td>-0.880118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.3</th>\n",
       "      <td>-0.923117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.4</th>\n",
       "      <td>-0.956893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.5</th>\n",
       "      <td>-0.981108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.6</th>\n",
       "      <td>-0.995521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.7</th>\n",
       "      <td>-0.999986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.8</th>\n",
       "      <td>-0.994460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.9</th>\n",
       "      <td>-0.978997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.0</th>\n",
       "      <td>-0.953753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.1</th>\n",
       "      <td>-0.918979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.2</th>\n",
       "      <td>-0.875023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.3</th>\n",
       "      <td>-0.822324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.4</th>\n",
       "      <td>-0.761408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.5</th>\n",
       "      <td>-0.692885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.6</th>\n",
       "      <td>-0.617439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.7</th>\n",
       "      <td>-0.535823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.8</th>\n",
       "      <td>-0.448854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.9</th>\n",
       "      <td>-0.357400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>-0.262375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>501 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sine\n",
       "0.0   0.000000\n",
       "0.1   0.099833\n",
       "0.2   0.198669\n",
       "0.3   0.295520\n",
       "0.4   0.389418\n",
       "0.5   0.479426\n",
       "0.6   0.564642\n",
       "0.7   0.644218\n",
       "0.8   0.717356\n",
       "0.9   0.783327\n",
       "1.0   0.841471\n",
       "1.1   0.891207\n",
       "1.2   0.932039\n",
       "1.3   0.963558\n",
       "1.4   0.985450\n",
       "1.5   0.997495\n",
       "1.6   0.999574\n",
       "1.7   0.991665\n",
       "1.8   0.973848\n",
       "1.9   0.946300\n",
       "2.0   0.909297\n",
       "2.1   0.863209\n",
       "2.2   0.808496\n",
       "2.3   0.745705\n",
       "2.4   0.675463\n",
       "2.5   0.598472\n",
       "2.6   0.515501\n",
       "2.7   0.427380\n",
       "2.8   0.334988\n",
       "2.9   0.239249\n",
       "...        ...\n",
       "47.1  0.023888\n",
       "47.2 -0.076037\n",
       "47.3 -0.175201\n",
       "47.4 -0.272615\n",
       "47.5 -0.367305\n",
       "47.6 -0.458325\n",
       "47.7 -0.544766\n",
       "47.8 -0.625764\n",
       "47.9 -0.700509\n",
       "48.0 -0.768255\n",
       "48.1 -0.828324\n",
       "48.2 -0.880118\n",
       "48.3 -0.923117\n",
       "48.4 -0.956893\n",
       "48.5 -0.981108\n",
       "48.6 -0.995521\n",
       "48.7 -0.999986\n",
       "48.8 -0.994460\n",
       "48.9 -0.978997\n",
       "49.0 -0.953753\n",
       "49.1 -0.918979\n",
       "49.2 -0.875023\n",
       "49.3 -0.822324\n",
       "49.4 -0.761408\n",
       "49.5 -0.692885\n",
       "49.6 -0.617439\n",
       "49.7 -0.535823\n",
       "49.8 -0.448854\n",
       "49.9 -0.357400\n",
       "50.0 -0.262375\n",
       "\n",
       "[501 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_percent=0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)*test_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_point=np.round(len(df)*test_percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ind=int(len(df)-test_point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "451"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=df.iloc[:test_ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=df.iloc[test_ind:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler(copy=True, feature_range=(0, 1))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_train=scaler.transform(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_test=scaler.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "length=50\n",
    "batch_size=1\n",
    "\n",
    "generator=TimeseriesGenerator(scaled_train,scaled_train,length=length,batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "451"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(scaled_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=generator[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[4.99999116e-01],\n",
       "        [5.49916225e-01],\n",
       "        [5.99334579e-01],\n",
       "        [6.47760405e-01],\n",
       "        [6.94709850e-01],\n",
       "        [7.39713809e-01],\n",
       "        [7.82322618e-01],\n",
       "        [8.22110545e-01],\n",
       "        [8.58680040e-01],\n",
       "        [8.91665714e-01],\n",
       "        [9.20737985e-01],\n",
       "        [9.45606372e-01],\n",
       "        [9.66022399e-01],\n",
       "        [9.81782075e-01],\n",
       "        [9.92727935e-01],\n",
       "        [9.98750612e-01],\n",
       "        [9.99789928e-01],\n",
       "        [9.95835500e-01],\n",
       "        [9.86926839e-01],\n",
       "        [9.73152957e-01],\n",
       "        [9.54651478e-01],\n",
       "        [9.31607263e-01],\n",
       "        [9.04250562e-01],\n",
       "        [8.72854714e-01],\n",
       "        [8.37733417e-01],\n",
       "        [7.99237589e-01],\n",
       "        [7.57751870e-01],\n",
       "        [7.13690771e-01],\n",
       "        [6.67494535e-01],\n",
       "        [6.19624741e-01],\n",
       "        [5.70559686e-01],\n",
       "        [5.20789614e-01],\n",
       "        [4.70811810e-01],\n",
       "        [4.21125636e-01],\n",
       "        [3.72227539e-01],\n",
       "        [3.24606094e-01],\n",
       "        [2.78737119e-01],\n",
       "        [2.35078919e-01],\n",
       "        [1.94067715e-01],\n",
       "        [1.56113277e-01],\n",
       "        [1.21594831e-01],\n",
       "        [9.08572769e-02],\n",
       "        [6.42077324e-02],\n",
       "        [4.19124713e-02],\n",
       "        [2.41942605e-02],\n",
       "        [1.12301346e-02],\n",
       "        [3.14962674e-03],\n",
       "        [3.34747619e-05],\n",
       "        [1.91281421e-03],\n",
       "        [8.76886734e-03]]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02053313]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y #this means after 4 x predict y as this\n",
    "   #if length is 2 this means after 2 x predicts y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.99999116e-01],\n",
       "       [5.49916225e-01],\n",
       "       [5.99334579e-01],\n",
       "       [6.47760405e-01],\n",
       "       [6.94709850e-01],\n",
       "       [7.39713809e-01],\n",
       "       [7.82322618e-01],\n",
       "       [8.22110545e-01],\n",
       "       [8.58680040e-01],\n",
       "       [8.91665714e-01],\n",
       "       [9.20737985e-01],\n",
       "       [9.45606372e-01],\n",
       "       [9.66022399e-01],\n",
       "       [9.81782075e-01],\n",
       "       [9.92727935e-01],\n",
       "       [9.98750612e-01],\n",
       "       [9.99789928e-01],\n",
       "       [9.95835500e-01],\n",
       "       [9.86926839e-01],\n",
       "       [9.73152957e-01],\n",
       "       [9.54651478e-01],\n",
       "       [9.31607263e-01],\n",
       "       [9.04250562e-01],\n",
       "       [8.72854714e-01],\n",
       "       [8.37733417e-01],\n",
       "       [7.99237589e-01],\n",
       "       [7.57751870e-01],\n",
       "       [7.13690771e-01],\n",
       "       [6.67494535e-01],\n",
       "       [6.19624741e-01],\n",
       "       [5.70559686e-01],\n",
       "       [5.20789614e-01],\n",
       "       [4.70811810e-01],\n",
       "       [4.21125636e-01],\n",
       "       [3.72227539e-01],\n",
       "       [3.24606094e-01],\n",
       "       [2.78737119e-01],\n",
       "       [2.35078919e-01],\n",
       "       [1.94067715e-01],\n",
       "       [1.56113277e-01],\n",
       "       [1.21594831e-01],\n",
       "       [9.08572769e-02],\n",
       "       [6.42077324e-02],\n",
       "       [4.19124713e-02],\n",
       "       [2.41942605e-02],\n",
       "       [1.12301346e-02],\n",
       "       [3.14962674e-03],\n",
       "       [3.34747619e-05],\n",
       "       [1.91281421e-03],\n",
       "       [8.76886734e-03],\n",
       "       [2.05331307e-02],\n",
       "       [3.70880598e-02],\n",
       "       [5.82682430e-02],\n",
       "       [8.38620552e-02],\n",
       "       [1.13613771e-01],\n",
       "       [1.47226122e-01],\n",
       "       [1.84363264e-01],\n",
       "       [2.24654135e-01],\n",
       "       [2.67696162e-01],\n",
       "       [3.13059283e-01],\n",
       "       [3.60290246e-01],\n",
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       "       [3.24190300e-01],\n",
       "       [2.78338972e-01],\n",
       "       [2.34702398e-01],\n",
       "       [1.93716581e-01],\n",
       "       [1.55791039e-01],\n",
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       "       [9.06021693e-02],\n",
       "       [6.39901882e-02],\n",
       "       [4.17346643e-02],\n",
       "       [2.40579672e-02],\n",
       "       [1.11367167e-02],\n",
       "       [3.10001778e-03],\n",
       "       [2.81703720e-05],\n",
       "       [1.95186738e-03],\n",
       "       [8.85188786e-03],\n",
       "       [2.06592891e-02],\n",
       "       [3.72560954e-02],\n",
       "       [5.84764771e-02],\n",
       "       [8.41084070e-02],\n",
       "       [1.13895779e-01],\n",
       "       [1.47540969e-01],\n",
       "       [1.84707803e-01],\n",
       "       [2.25024924e-01],\n",
       "       [2.68089497e-01],\n",
       "       [3.13471233e-01],\n",
       "       [3.60716695e-01],\n",
       "       [4.09353821e-01],\n",
       "       [4.58896644e-01],\n",
       "       [5.08850150e-01],\n",
       "       [5.58715219e-01],\n",
       "       [6.07993616e-01],\n",
       "       [6.56192968e-01],\n",
       "       [7.02831682e-01],\n",
       "       [7.47443761e-01],\n",
       "       [7.89583454e-01],\n",
       "       [8.28829716e-01],\n",
       "       [8.64790412e-01],\n",
       "       [8.97106234e-01],\n",
       "       [9.25454293e-01]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "length=50\n",
    "batch_size=1\n",
    "\n",
    "generator=TimeseriesGenerator(scaled_train,scaled_train,length=length,batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense,SimpleRNN,LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_features=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(SimpleRNN(50,input_shape=(length,n_features)))\n",
    "\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam',loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "simple_rnn (SimpleRNN)       (None, 50)                2600      \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 2,651\n",
      "Trainable params: 2,651\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-64-063492f5aede>:1: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use Model.fit, which supports generators.\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 401 steps\n",
      "Epoch 1/5\n",
      "401/401 [==============================] - 9s 21ms/step - loss: 0.0091\n",
      "Epoch 2/5\n",
      "401/401 [==============================] - 8s 20ms/step - loss: 1.5108e-04\n",
      "Epoch 3/5\n",
      "401/401 [==============================] - 8s 20ms/step - loss: 5.9223e-05: 1s\n",
      "Epoch 4/5\n",
      "401/401 [==============================] - 8s 20ms/step - loss: 0.0012\n",
      "Epoch 5/5\n",
      "401/401 [==============================] - 7s 18ms/step - loss: 4.2773e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b150aa81d0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(generator,epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses=pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b15081d3c8>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_eval_batch=scaled_train[-length:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_eval_batch=first_eval_batch.reshape((1,length,n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.95024574]], dtype=float32)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(first_eval_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.94955134])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions=[]\n",
    "\n",
    "\n",
    "first_eval_batch=scaled_train[-length:]\n",
    "current_batch=first_eval_batch.reshape((1,length,n_features))\n",
    "\n",
    "for i in range(len(test)):\n",
    "    current_pred=model.predict(current_batch)[0]\n",
    "    \n",
    "    test_predictions.append(current_pred)\n",
    "    \n",
    "    current_batch=np.append(current_batch[:,1:,:],[[current_pred]],axis=1)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.95024574], dtype=float32),\n",
       " array([0.9692302], dtype=float32),\n",
       " array([0.98332], dtype=float32),\n",
       " array([0.9923617], dtype=float32),\n",
       " array([0.99625623], dtype=float32),\n",
       " array([0.9949897], dtype=float32),\n",
       " array([0.9890636], dtype=float32),\n",
       " array([0.977695], dtype=float32),\n",
       " array([0.9617392], dtype=float32),\n",
       " array([0.9414637], dtype=float32),\n",
       " array([0.91697776], dtype=float32),\n",
       " array([0.88869184], dtype=float32),\n",
       " array([0.85703564], dtype=float32),\n",
       " array([0.821872], dtype=float32),\n",
       " array([0.78396916], dtype=float32),\n",
       " array([0.7437765], dtype=float32),\n",
       " array([0.7012765], dtype=float32),\n",
       " array([0.6570749], dtype=float32),\n",
       " array([0.61152834], dtype=float32),\n",
       " array([0.5646301], dtype=float32),\n",
       " array([0.5173013], dtype=float32),\n",
       " array([0.46972504], dtype=float32),\n",
       " array([0.42189956], dtype=float32),\n",
       " array([0.37478387], dtype=float32),\n",
       " array([0.32868174], dtype=float32),\n",
       " array([0.28389114], dtype=float32),\n",
       " array([0.24136308], dtype=float32),\n",
       " array([0.20139109], dtype=float32),\n",
       " array([0.16404179], dtype=float32),\n",
       " array([0.13049632], dtype=float32),\n",
       " array([0.10079952], dtype=float32),\n",
       " array([0.07509457], dtype=float32),\n",
       " array([0.05426009], dtype=float32),\n",
       " array([0.03790446], dtype=float32),\n",
       " array([0.02590914], dtype=float32),\n",
       " array([0.01922837], dtype=float32),\n",
       " array([0.01720485], dtype=float32),\n",
       " array([0.01959506], dtype=float32),\n",
       " array([0.02712191], dtype=float32),\n",
       " array([0.03875527], dtype=float32),\n",
       " array([0.05446129], dtype=float32),\n",
       " array([0.07504781], dtype=float32),\n",
       " array([0.09918932], dtype=float32),\n",
       " array([0.12676856], dtype=float32),\n",
       " array([0.15855585], dtype=float32),\n",
       " array([0.19306667], dtype=float32),\n",
       " array([0.23031497], dtype=float32),\n",
       " array([0.27118158], dtype=float32),\n",
       " array([0.31358156], dtype=float32),\n",
       " array([0.35767716], dtype=float32)]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.49551344e-01],\n",
       "       [9.69156618e-01],\n",
       "       [9.84074225e-01],\n",
       "       [9.94155114e-01],\n",
       "       [9.99298559e-01],\n",
       "       [9.99453170e-01],\n",
       "       [9.94617400e-01],\n",
       "       [9.84839568e-01],\n",
       "       [9.70217371e-01],\n",
       "       [9.50896908e-01],\n",
       "       [9.27071224e-01],\n",
       "       [8.98978376e-01],\n",
       "       [8.66899060e-01],\n",
       "       [8.31153800e-01],\n",
       "       [7.92099752e-01],\n",
       "       [7.50127132e-01],\n",
       "       [7.05655314e-01],\n",
       "       [6.59128649e-01],\n",
       "       [6.11012013e-01],\n",
       "       [5.61786173e-01],\n",
       "       [5.11942978e-01],\n",
       "       [4.61980443e-01],\n",
       "       [4.12397778e-01],\n",
       "       [3.63690397e-01],\n",
       "       [3.16344967e-01],\n",
       "       [2.70834550e-01],\n",
       "       [2.27613868e-01],\n",
       "       [1.87114770e-01],\n",
       "       [1.49741909e-01],\n",
       "       [1.15868702e-01],\n",
       "       [8.58335998e-02],\n",
       "       [5.99367021e-02],\n",
       "       [3.84367625e-02],\n",
       "       [2.15486014e-02],\n",
       "       [9.44095974e-03],\n",
       "       [2.23481297e-03],\n",
       "       [2.16257798e-06],\n",
       "       [2.76531647e-03],\n",
       "       [1.04966661e-02],\n",
       "       [2.31189624e-02],\n",
       "       [4.05060876e-02],\n",
       "       [6.24843152e-02],\n",
       "       [8.88340461e-02],\n",
       "       [1.19292002e-01],\n",
       "       [1.53553858e-01],\n",
       "       [1.91277281e-01],\n",
       "       [2.32085350e-01],\n",
       "       [2.75570325e-01],\n",
       "       [3.21297718e-01],\n",
       "       [3.68810636e-01]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_predictions=scaler.inverse_transform(test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.90048602],\n",
       "       [ 0.93845459],\n",
       "       [ 0.96663401],\n",
       "       [ 0.98471731],\n",
       "       [ 0.99250627],\n",
       "       [ 0.98997321],\n",
       "       [ 0.97812116],\n",
       "       [ 0.95538408],\n",
       "       [ 0.92347272],\n",
       "       [ 0.8829221 ],\n",
       "       [ 0.8339506 ],\n",
       "       [ 0.77737921],\n",
       "       [ 0.71406731],\n",
       "       [ 0.64374059],\n",
       "       [ 0.56793554],\n",
       "       [ 0.48755086],\n",
       "       [ 0.4025515 ],\n",
       "       [ 0.3141491 ],\n",
       "       [ 0.22305665],\n",
       "       [ 0.12926091],\n",
       "       [ 0.03460413],\n",
       "       [-0.06054766],\n",
       "       [-0.15619786],\n",
       "       [-0.25042847],\n",
       "       [-0.34263201],\n",
       "       [-0.43221248],\n",
       "       [-0.51726792],\n",
       "       [-0.59721127],\n",
       "       [-0.67190926],\n",
       "       [-0.73899965],\n",
       "       [-0.79839278],\n",
       "       [-0.84980228],\n",
       "       [-0.8914709 ],\n",
       "       [-0.92418189],\n",
       "       [-0.94817235],\n",
       "       [-0.96153378],\n",
       "       [-0.96558079],\n",
       "       [-0.96080041],\n",
       "       [-0.94574683],\n",
       "       [-0.92248029],\n",
       "       [-0.8910685 ],\n",
       "       [-0.8498958 ],\n",
       "       [-0.80161316],\n",
       "       [-0.74645512],\n",
       "       [-0.68288105],\n",
       "       [-0.61385996],\n",
       "       [-0.53936396],\n",
       "       [-0.45763139],\n",
       "       [-0.37283213],\n",
       "       [-0.28464162]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADITYA\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test['Predicitons']=true_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Sine</th>\n",
       "      <th>Predicitons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45.1</th>\n",
       "      <td>0.899097</td>\n",
       "      <td>0.900486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.2</th>\n",
       "      <td>0.938307</td>\n",
       "      <td>0.938455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.3</th>\n",
       "      <td>0.968142</td>\n",
       "      <td>0.966634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.4</th>\n",
       "      <td>0.988304</td>\n",
       "      <td>0.984717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.5</th>\n",
       "      <td>0.998591</td>\n",
       "      <td>0.992506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.6</th>\n",
       "      <td>0.998900</td>\n",
       "      <td>0.989973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.7</th>\n",
       "      <td>0.989229</td>\n",
       "      <td>0.978121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.8</th>\n",
       "      <td>0.969673</td>\n",
       "      <td>0.955384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.9</th>\n",
       "      <td>0.940429</td>\n",
       "      <td>0.923473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.0</th>\n",
       "      <td>0.901788</td>\n",
       "      <td>0.882922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.1</th>\n",
       "      <td>0.854137</td>\n",
       "      <td>0.833951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.2</th>\n",
       "      <td>0.797952</td>\n",
       "      <td>0.777379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.3</th>\n",
       "      <td>0.733794</td>\n",
       "      <td>0.714067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.4</th>\n",
       "      <td>0.662304</td>\n",
       "      <td>0.643741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.5</th>\n",
       "      <td>0.584197</td>\n",
       "      <td>0.567936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.6</th>\n",
       "      <td>0.500252</td>\n",
       "      <td>0.487551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.7</th>\n",
       "      <td>0.411309</td>\n",
       "      <td>0.402551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.8</th>\n",
       "      <td>0.318257</td>\n",
       "      <td>0.314149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.9</th>\n",
       "      <td>0.222024</td>\n",
       "      <td>0.223057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.0</th>\n",
       "      <td>0.123573</td>\n",
       "      <td>0.129261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.1</th>\n",
       "      <td>0.023888</td>\n",
       "      <td>0.034604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.2</th>\n",
       "      <td>-0.076037</td>\n",
       "      <td>-0.060548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.3</th>\n",
       "      <td>-0.175201</td>\n",
       "      <td>-0.156198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.4</th>\n",
       "      <td>-0.272615</td>\n",
       "      <td>-0.250428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.5</th>\n",
       "      <td>-0.367305</td>\n",
       "      <td>-0.342632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.6</th>\n",
       "      <td>-0.458325</td>\n",
       "      <td>-0.432212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.7</th>\n",
       "      <td>-0.544766</td>\n",
       "      <td>-0.517268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.8</th>\n",
       "      <td>-0.625764</td>\n",
       "      <td>-0.597211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47.9</th>\n",
       "      <td>-0.700509</td>\n",
       "      <td>-0.671909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.0</th>\n",
       "      <td>-0.768255</td>\n",
       "      <td>-0.739000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.1</th>\n",
       "      <td>-0.828324</td>\n",
       "      <td>-0.798393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.2</th>\n",
       "      <td>-0.880118</td>\n",
       "      <td>-0.849802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.3</th>\n",
       "      <td>-0.923117</td>\n",
       "      <td>-0.891471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.4</th>\n",
       "      <td>-0.956893</td>\n",
       "      <td>-0.924182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.5</th>\n",
       "      <td>-0.981108</td>\n",
       "      <td>-0.948172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.6</th>\n",
       "      <td>-0.995521</td>\n",
       "      <td>-0.961534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.7</th>\n",
       "      <td>-0.999986</td>\n",
       "      <td>-0.965581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.8</th>\n",
       "      <td>-0.994460</td>\n",
       "      <td>-0.960800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.9</th>\n",
       "      <td>-0.978997</td>\n",
       "      <td>-0.945747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.0</th>\n",
       "      <td>-0.953753</td>\n",
       "      <td>-0.922480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.1</th>\n",
       "      <td>-0.918979</td>\n",
       "      <td>-0.891069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.2</th>\n",
       "      <td>-0.875023</td>\n",
       "      <td>-0.849896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.3</th>\n",
       "      <td>-0.822324</td>\n",
       "      <td>-0.801613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.4</th>\n",
       "      <td>-0.761408</td>\n",
       "      <td>-0.746455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.5</th>\n",
       "      <td>-0.692885</td>\n",
       "      <td>-0.682881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.6</th>\n",
       "      <td>-0.617439</td>\n",
       "      <td>-0.613860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.7</th>\n",
       "      <td>-0.535823</td>\n",
       "      <td>-0.539364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.8</th>\n",
       "      <td>-0.448854</td>\n",
       "      <td>-0.457631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.9</th>\n",
       "      <td>-0.357400</td>\n",
       "      <td>-0.372832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>-0.262375</td>\n",
       "      <td>-0.284642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sine  Predicitons\n",
       "45.1  0.899097     0.900486\n",
       "45.2  0.938307     0.938455\n",
       "45.3  0.968142     0.966634\n",
       "45.4  0.988304     0.984717\n",
       "45.5  0.998591     0.992506\n",
       "45.6  0.998900     0.989973\n",
       "45.7  0.989229     0.978121\n",
       "45.8  0.969673     0.955384\n",
       "45.9  0.940429     0.923473\n",
       "46.0  0.901788     0.882922\n",
       "46.1  0.854137     0.833951\n",
       "46.2  0.797952     0.777379\n",
       "46.3  0.733794     0.714067\n",
       "46.4  0.662304     0.643741\n",
       "46.5  0.584197     0.567936\n",
       "46.6  0.500252     0.487551\n",
       "46.7  0.411309     0.402551\n",
       "46.8  0.318257     0.314149\n",
       "46.9  0.222024     0.223057\n",
       "47.0  0.123573     0.129261\n",
       "47.1  0.023888     0.034604\n",
       "47.2 -0.076037    -0.060548\n",
       "47.3 -0.175201    -0.156198\n",
       "47.4 -0.272615    -0.250428\n",
       "47.5 -0.367305    -0.342632\n",
       "47.6 -0.458325    -0.432212\n",
       "47.7 -0.544766    -0.517268\n",
       "47.8 -0.625764    -0.597211\n",
       "47.9 -0.700509    -0.671909\n",
       "48.0 -0.768255    -0.739000\n",
       "48.1 -0.828324    -0.798393\n",
       "48.2 -0.880118    -0.849802\n",
       "48.3 -0.923117    -0.891471\n",
       "48.4 -0.956893    -0.924182\n",
       "48.5 -0.981108    -0.948172\n",
       "48.6 -0.995521    -0.961534\n",
       "48.7 -0.999986    -0.965581\n",
       "48.8 -0.994460    -0.960800\n",
       "48.9 -0.978997    -0.945747\n",
       "49.0 -0.953753    -0.922480\n",
       "49.1 -0.918979    -0.891069\n",
       "49.2 -0.875023    -0.849896\n",
       "49.3 -0.822324    -0.801613\n",
       "49.4 -0.761408    -0.746455\n",
       "49.5 -0.692885    -0.682881\n",
       "49.6 -0.617439    -0.613860\n",
       "49.7 -0.535823    -0.539364\n",
       "49.8 -0.448854    -0.457631\n",
       "49.9 -0.357400    -0.372832\n",
       "50.0 -0.262375    -0.284642"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b15067be80>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop=EarlyStopping(monitor='val_loss',patience=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "length=49\n",
    "generator=TimeseriesGenerator(scaled_train,scaled_train,length=length,batch_size=1)\n",
    "\n",
    "validation_generator=TimeseriesGenerator(scaled_test,scaled_test,length=length,batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(LSTM(50,input_shape=(length,n_features)))\n",
    "\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam',loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 402 steps, validate for 1 steps\n",
      "Epoch 1/20\n",
      "402/402 [==============================] - 12s 31ms/step - loss: 5.0952e-06 - val_loss: 5.3788e-07\n",
      "Epoch 2/20\n",
      "402/402 [==============================] - 12s 30ms/step - loss: 4.1382e-06 - val_loss: 1.2138e-05\n",
      "Epoch 3/20\n",
      "402/402 [==============================] - 12s 30ms/step - loss: 1.2156e-05 - val_loss: 1.0471e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b15c9f93c8>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(generator,epochs=20,validation_data=validation_generator,callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions=[]\n",
    "\n",
    "\n",
    "first_eval_batch=scaled_train[-length:]\n",
    "current_batch=first_eval_batch.reshape((1,length,n_features))\n",
    "\n",
    "for i in range(len(test)):\n",
    "    current_pred=model.predict(current_batch)[0]\n",
    "    \n",
    "    test_predictions.append(current_pred)\n",
    "    \n",
    "    current_batch=np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADITYA\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b15dc19128>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_predictions= scaler.inverse_transform(test_predictions)\n",
    "test['LSTM Predictions']=true_predictions\n",
    "test.plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_scaler=MinMaxScaler()\n",
    "scaled_full_data=full_scaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "generator=TimeseriesGenerator(scaled_train,scaled_train,length=length,batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(LSTM(50,input_shape=(length,n_features)))\n",
    "\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam',loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 402 steps\n",
      "Epoch 1/6\n",
      "402/402 [==============================] - 15s 36ms/step - loss: 0.0138\n",
      "Epoch 2/6\n",
      "402/402 [==============================] - 12s 29ms/step - loss: 8.6582e-05\n",
      "Epoch 3/6\n",
      "402/402 [==============================] - 12s 29ms/step - loss: 1.3958e-04\n",
      "Epoch 4/6\n",
      "402/402 [==============================] - 12s 29ms/step - loss: 8.5114e-05\n",
      "Epoch 5/6\n",
      "402/402 [==============================] - 12s 29ms/step - loss: 3.4279e-04\n",
      "Epoch 6/6\n",
      "402/402 [==============================] - 12s 29ms/step - loss: 7.3607e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b15e15e208>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(generator,epochs=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast=[]\n",
    "\n",
    "\n",
    "first_eval_batch=scaled_train[-length:]\n",
    "current_batch=first_eval_batch.reshape((1,length,n_features))\n",
    "\n",
    "for i in range(25):\n",
    "    current_pred=model.predict(current_batch)[0]\n",
    "    \n",
    "    forecast.append(current_pred)\n",
    "    \n",
    "    current_batch=np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast=scaler.inverse_transform(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25*0.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_index=np.arange(50.1,52.6,step=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b160770240>]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.index,df['Sine'])\n",
    "plt.plot(forecast_index,forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}