ARMA_feature.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nnkKq8DV3ThO"
   },
   "outputs": [],
   "source": [
    "# Autoencoder NHPP Wavenet: NHPP Are instead drawn from autoencoded data to remove the noise\n",
    "# The autoencoder is just a combination of Linear layers with sigmoid activations, but\n",
    "# can be more complex if one would want to develop it\n",
    "\n",
    "\n",
    "# Importing modules\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import numpy as np\n",
    "import torch\n",
    "import random\n",
    "import scipy\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from IPython.display import Audio\n",
    "from scipy.io import wavfile\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from collections import Counter\n",
    "from collections import defaultdict\n",
    "import math\n",
    "import copy\n",
    "import random\n",
    "import pickle\n",
    "import scipy.fftpack as fft\n",
    "from numpy import fft as npfft\n",
    "import time\n",
    "import statsmodels.tsa.arima_model as arima_model\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "53FeW83gQ20S",
    "outputId": "ecca089f-992b-493a-f554-04f087de78b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(5.8985)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "file = 'AEP_hourly'\n",
    "if (file[0] == 'Noise'):\n",
    "    df = pd.read_csv(file + '.csv')\n",
    "    vals = []\n",
    "    for row in df.iterrows():\n",
    "      vals.append(row[1])\n",
    "    vals = torch.Tensor(vals)\n",
    "    vals = vals.squeeze(1)\n",
    "    \n",
    "else:\n",
    "  df = pd.read_csv(file + '.csv')\n",
    "\n",
    "  class Value(object):\n",
    "\n",
    "      \n",
    "      def __init__(self, date, val):\n",
    "          self.date = date\n",
    "          self.value = val\n",
    "          \n",
    "  unordered_time_series = defaultdict(float)\n",
    "\n",
    "\n",
    "  for row in df.values:\n",
    "      if (math.isnan(row[1]) == False ):\n",
    "          \n",
    "          last_date = row[1]\n",
    "        \n",
    "        \n",
    "          unordered_time_series[row[0]] += row[1] \n",
    "  #     else:\n",
    "  #       unordered_time_series[last_date] += row[2]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "  # Separating the data by date\n",
    "\n",
    "  time_series = []\n",
    "\n",
    "  for key in unordered_time_series.keys():\n",
    "      year = int(key.split('-')[0])\n",
    "      month = int(key.split('-')[1])\n",
    "      day = int(key.split('-')[2].split(' ')[0])\n",
    "      hour = int(key.split(' ')[1].split(':')[0])\n",
    "      date = datetime.date(year, month, day)\n",
    "      time_series.append(Value(date, unordered_time_series[key]))\n",
    "      \n",
    "      \n",
    "      \n",
    "      \n",
    "      \n",
    "  # Sorting the data\n",
    "  time_series.sort(key=lambda r: r.date)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "  # Creating the data list\n",
    "\n",
    "  vals = [] # list which contains the data\n",
    "  days = []\n",
    "  months = []\n",
    "  weekdays = []\n",
    "  value2index = defaultdict(int)\n",
    "  index = 0\n",
    "  for i in time_series:\n",
    "      months.append(int(str(i.date).split(' ')[0].split('-')[1]))\n",
    "      days.append(int(str(i.date).split(' ')[0].split('-')[1]))\n",
    "      weekdays.append(int(i.date.weekday()))\n",
    "      vals.append(i.value)\n",
    "      \n",
    "      value2index[i.value] = index\n",
    "      index += 1\n",
    "  vals_backup = vals\n",
    "  print (len(vals))\n",
    "  vals = torch.Tensor(vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "rhxiLlpoYCPm",
    "outputId": "350386e0-3048-4928-8c9a-535e66c3d518"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([99999])\n"
     ]
    }
   ],
   "source": [
    "print (vals.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QoPqgOnPu9ny"
   },
   "outputs": [],
   "source": [
    "  \n",
    "class RMSELoss(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.mse = nn.MSELoss()\n",
    "        \n",
    "    def forward(self,yhat,y):\n",
    "        return torch.sqrt(self.mse(yhat,y))\n",
    "\n",
    "vals_normed = torch.Tensor(vals)\n",
    "max_vals = max(vals)\n",
    "vals_normed = (vals_normed/max_vals + 1)/2 - 0.5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "y5dA8bntQmxi",
    "outputId": "3d3d0b5a-ab9b-4cab-8468-ee85d7513fda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f0fc4a10b38>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sm4vXAQSX90qAoIDpNw4rgGHbRFSJwa7witQVfYxMFvmzbx/GQVDExgv1tpgB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\nZV/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VN2JznduyPQrmXnFsW92A16ls9H3xNKdHk6okYD7P4b6xeTkhO6JBHrh5G5omrJV/\n39kU8XQW7xzFW8IBnc+95xIevn0Xu9obEMCaZt9rqj95nGpFdb6zGM1Y9icO9ani9bpGa73XqrOc\nyBYsXflwoLT4eFmQlgqk7lD+g8Hx8kxvxcQiARKZHKeGk6xu8rF/V5sqr+nU+LubLpp159PVF+el\n3jgnR1KMp/MMJXO4NLUKNaUSokGdBq+T4WSuIif1fDBrKZhjoUTJUpt1mDMFCPs9eBzwF2/eNOsi\nKxzQuWXHKlJ55f9KZCSnR9M0eJ34yshvKKYjGuRj+y60nmckDBuDfnH9i9naN7UKWzoPqbz6rAbv\nfDIAagt7AqiQy4wiL7oDhsbSxFM5nu4aot7nYmA8Y0XweFxza4WD2lE4HGadWxceN7xtRxsXr6qf\nt6NoNJklmZVkUSuXgpSkspL7S4wbv/3hAxw4NjKpbrHHObGaSuXU6rtcO3KlmIqPoMINm+t0ekZU\njV0z4lLXUAk97rlv1uHxDNn8hO6RGQUyMJ7hkV8fm/d5up0aKxu99MVTfPf5k5wZy9De5JszrHF3\nrJl372qbdKze66YvnqZrIKkG/kSa0WSO9c2+8+5IDPl1VjV4J4XMDifTjCQnVvqnRlKk8nPLmCv1\n2lcBpZ8lmKgXEfKVnqE8FVMuxNyRmz613xwbKclmb/qWNFQknM8lWNXgYZURDtrgcUwrMb5YsCeA\nCmmsc1ulH4tvhCafC6cmJlbOTXPHyJurdYem4UBFsSQz8PUD3dx66ep5mamKbyxQndlQgZg1C9ik\nqy9O3+hExSiB8kk0+nSGE1lWNXhoMATAzmcxa1Bt+83xYZxAJq+cpZpDJUo1Bzx4XQKHQ8Pp0HA5\n5u7qsUiAWEsdbn3CDORxqElk70Xl+19A7VJeORNnKJEh5PcQMmrAllI4p7N3lH9/sRePY2JSiqcm\n7PzJTI5UDpr8bj6z/5Lzbks2E+iKZcyTWSjyt86oNTXdZ939pk1K/sRwwpsbZrNgTSXcsL3VijBy\nAuua56750RENcvfVG/G5BR636kMSOJvK4tCEJdjodpa2uKtF7AmgQkJ+nWYjW7M4bG3jigArGzwc\nODaC26GyB0tBOVIlBaFqC9x9dYzN0UDZSTAm5k3qZOJiuwRsKDEtPxYJsMFQnswakteZnKqg5RAq\nEW4klZ9XmGqlhAM6V20Mk8MIm83BXVesI19Q5Qh1h0YyW+D44FjJIb5Br5t8TpmBIgGlKLltdWNF\ndVqdDo0mn5sTgwnyBYljmvoL0xHy67SH6nC7Haxp9uF1CVxOB3ftaZ8UXTYwljnv5jeTE4MJMnmj\nX8ww2rtmyX8pZm1YOYGDXjfRoE5LQJm55uOANzGDIH7w2x6e/H0fQY+DPJS0oOrsHeVzvzhCLidx\na4K+eJpkVuISAr/utExDjql1LhcRi/fMa4SuvrgVgVKcvPLkob6iIioamRKXMSG/zoaWoBLQcgi+\n+v+OV3yOJwYT5ACfsarNytKTtsyVWTGmMy8jlZnk5u2tPHLnZfOK5KkUcyCVEhDwYs+IVZd2JKXk\nr1O50iqpmaGNXt3J/l1tNPvdnI6nZ9TrKYWOaJDPv/sS/LqTAnA6nqZ9mvoLM53Pl267lDt2ryXg\ncVHISzLZPD9+qY+es0qEsMXvptnnmvcCoRI6e0f5xBMvoU9JJZlqEnHOkoBXzPB4hkRWySP0jSof\nmCaU/HUlu5uCNOr2ShW5Ey0xq7gjGmTflhVkCkxSOR1K5ugbTeNwKImJf7hl5nDSWseeACokFglY\nei8A7SGfNXgMJbJGRaECPcPJklO9k5kcJ0dSHB1IsHfLCnrOTq9hUyrbVjfgcaqqRU5gTZOXT99a\netjr2nCdJaQFakvuL6p29v3f9pSdo7DQmLH1T3UO4JoikeBxlO6fiEUCrKz38tvuEY4OjiMprczi\nbOyONXPTxStxCGjyusraTRw8qmrRHhsct7Kcjw0lMaMqz4yp8onff+5kRec4H4bHMyQyeYJF5RWb\niuS3zegxba4YXAOzFnXQUBFN5dTEXunO0qs7cKKysdc0eUlmciXdT/3xtFWLu5hYs5fRREad36L2\nANgTwIIQrfda+iJSSr5+oJszYxl0TQ1MuTzcs7ej5FWCaS4qAN/73Snycv4hlqBu1FQOTo6kyKFW\nQuXI/n7k0ees7F/zvMzomlizl/aQj28+O3eY37nA9MG0+N2sqFcTcbYA49kcbQ0eNrbUoZVg/zcZ\nHEvTM5Lk1YEx0tkC77ykreLVXWfvKJ//xREuWdOgRPnKmCzXhuvQHcoZ2tagrtmeWBOamKhZoGul\n1WJeaGKRALFwnSVxAaoojduhFglup0adW+MvrytdpG5tuI5MQfW1oMeBZH51sE06okHu27uZHGrl\nHk/l2BAJlNz/63Snpddv0jWQtJIpU7kC93z3hbKTwGqFiiYAIUSTEOLHQohXjP9fKxE58d6gEOKk\nEOIzlXxnLZLI5DibynHdlggfujJm+QIuXdeEAFobZi76MZVwQOfWS5UmuFnU5Iv7d1Y0CMUiAS5e\nVW/pspeja1PsQwC1stYdE8lkXQNJjg0leMclbVVLaBGa4MxYxirFCZDKSnSnxr4tURKZQskO6o5o\nkHv2XoDDIaj3OPnyr47PKJZXDslszpIFef3G5rJ+K5dTQwO6R9I4gANHh6yaBV6XwF1GJbCFJBzQ\n+dpdu9m/qw2BysI+czZFTsIbNjUznMywqsHL4y9MXwhmOobHM2SM+XE8k18QjfziYIehRJZrL4iU\n9PuHAzrXb22lz5jgBK8tFASwYpqiMouFSncA9wBPSSk3AE8Zz2fi48AvK/y+miUvVfLLx773oqXm\n+ewJVVC952xpCT8m21Y3ENAdrGv2Ue91VZAFqTBrH/SdTZGRc4uQTcX0Ibg1tbJO5yeX/GsNeqqm\nZWLGejvEhFSCSyiJilcHEzz5+146Wvwln19/PM3jL/SQzRQYSuZwziKWVyohv8721U3W4FFO0aGQ\nX2dNUx2GiwOXQ0U8AWxta0ATgtVNc0e0nCu6+uL864FunCjdq4xUxdaf6hwgnclPkmAuhd2xZj77\n7h20N3nxuR287ZK2uf+oBDSUrESd21HWbml1yIfDkGte3eTlJcMk5Ncn8h/ufuP8fUTVptIJ4Abg\nK8bjrwA3TvcmIcQlQAT4jwq/r+YIB3Tuf8tmfEZCTEZCxO8mlcuTNHbGTsobREwZ5xX13pLCF0th\n39Yo9+7rwKnBX76l9C05KM2Sm7e3siESsIqr+txKo8XjVGqG1ZCyNdm2ugGHgNNnU+SZCHU1C7mU\nI1IXDuj86VUxdN2Bz63xqbdvr9gEZH7ms8dHypb0MKWSJRhZ1xM1gH99ZJDxTAFRRh2Gc4FZkMfk\n2JDaiaXzyl9x/dbWsgbIBp+LUyPJkkKnS2F3rJlH7ryM735oD9/54OVlK862h3x857lujg8lOWUE\nfIwbciEwt8xFLVNpKltEStlrPD6NGuQnIYTQgE8C7wHeVOH31SSxSICQz013JkWTz4XLqZHKqWSp\noMfN4BxCZNOxO9ZsrfwXanVx55XruXBlfdnROmZlsWhQJ2esPhMZyXVbIhwfTPBSb3xeOjQLxdN/\n6J8UgmgWAc8UIJWT3LKjPCnh3bFmvvn+y4HKZCqmfuZX7thV9ueZ5SqLMZMLzYL3Y+n8a/7ufGHa\n502BNA21+5JG5I23TJlkUL/Vv9xxGbFIYMH6/nwj1AbH0pyJpzArlWZRq38zW1wA33n+JFdvLs2s\nVGvMubwUQvxECHFomn83FL9PSmkuUqbyIeAJKeWcYQpCiPcLIQ4KIQ729/eX3IhqMziWZjChbsYh\noxShW4NI0EvA48Tnml8qezigL3inmq/oWnuTl9OjaetG39Lq5+muQS5fH1IJMVWMAjJLJZqW8EJh\nckx6KQlvU+mIBhc8tG8+n9cRDfLgzdvQNfC5JkecmMqxjec5/6IYs8A5KGkEl0MlRJpO0nKqxBWz\nO1aen+Rc0RENcs3mFdbzPbEmxtIFK68mWu+ZVWa81plzByClnHHVLoToE0JEpZS9QogocGaat+0G\nXi+E+BDgB9xCiDEp5Wv8BVLKh4CHAHbu3FndfW25GPemW6jOnynAiaEkQsAF0eCidRKZ6E6VBdng\ndRJP5jjUMwbAk4dOA+c/CcykP57ma88cx+sSaJognytQFLK9JMLcRpNKb3/Pukae7hrCCexob7Cc\nym/ZUl1FybdubeUXf+hncCzF1M2Iw1EdB/VC0R9PTyqb+nSXCp7IoXxNPWdTHO0fX7Z5AI8DtxmP\nbwO+P/UNUsr9UsrVUsp24KPAv0w3+C9mOqJB/sd1W/A4lcSCWRO10efC53Zw/3Xla5nXEoNjaU6d\nVbbPkWSOPFhSCeNpVSGsWpj2dU3AjdtWsr5lssN8ZWP5ZT5rDTOP47kTw3icSmHzwLERfEYuRsBT\nvR1AZ+8oH/jaQU6NpADlEzJrMngc0FSBjk+t0Bzw0OR1WqtlcyNm1hWejwx6rVDpBPAAcI0Q4hWU\nff8BACHETiHEP1V6couF/niabz93EinV9tcMfBhKZCnIyhOJqk3IrxMLB/DpAo9TY0+sifZQHVta\n/Qwlc0SDc0stn0tGElnGM5JHDnSzZoq5p2ekvAisWsXp1EhkJPUet1XbIJGRNHmdVckCNgn5ddoa\nfUhUQZdUbqIKXi6vQo7LqZVba5hyI0PJnKWCqwZ+telfWe9d1BNcRROAlHJQSvlGKeUGKeWbpJRD\nxvGDUso7p3n/l6WUf1rJd9YyQgiCHgcFJoTTWhdxjLCJKUnwybdtp9Hn4umuIQ71xC0zUCjgmeMT\nzi2nhicShcyKUtGgTlDX5i0lXEt0RIPcebkqH9g3lrEckgAjqVxVJzi1A9sAqIXP/l1tVp3hPEo4\nsJoT1EJww45VVuSVSTKrgg36ZykruRhYCibSqmNWk0rlJKOGAbrJryNZ/B2kmAeefJne0TROVPap\nQN3wX3rvzqqauG7YscqSHTBJZvOMpgs0eBe/CQJg0wplY3ZrsCni5+btrQQ9Djw1pERZAB577iQC\nFaEkgQ9eWZmOTy0wOJYmmVWzrltTeQ6gCs6vDVUvB2MhsCeABaIvrmKf63QNj0NY8fJXrA8tWgdR\nMQePDlmidzmUqUsCj/6mu+omlnBA51PvuJhNLXWq2IpDmSMifnfFQmK1gulkzxSUU/ix53sYTeVJ\n5+ZXCvJcnBtAMicZKjqfSpQ8awnjdsbh0Ni7ZQVuTZmCLmwNLur+ZU8AC0B/PM2vjILi4+kCqbwk\nnVeOgJ8fHli0OiHFrA3X4ZkmZqwWpLD642ke+r+vkitIQ5dGEKpzE89kuf/xQwsi5VBtGuvclvOx\n34j/D3g0/vbG2auKnS8cAsspPZTM0eRz0RGZXynNWsRhKJoWCgUe/tUxIkEPbgf88PenF/X9bU8A\nC4CZrVnMiFEt6MLWxR8CChN1D6byZ1fFqj4AhQM673/9OgIeF61BnXRe0hdPoQmNT1y/perntxAM\nj2esHIxrDUmJRLpQUq3dc00sEqAloCuntM+FrqkAiFRucUVyz4RZsF4DXE4HV25sZjCRoa3Rx2dv\n3bGod/j2BLBA7NkYxufWJjmLzqby3HHF2kW9RTQZHEszms7icTKpaHp/Dfg3+uNpPvOzLg73jTKc\nzCCBQl6SyxfOe5nKc0VjnZs63UFrvYcfHurDIZTgWjWTwEzCAZ2Hb9/FXXvalY5Pcx0eB5wcSlTd\nPLgQhAM6n3zHxWxoqSOoO3mqc4BEpsCxClRKawV7AlggQn6dBo+LoWQO3SFo8rmqrNCysIT8Opta\ngjT6dMbSBQSwvS3I3ddsmvNvzzWmamMyK8kYy+S0IQNRrUpZ5wIhJMOJFB6XxtZVQZ7qHOCKdaGq\nLzD642kefLKTn3ae4a/+7fccHxxHSuiIBhZ9DkYxJ0eSDCfSCFTdg44Vi7999gSwQHT1xekfS6si\n0UIylMgiqF6G7EITDujcccVaThfVB/5d9ygffuTZqpsgAK7eHGF9s29SVbabt7dWLCVcK4T8OhG/\nh2QWktkCz3ePVq0OwFTCAZ0r1oU4MpDgcN8YqbyagN/UsTj1caZjeDxDKqME4FyGiUvUhAesMuwJ\nYIHYHWvmw2+IMZLKY9SzYFOJdXcXCw1Fu5qMVFnPt19eGyYu5QdYD6idCcDvukdqYnJaCAbH0owk\nsrQWhbtuWFF6YZNzzZ6NYbwuMckEGq33VvGMFpbdsWb+9qaLcDkceN2qZOjDd+yqib5fCfYEsICY\nsdpSglMTfKSCWrK1ToPXSXvIV1NRHttWNxAN6lZUxpmxpZOD0REN8okbL2IkqdrjEPDhN2yoqf4l\nhFDSIKiBZT4ifLXM1ZsjtAR03nhBC984WP3w54WgUjlomyJMc09Wwv5LV5Wl+74YKFb8PJvMMZZW\nWai1MAj1x9Pc850XLIlkgI9cFVvUERpTWRuuQ3e52LslzOYVwZrqX6o+8IRE8n37OpZE9FUxP32p\njyMDCY4MJKii/NWCYu8AFpBYJMCmiB+vEx5/oXdRxwdPx861TVzc1sD2tqBSBvW5a8bEFQ7ovPWi\niQFx/6427rxyfRXPaOEJ+XVa/G4ee76Hv/tRZ03lN8QiAba0BnACDg0uXFlf7VNacLatbkA3xE1b\n6z010/crwZ4AFphmv05rg49kJlf1DM2FJhzQeeDmi3Bq6i74+PW1pYNu1gXwujT+eHd7dU/mHBAO\n6LzdKJHo0iovVbmQhAM6H37DBoQmuOfNS2/1D8oM9+l37qDOrdHasDT8G7YJaAEJB3Q+9tbNPPhk\nJx+9duneBB+/cQsf+96hmrL/gzKR1HudPHDT1iVl+ilmz8Ywvp8eZmNLfc04gE3MHeINO1ZV+1TO\nGfu2RlkbVvo/tbT4mS9CytqMVt+5c6c8ePBgtU9jXvTH00uic8xGrbaxs3d0yQ7+Jp29ozU7ANVq\nv1hOCCGelVLuLOW99g7gHLAcboBabeNSH/yhtttYq/3CZnpsH4CNjY3NMsWeAGxsbGyWKfYEYGNj\nY7NMsScAGxsbm2WKPQHY2NjYLFPsCcDGxsZmmWJPADY2NjbLlJpNBBNC9APHK/iIZqB2xFLOD8ut\nzcutvWC3eblQSZvXSClLKhRRsxNApQghDpaaDbdUWG5tXm7tBbvNy4Xz1WbbBGRjY2OzTLEnABsb\nG5tlylKeAB6q9glUgeXW5uXWXrDbvFw4L21esj4AGxsbG5vZWZFcnBMAAASiSURBVMo7ABsbGxub\nWbAnABsbG5tlypKbAIQQe4UQh4UQXUKIe6p9PuUihGgTQvxMCPGSEOL3QoiPGMebhBA/FkK8Yvzf\naBwXQoh/NNr7ghBiR9Fn3Wa8/xUhxG1Fxy8RQrxo/M0/CiGqXuJaCOEQQjwvhPiB8XytEOIZ4xy/\nIYRwG8d143mX8Xp70Wfcaxw/LIR4c9HxmusTQogGIcS3hRCdQoiXhRC7l8E1/q9Gnz4khHhUCOFZ\natdZCPHPQogzQohDRcfO+XWd6TvmREq5ZP4BDuAIsA5wA78DNlf7vMpsQxTYYTwOAH8ANgP/E7jH\nOH4P8KDx+Drgh4AAXgc8YxxvAl41/m80Hjcarx0w3iuMv91XA+3+c+DrwA+M598E3mU8/gLwQePx\nh4AvGI/fBXzDeLzZuN46sNboB45a7RPAV4A7jcduoGEpX2NgJXAU8BZd3z9ZatcZ+C/ADuBQ0bFz\nfl1n+o45z7faN8IC//i7gR8VPb8XuLfa51Vhm74PXAMcBqLGsShw2Hj8ReDWovcfNl6/Ffhi0fEv\nGseiQGfR8Unvq1IbVwFPAVcDPzA69wDgnHpdgR8Bu43HTuN9Yuq1Nt9Xi30CqDcGQzHl+FK+xiuB\nbmNQcxrX+c1L8ToD7UyeAM75dZ3pO+b6t9RMQGYnMzlpHFuUGNve7cAzQERK2Wu8dBqIGI9navNs\nx09Oc7yafBr4b0DBeB4CRqSUOeN58Tla7TJeP2u8v9zfoZqsBfqBhw2z1z8JIepYwtdYSnkK+Afg\nBNCLum7PsrSvs8n5uK4zfcesLLUJYMkghPAD3wHullKOFr8m1TS/JOJ3hRBvBc5IKZ+t9rmcR5wo\nM8HnpZTbgXHUtt1iKV1jAMMmfQNq8msF6oC9VT2pKnA+rms537HUJoBTQFvR81XGsUWFEMKFGvwf\nkVI+ZhzuE0JEjdejwBnj+Extnu34qmmOV4srgOuFEMeAf0WZgf4X0CCEcBrvKT5Hq13G6/XAIOX/\nDtXkJHBSSvmM8fzbqAlhqV5jgDcBR6WU/VLKLPAY6tov5etscj6u60zfMStLbQL4DbDBiCxwo5xH\nj1f5nMrC8Or/H+BlKeWnil56HDCjAW5D+QbM4+81IgpeB5w1toI/Aq4VQjQaq69rUTbSXmBUCPE6\n47veW/RZ5x0p5b1SylVSynbU9fqplHI/8DPgFuNtU9tr/g63GO+XxvF3GdEja4ENKIdZzfUJKeVp\noFsIsck49EbgJZboNTY4AbxOCOEzzsls85K9zkWcj+s603fMTrWcQufQAXMdKnLmCHBftc9nHue/\nB7V9ewH4rfHvOpT98yngFeAnQJPxfgF81mjvi8DOos+6A+gy/t1edHwncMj4m88wxRlZxba/gYko\noHWoG7sL+BagG8c9xvMu4/V1RX9/n9GmwxRFvdRinwAuBg4a1/l7qGiPJX2Ngb8GOo3z+ioqkmdJ\nXWfgUZSPI4va6b3vfFzXmb5jrn+2FISNjY3NMmWpmYBsbGxsbErEngBsbGxslin2BGBjY2OzTLEn\nABsbG5tlij0B2NjY2CxT7AnAxsbGZpliTwA2NjY2y5T/DxbkytUET5WGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(vals.size(0)), vals_normed.data.tolist(), s = 0.1)\n",
    "# plt.scatter(range(vals.size(0)), vals_normed.data.tolist(), s = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 809
    },
    "colab_type": "code",
    "id": "5S2usmp1E2_L",
    "outputId": "1935866b-8ee6-4ac6-bb25-dd073102f4d3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aPtD7jwZX3Xo/m3Yd4HBfPxM62lg4ewqrlp/vUDezMaXlWy4btvWwadcBDvX1E8Chvn42\n7TrAhm09zS7NzGxEtXygb957kMN9/ceMHe7rZ8veg02qyMysOVo+0F8/YzITOtqOGZvQ0caCGZOb\nVJGZWXO0fKAvOnsaC2dPQf19EEeZmPfQF509rdmlmZmNqJYP9LZxYtXy8+l89JtM2f19/mrZuT4g\namZjUsuf5QJZqE88sIOJB3Zw0fzpzS7HzKwpWn4P3czMMg50M7NEONDNzBKRRA+9Gl8SwMzGkmQD\n3ZcEMLOxpqaWi6QlkrZJ2i7p+grLPyRpi6SHJa2XdFbxpdbHlwQws7FmyECX1AasBJYCC4BlkhaU\nTXsQ6IqIc4CvAf+16ELr5UsCmNlYU8se+nnA9ojYERF9wJ3AFaUTIuLeiDiUP70PmFVsmfXzJQHM\nbKypJdBnArtKnu/Ox6pZDnyr0gJJKyR1S+ru7e2tvcph8CUBzGysKfS0RUm/A3QB/63S8oi4JSK6\nIqKrs7OzyI8+ji8JYGZjTS1nuewBZpc8n5WPHUPSxcCfAL8SES8WU96J8SUBzGwsqSXQNwLzJM0l\nC/IrgXeXTpB0LvC3wJKIGPWnkfj8dDNL0ZCBHhFHJF0HrAPagNsiYrOkm4DuiFhD1mKZBNwlCeDn\nEXF5A+seNp+fbmapqukXiyJiLbC2bOzGkscXF1xXw5Senw7Hnp/utoyZtbIxdy0Xn59uZqkac4Hu\n89PNLFVjLtAHOz+9/2iwfut+vrD+UdZv3U//0Wh2uWZmNUv24lzVDJyf/pbfXE7fKdP43A1/+NIv\nG/lgqZm1sjG3hw4vn58+Zc99XDR/Om3j5It5mVnLG5OBXslgB0vdijGzVjDmWi7VDBwsPVQS6hM6\n2njdL53qVoyZtQTvoeeqHSxFuBVjZi3BgZ6rdjGvrfuecyvGzFqCWy4lKl3My60YM2sV3kMfglsx\nZtYqHOhDcCvGzFqFWy41cCvGzFqB99CHabitmGp7796rN7MT5T30Yap2CYGV926v2opZdPa0invv\nt19zHu/9ux96r97MToj30E9ApUsIDHY1x2qXF/jr7zzqA6xmdsIc6AUb7GqO1S4v0L3zmboPsLpF\nY2bl3HIpWLVWTOnee/mB1K6zXnHMXZQGxqsdYB1Oi8b3UTVLnwO9ASqdFQMv773/4Kf7iHHtTDz5\nJBbOnsJ1i+fRvfOZ48ZLD7BC5RZN6fiGbT0sOnvaccENvjSw2VjgQB9Bg+2913OAtVqL5pE9z3Lr\n9352XHBfc+GcujYADnmz1uRAH2HV9t7rOde9Woum/2hUDO67H95X1wZg1fLzASoGfbXWjVs6Zs3n\nQB/F6m3RjBunisENVNwwVNsAfGfrfv7u3x6vuXfvnr7Z6OBAH8XqbdFs2NZTMbgvfeOZ9Dz3Ys0b\ngLt/vK+u3r17+majgwN9lKunRVNtj37x/Oksnj+95g0AUFfvfiR6+m71mA3NgZ6QwfbogZo3AJee\ncyb3bNlfc+++0T394bR6oJhjAM0ab7TRVo8Vw4GemGp79NXmVtoAAHX17hvd06+31VPUMYBmjTd6\ngwSVW15F1pOCVlw3B/oYV20DUE/vvtE9/XpbPUUdA2jWeKM3SNVaXkXVM9gGoJp6w7PIsK1nozfa\nD/Q70K2ienr31caL6unX2+qBYo4BNGu80Rukai2vouqptgGoFvRQPTyLmD8QqvUEd73HeYZbU9Ec\n6NYwRfX06231FHUMoFnj0NgN0sDnNKqeahuAakFfLTyLmj8QqvUEd73HeYZTUyNC3RfnsoaqdEXK\nweZWujtUR/u4usYXv256xQukXbd4XkuMX3rOmRWv2Nl11isKGb/0jWc2tB6ovAEoDfrSq4pWC8+i\n5m/Y1lP1SqfV3qt0XUrXrfQ4z4nW1AgOdBtVqm0A6hkvasPQrPFGb5AWz5/e0HqqbQCgctCXLm/E\n/C17D1a90mm196q20RvqQH89NTVCTYEuaYmkbZK2S7q+wvKTJX01X36/pDlFF2pWjyI2DM0ab/QG\naeAzGlVPtQ1AtaCvFp5FzV8wY3LV+xRUe69qG703zjytsJoaYcgeuqQ2YCXwdmA3sFHSmojYUjJt\nOfBMRLxG0pXAZ4F3NaLgcj947CkADh7+xTHPB4y28dFYk8dH5/iRfdsYxzYmdvwxP/zZ04WPN7Ke\naxe9hoe//jf0T5rOtR9YkV09FJg79RQ2//xJaGvn5JPamTv1FCZ2tDd0/vj2trrfa2A9ytdtfHtb\nITUNHEgtmiIGvzGCpLcAn4yI/5g//xhARPx5yZx1+ZwfSGoHngA6Y5A3P/2s+fH2j99Wd8GbHtoE\nwMI3LQTg4AvZl+/RLY8AMG/BG46ZP9rGR2NNHvf4SI1HBD/dvgPaOpgx40wmndyGpBGZX+97NbKm\n0yZ0MFyrP/DWByKiq9KyWgL9HcCSiHhf/vwq4PyIuK5kziP5nN3588fyOU+WvdcKYAXApDNf/R8u\n+cSqYa/UgIFANzNrFZPHnzTs1w4W6CN62mJE3ALcAtDV1RVf/d23jOTHm5m1vNUfqL6sloOie4DZ\nJc9n5WMV5+Qtl9OA45vHZmbWMLUE+kZgnqS5kjqAK4E1ZXPWAFfnj98BfGew/rmZmRVvyJZLRByR\ndB2wDmgDbouIzZJuArojYg1wK7BK0nbgabLQNzOzEVRTDz0i1gJry8ZuLHn8AvCfii3NzMzq4d8U\nNTNLhAPdzCwRDnQzs0Q40M3MEuFANzNLxJC/+t+wD5Z6gZ3DfPlU4MkhZ6VlrK2z1zdtY219obh1\nPisiOistaFqgnwhJ3dWuZZCqsbbOXt+0jbX1hZFZZ7dczMwS4UA3M0tEqwb6Lc0uoAnG2jp7fdM2\n1tYXRmCdW7KHbmZmx2vVPXQzMyvjQDczS0TLBbqkJZK2Sdou6fpm11M0SbdJ6slv6zcwdrqkb0t6\nNP/7Fc2ssUiSZku6V9IWSZslfTAfT3mdx0v6oaSH8nX+VD4+V9L9+Xf7q/n9B5IhqU3Sg5L+OX+e\n7PpKelzSjyVtktSdjzX8O91SgS6pDVgJLAUWAMskLWhuVYW7HVhSNnY9sD4i5gHr8+epOAJ8OCIW\nABcA1+b/TVNe5xeBxRHxJmAhsETSBcBngc9HxGuAZ4DlTayxET4IbC15nvr6/mpELCw597zh3+mW\nCnTgPGB7ROyIiD7gTuCKJtdUqIj4LtlNQkpdAXwpf/wl4DdGtKgGioh9EfGj/PFzZP/DzyTtdY6I\neD5/elL+J4DFwNfy8aTWWdIs4FLgi/lzkfD6VtHw73SrBfpMYFfJ8935WOqmR8S+/PETwPRmFtMo\nkuYA5wL3k/g65+2HTUAP8G3gMeBARBzJp6T23f4fwEeAo/nzM0h7fQO4R9IDklbkYw3/Ttd0xyIb\nPSIiJCV3rqmkScA/An8QEQezHbhMiuscEf3AQklTgK8Dr2tySQ0j6TKgJyIekLSo2fWMkLdFxB5J\n04BvS/pJ6cJGfadbbQ99DzC75PmsfCx1+yWdCZD/3dPkegol6SSyMP+HiPg/+XDS6zwgIg4A9wJv\nAaZIGtjJSum7fSFwuaTHydqki4G/JN31JSL25H/3kG2wz2MEvtOtFugbgXn50fEOsptRr2lyTSNh\nDXB1/vhq4J+aWEuh8l7qrcDWiLi5ZFHK69yZ75kjaQLwdrJjB/cC78inJbPOEfGxiJgVEXPI/p/9\nTkT8Nomur6RTJJ068Bj4NeARRuA73XK/KSrpErJ+XBtwW0R8usklFUrSHcAisktt7gc+AXwDWA28\nkuySw++MiPIDpy1J0tuA/wf8mJf7qx8n66Onus7nkB0UayPbqVodETdJehXZHuzpwIPA70TEi82r\ntHh5y+WPIuKyVNc3X6+v50/bga9ExKclnUGDv9MtF+hmZlZZq7VczMysCge6mVkiHOhmZolwoJuZ\nJcKBbmaWCAe6mVkiHOhmZon4/1/X7/sdLOP+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vals_normed = np.array(vals_normed)\n",
    "plot_acf(vals_normed)\n",
    "plot_pacf(vals_normed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "VTPMlpZ3hdCY",
    "outputId": "53abb70f-7fbd-4dbc-d948-57a6914a21e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time:  0.08602981199999249\n"
     ]
    }
   ],
   "source": [
    "import timeit\n",
    "\n",
    "start = timeit.default_timer()\n",
    "\n",
    "#Your statements here\n",
    "\n",
    "N = 1000\n",
    "cumsum, moving_aves = [0], []\n",
    "\n",
    "for i, x in enumerate(vals_normed, 1):\n",
    "    cumsum.append(cumsum[i-1] + float(x))\n",
    "    if i>=N:\n",
    "        moving_ave = (cumsum[i] - cumsum[i-N])/N\n",
    "        #can do stuff with moving_ave here\n",
    "        moving_aves.append(moving_ave)\n",
    "\n",
    "\n",
    "total_mvs = list(np.zeros(N-1))\n",
    "total_mvs.extend(moving_aves)\n",
    "stop = timeit.default_timer()\n",
    "# max_dct = max(dct[100:])\n",
    "\n",
    "print('Time: ', stop - start)  \n",
    "# dct = (dct/max_dct + 1)/2 - 0.5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "vNZ66GdOfLKY",
    "outputId": "68032ec8-6842-4ece-c66a-27b60e1b892a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99999\n"
     ]
    }
   ],
   "source": [
    "idct = total_mvs\n",
    "print (len(idct))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "x1kPQ6zpfudn",
    "outputId": "76848b9c-2fe9-4d24-c646-b673b2f16a50"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sm4vXAQSX90qAoIDpNw4rgGHbRFSJwa7witQVfYxMFvmzbx/GQVDExgv1tpgB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\nZV/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VN2JznduyPQrmXnFsW92A16ls9H3xNKdHk6okYD7P4b6xeTkhO6JBHrh5G5omrJV/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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(range(vals.size(0)), vals_normed.data.tolist(), s = 0.1)\n",
    "plt.show()\n",
    "# plt.scatter(range(len(actual_values)), actual_values, s = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "colab_type": "code",
    "id": "WffcjVasTlAZ",
    "outputId": "fd51037b-a995-4368-8671-66a87539ca56"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aPtD7jwZX3Xo/m3Yd4HBfPxM62lg4ewqrlp/vUDezMaXlWy4btvWwadcBDvX1E8Chvn42\n7TrAhm09zS7NzGxEtXygb957kMN9/ceMHe7rZ8veg02qyMysOVo+0F8/YzITOtqOGZvQ0caCGZOb\nVJGZWXO0fKAvOnsaC2dPQf19EEeZmPfQF509rdmlmZmNqJYP9LZxYtXy8+l89JtM2f19/mrZuT4g\namZjUsuf5QJZqE88sIOJB3Zw0fzpzS7HzKwpWn4P3czMMg50M7NEONDNzBKRRA+9Gl8SwMzGkmQD\n3ZcEMLOxpqaWi6QlkrZJ2i7p+grLPyRpi6SHJa2XdFbxpdbHlwQws7FmyECX1AasBJYCC4BlkhaU\nTXsQ6IqIc4CvAf+16ELr5UsCmNlYU8se+nnA9ojYERF9wJ3AFaUTIuLeiDiUP70PmFVsmfXzJQHM\nbKypJdBnArtKnu/Ox6pZDnyr0gJJKyR1S+ru7e2tvcph8CUBzGysKfS0RUm/A3QB/63S8oi4JSK6\nIqKrs7OzyI8+ji8JYGZjTS1nuewBZpc8n5WPHUPSxcCfAL8SES8WU96J8SUBzGwsqSXQNwLzJM0l\nC/IrgXeXTpB0LvC3wJKIGPWnkfj8dDNL0ZCBHhFHJF0HrAPagNsiYrOkm4DuiFhD1mKZBNwlCeDn\nEXF5A+seNp+fbmapqukXiyJiLbC2bOzGkscXF1xXw5Senw7Hnp/utoyZtbIxdy0Xn59uZqkac4Hu\n89PNLFVjLtAHOz+9/2iwfut+vrD+UdZv3U//0Wh2uWZmNUv24lzVDJyf/pbfXE7fKdP43A1/+NIv\nG/lgqZm1sjG3hw4vn58+Zc99XDR/Om3j5It5mVnLG5OBXslgB0vdijGzVjDmWi7VDBwsPVQS6hM6\n2njdL53qVoyZtQTvoeeqHSxFuBVjZi3BgZ6rdjGvrfuecyvGzFqCWy4lKl3My60YM2sV3kMfglsx\nZtYqHOhDcCvGzFqFWy41cCvGzFqB99CHabitmGp7796rN7MT5T30Yap2CYGV926v2opZdPa0invv\nt19zHu/9ux96r97MToj30E9ApUsIDHY1x2qXF/jr7zzqA6xmdsIc6AUb7GqO1S4v0L3zmboPsLpF\nY2bl3HIpWLVWTOnee/mB1K6zXnHMXZQGxqsdYB1Oi8b3UTVLnwO9ASqdFQMv773/4Kf7iHHtTDz5\nJBbOnsJ1i+fRvfOZ48ZLD7BC5RZN6fiGbT0sOnvaccENvjSw2VjgQB9Bg+2913OAtVqL5pE9z3Lr\n9352XHBfc+GcujYADnmz1uRAH2HV9t7rOde9Woum/2hUDO67H95X1wZg1fLzASoGfbXWjVs6Zs3n\nQB/F6m3RjBunisENVNwwVNsAfGfrfv7u3x6vuXfvnr7Z6OBAH8XqbdFs2NZTMbgvfeOZ9Dz3Ys0b\ngLt/vK+u3r17+majgwN9lKunRVNtj37x/Oksnj+95g0AUFfvfiR6+m71mA3NgZ6QwfbogZo3AJee\ncyb3bNlfc+++0T394bR6oJhjAM0ab7TRVo8Vw4GemGp79NXmVtoAAHX17hvd06+31VPUMYBmjTd6\ngwSVW15F1pOCVlw3B/oYV20DUE/vvtE9/XpbPUUdA2jWeKM3SNVaXkXVM9gGoJp6w7PIsK1nozfa\nD/Q70K2ienr31caL6unX2+qBYo4BNGu80Rukai2vouqptgGoFvRQPTyLmD8QqvUEd73HeYZbU9Ec\n6NYwRfX06231FHUMoFnj0NgN0sDnNKqeahuAakFfLTyLmj8QqvUEd73HeYZTUyNC3RfnsoaqdEXK\nweZWujtUR/u4usYXv256xQukXbd4XkuMX3rOmRWv2Nl11isKGb/0jWc2tB6ovAEoDfrSq4pWC8+i\n5m/Y1lP1SqfV3qt0XUrXrfQ4z4nW1AgOdBtVqm0A6hkvasPQrPFGb5AWz5/e0HqqbQCgctCXLm/E\n/C17D1a90mm196q20RvqQH89NTVCTYEuaYmkbZK2S7q+wvKTJX01X36/pDlFF2pWjyI2DM0ab/QG\naeAzGlVPtQ1AtaCvFp5FzV8wY3LV+xRUe69qG703zjytsJoaYcgeuqQ2YCXwdmA3sFHSmojYUjJt\nOfBMRLxG0pXAZ4F3NaLgcj947CkADh7+xTHPB4y28dFYk8dH5/iRfdsYxzYmdvwxP/zZ04WPN7Ke\naxe9hoe//jf0T5rOtR9YkV09FJg79RQ2//xJaGvn5JPamTv1FCZ2tDd0/vj2trrfa2A9ytdtfHtb\nITUNHEgtmiIGvzGCpLcAn4yI/5g//xhARPx5yZx1+ZwfSGoHngA6Y5A3P/2s+fH2j99Wd8GbHtoE\nwMI3LQTg4AvZl+/RLY8AMG/BG46ZP9rGR2NNHvf4SI1HBD/dvgPaOpgx40wmndyGpBGZX+97NbKm\n0yZ0MFyrP/DWByKiq9KyWgL9HcCSiHhf/vwq4PyIuK5kziP5nN3588fyOU+WvdcKYAXApDNf/R8u\n+cSqYa/UgIFANzNrFZPHnzTs1w4W6CN62mJE3ALcAtDV1RVf/d23jOTHm5m1vNUfqL6sloOie4DZ\nJc9n5WMV5+Qtl9OA45vHZmbWMLUE+kZgnqS5kjqAK4E1ZXPWAFfnj98BfGew/rmZmRVvyJZLRByR\ndB2wDmgDbouIzZJuArojYg1wK7BK0nbgabLQNzOzEVRTDz0i1gJry8ZuLHn8AvCfii3NzMzq4d8U\nNTNLhAPdzCwRDnQzs0Q40M3MEuFANzNLxJC/+t+wD5Z6gZ3DfPlU4MkhZ6VlrK2z1zdtY219obh1\nPisiOistaFqgnwhJ3dWuZZCqsbbOXt+0jbX1hZFZZ7dczMwS4UA3M0tEqwb6Lc0uoAnG2jp7fdM2\n1tYXRmCdW7KHbmZmx2vVPXQzMyvjQDczS0TLBbqkJZK2Sdou6fpm11M0SbdJ6slv6zcwdrqkb0t6\nNP/7Fc2ssUiSZku6V9IWSZslfTAfT3mdx0v6oaSH8nX+VD4+V9L9+Xf7q/n9B5IhqU3Sg5L+OX+e\n7PpKelzSjyVtktSdjzX8O91SgS6pDVgJLAUWAMskLWhuVYW7HVhSNnY9sD4i5gHr8+epOAJ8OCIW\nABcA1+b/TVNe5xeBxRHxJmAhsETSBcBngc9HxGuAZ4DlTayxET4IbC15nvr6/mpELCw597zh3+mW\nCnTgPGB7ROyIiD7gTuCKJtdUqIj4LtlNQkpdAXwpf/wl4DdGtKgGioh9EfGj/PFzZP/DzyTtdY6I\neD5/elL+J4DFwNfy8aTWWdIs4FLgi/lzkfD6VtHw73SrBfpMYFfJ8935WOqmR8S+/PETwPRmFtMo\nkuYA5wL3k/g65+2HTUAP8G3gMeBARBzJp6T23f4fwEeAo/nzM0h7fQO4R9IDklbkYw3/Ttd0xyIb\nPSIiJCV3rqmkScA/An8QEQezHbhMiuscEf3AQklTgK8Dr2tySQ0j6TKgJyIekLSo2fWMkLdFxB5J\n04BvS/pJ6cJGfadbbQ99DzC75PmsfCx1+yWdCZD/3dPkegol6SSyMP+HiPg/+XDS6zwgIg4A9wJv\nAaZIGtjJSum7fSFwuaTHydqki4G/JN31JSL25H/3kG2wz2MEvtOtFugbgXn50fEOsptRr2lyTSNh\nDXB1/vhq4J+aWEuh8l7qrcDWiLi5ZFHK69yZ75kjaQLwdrJjB/cC78inJbPOEfGxiJgVEXPI/p/9\nTkT8Nomur6RTJJ068Bj4NeARRuA73XK/KSrpErJ+XBtwW0R8usklFUrSHcAisktt7gc+AXwDWA28\nkuySw++MiPIDpy1J0tuA/wf8mJf7qx8n66Onus7nkB0UayPbqVodETdJehXZHuzpwIPA70TEi82r\ntHh5y+WPIuKyVNc3X6+v50/bga9ExKclnUGDv9MtF+hmZlZZq7VczMysCge6mVkiHOhmZolwoJuZ\nJcKBbmaWCAe6mVkiHOhmZon4/1/X7/sdLOP+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plot_acf(np.array(vals_normed))\n",
    "plt.show()\n",
    "\n",
    "plot_pacf(np.array(vals_normed))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ydj8OBuKThiN"
   },
   "outputs": [],
   "source": [
    "model = arima_model.ARMA(vals_normed, (1,1)).fit( method='mle', trend='nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "colab_type": "code",
    "id": "NdYgpDOQTfvS",
    "outputId": "83f5e206-2569-4e26-a0bf-cf7c61f50d64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.01198022 0.01784948 ... 0.07910571 0.08138979 0.08185932]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0fc4865470>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJmqPM+E705CF4zX3jWRdQwTQGNBp8JXW8pwFd/5hetryTq/W48llHIimyORt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\nXeYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arma = model.predict(start = 0, end = len(vals_normed)-1)\n",
    "print (arma)\n",
    "loss_fct = RMSELoss()\n",
    "# arma = torch.Tensor(arma)\n",
    "plt.scatter(range(len(arma)),vals_normed, s = 0.1, label = 'real data')\n",
    "plt.scatter(range(len(arma)), arma, s = 0.1, label = 'ARMA(15,1)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0crJnovgZPUq"
   },
   "outputs": [],
   "source": [
    "# Rescaling the data and separating it in train/test/valid datasets\n",
    "  \n",
    "\n",
    "test_proportion = 0.2\n",
    "validation_proportion = 0.2\n",
    "\n",
    "val_break = round(len(vals)*(1-(test_proportion + validation_proportion)))\n",
    "test_break = round(len(vals)*(1-test_proportion))\n",
    "\n",
    "\n",
    "values = vals[0 : val_break]\n",
    "values_valid = vals[val_break : test_break]\n",
    "values_test = vals[test_break:(len(vals))]\n",
    "      \n",
    "      \n",
    "      \n",
    "values = torch.Tensor(values)\n",
    "values_test = torch.Tensor(values_test)\n",
    "values_valid = torch.Tensor(values_valid)\n",
    "\n",
    "\n",
    "\n",
    "max_vals = max(vals)\n",
    "\n",
    "values = values/max_vals\n",
    "values_test = values_test/max_vals\n",
    "values_valid = values_valid/max_vals\n",
    "\n",
    "\n",
    "loss_fct = RMSELoss()\n",
    "\n",
    "values_test = values_test.data.tolist()\n",
    "values = values.data.tolist()\n",
    "values_valid = values_valid.data.tolist()\n",
    "\n",
    "arma_train = arma[0:val_break]\n",
    "arma_valid = arma[val_break : test_break]\n",
    "arma_test = arma[test_break:(len(vals))]\n",
    "\n",
    "# arma_train = actual_values[0:val_break]\n",
    "# arma_valid = actual_values[val_break : test_break]\n",
    "# arma_test = actual_values[test_break:(len(vals))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iseAD9gzzVmH"
   },
   "outputs": [],
   "source": [
    "\n",
    "# gmm_range = (torch.Tensor(range(vals_normed.size(0))) - vals.size(0)/2)/vals.size(0)\n",
    "# gmm_data = np.array([[float(vals_normed[i]), float(gmm_range[i])] for i in range (gmm_range.size(0))])\n",
    "\n",
    "# gmm_batch_size = 800\n",
    "# gmm_interval = 50\n",
    "# number_gaussian_components = 4\n",
    "# Fisher_vector = []\n",
    "# with torch.no_grad():\n",
    "#   for i in range (0, len(gmm_data) - gmm_batch_size, gmm_interval):\n",
    "\n",
    "#     gmm = GaussianMixture(n_components = number_gaussian_components)\n",
    "#     gmm.fit(gmm_data[i : i+gmm_batch_size])\n",
    "#     u_k = np.zeros((4, 2))\n",
    "#     v_k = np.zeros((4, 2))\n",
    "#     cov_halfinvs = []\n",
    "\n",
    "#     # Calculating Sigma^{-1/2}\n",
    "#     for cov in gmm.covariances_:\n",
    "#       eigenval, eigenvec = scipy.linalg.eig(cov)\n",
    "#       inveigenval = [1/np.sqrt(float(i)) for i in eigenval]\n",
    "#       halfinv = np.dot(np.diag(inveigenval), np.transpose(eigenvec))\n",
    "#       cov_halfinvs.append(halfinv)\n",
    "#     current_fisher = []\n",
    "\n",
    "#     # Calculating u_k and v_k\n",
    "#     for component in range (number_gaussian_components):\n",
    "#       for j in range (i, i+gmm_batch_size):\n",
    "#         q_i = gmm.predict_proba(gmm_data[j].reshape(1,-1)).reshape(4)\n",
    "#         component_weight = gmm.weights_[component]\n",
    "\n",
    "#         u_k_term2 =  np.dot(cov_halfinvs[component] , \n",
    "#                             (gmm_data[j]-gmm.means_[component]).reshape(2))\n",
    "#         u_k[component] +=  (q_i[component] / np.sqrt(gmm_batch_size * component_weight)) * u_k_term2\n",
    "\n",
    "\n",
    "#         v_k_term2 = np.array([i**2 for i in u_k_term2]) - 1    \n",
    "#         v_k[component] +=  q_i[component] * v_k_term2\n",
    "\n",
    "#       current_fisher.extend(u_k[component].reshape(2))\n",
    "#       current_fisher.extend(v_k[component].reshape(2))\n",
    "#     Fisher_vector.append(current_fisher)\n",
    "#     if (i%1000 == 0):\n",
    "#       print (i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CRo6G7R4p167"
   },
   "outputs": [],
   "source": [
    "# The actual network\n",
    "class WaveNet(nn.Module):\n",
    "    def __init__(self, mu=1,n_residue=48, n_skip= 256, dilation_depth=10, \n",
    "                 n_repeat=5, nhpp = 50, nhpp_nbr = 0, additional_features = 0):\n",
    "        # n_residue: residue channels\n",
    "        # n_skip: skip channels\n",
    "        # dilation_depth & n_repeat: dilation layer setup\n",
    "        super(WaveNet, self).__init__()\n",
    "        self.nhpp_nbr = nhpp_nbr\n",
    "        self.dilation_depth = dilation_depth\n",
    "        dilations = self.dilations = [2**i for i in range(dilation_depth)] * n_repeat\n",
    "#         self.one_hot = One_Hot(mu)\n",
    "        self.from_input = nn.Conv1d(in_channels=mu, out_channels=n_residue, kernel_size=1)\n",
    "        self.conv_sigmoid = nn.ModuleList([nn.Conv1d(in_channels=n_residue, out_channels=n_residue, kernel_size=2, dilation=d)\n",
    "                         for d in dilations])\n",
    "        self.conv_tanh = nn.ModuleList([nn.Conv1d(in_channels=n_residue, out_channels=n_residue, kernel_size=2, dilation=d)\n",
    "                         for d in dilations])\n",
    "        self.skip_scale = nn.ModuleList([nn.Conv1d(in_channels=n_residue, out_channels=n_skip, kernel_size=1)\n",
    "                         for d in dilations]) \n",
    "        self.residue_scale = nn.ModuleList([nn.Conv1d(in_channels=n_residue, out_channels=n_residue, kernel_size=1)\n",
    "                         for d in dilations])\n",
    "        self.conv_post_1 = nn.Conv1d(in_channels=n_skip, out_channels=n_skip, kernel_size=1)\n",
    "        \n",
    "        if (additional_features > 0):      \n",
    "          self.linear1 = nn.Linear(in_features= n_skip + nhpp_nbr + additional_features, out_features=mu) # + 3, i.e. month, day, weekday\n",
    "        else:\n",
    "          self.linear1 = nn.Linear(in_features= n_skip + nhpp_nbr, out_features=mu)\n",
    "          \n",
    "          \n",
    "        if (nhpp_nbr + additional_features > 0): \n",
    "          self.linear_nhpp = nn.Linear(in_features = nhpp_nbr + additional_features, \n",
    "                                         out_features = nhpp_nbr + additional_features)\n",
    "          \n",
    "        self.dropout = nn.Dropout (p=0.5)\n",
    "        \n",
    "    def forward(self, input):\n",
    "        # Separating histories\n",
    "        history, nhpp = input\n",
    "        \n",
    "        \n",
    "        \n",
    "#         history = sigm(history) # Filtering the entire history, to remove and check that way\n",
    "        wn_history = self.preprocess(history) # Preparing the history for the Wavenet sequence\n",
    "        ######### Wavenet Sequence ##########\n",
    "        \n",
    "        skip_connections = []\n",
    "        loop_cst = 0\n",
    "        # Convolution sequence\n",
    "        for s, t, skip_scale, residue_scale in zip(self.conv_sigmoid, self.conv_tanh, self.skip_scale, self.residue_scale):\n",
    "            wn_history, skip = self.residue_forward(wn_history, s, t, skip_scale, residue_scale)\n",
    "            \n",
    "            skip_connections.append(skip)\n",
    "        # sum up skip connections\n",
    "        wn_history = sum([s[:,:,-wn_history.size(2):] for s in skip_connections]) # If standard Wavenet#\n",
    "        ######## End of Wavenet Sequence #########\n",
    "        # \n",
    "        output = self.postprocess(wn_history, nhpp)\n",
    "        \n",
    "        \n",
    "        return output\n",
    "    \n",
    "    def preprocess(self, input):\n",
    "        output = input.unsqueeze(0).unsqueeze(0).cuda()\n",
    "        output = self.from_input(output)\n",
    "        return output\n",
    "    \n",
    "    \n",
    "    def postprocess(self, wn_history, nhpp):\n",
    "        \n",
    "        \n",
    "            \n",
    "        # Discard first nhpp (+1) points as we don't need them - they don't have nhpp history points\n",
    "        wn_history = wn_history[:,:,-wn_history.size(2)+self.nhpp_nbr:]\n",
    "        \n",
    "        start_point = nhpp.size(1) - wn_history.size(2)\n",
    "        \n",
    "        nhpp = nhpp[:,start_point:].unsqueeze(0)\n",
    "  \n",
    "  \n",
    "        # One layer to process the nhp\n",
    "        if (add_nhpp_FF == True):\n",
    "          if (nhpp.size(1) + additional_features > 0):\n",
    "            nhpp = torch.tanh(self.linear_nhpp(nhpp.transpose(1,2)).transpose(1,2))\n",
    "        \n",
    "\n",
    "        output = torch.cat((wn_history, nhpp), dim = 1)\n",
    "        \n",
    "        \n",
    "        output = output.transpose(1,2)\n",
    "\n",
    "#         Dropout\n",
    "        if (add_dropout == True):\n",
    "          if (training_so_dropout == True):\n",
    "            output = self.dropout(output)\n",
    "      \n",
    "        output = self.linear1(output).squeeze(0).squeeze(1) # Last layer: linear - for regression\n",
    "        \n",
    "        \n",
    "        return output\n",
    "    \n",
    "    def residue_forward(self, input, conv_sigmoid, conv_tanh, skip_scale, residue_scale):\n",
    "        output = input\n",
    "        output_sigmoid, output_tanh = conv_sigmoid(output), conv_tanh(output)\n",
    "        output = torch.sigmoid(output_sigmoid) * torch.tanh(output_tanh)\n",
    "        skip = skip_scale(output)\n",
    "        output = residue_scale(output)\n",
    "        output = output + input[:,:,-output.size(2):]\n",
    "        return output, skip\n",
    "    \n",
    "    def generate_slow(self, batch, nhpp, wn_length, n=100):\n",
    "        print (nhpp.size(), batch.size())\n",
    "        for _ in range(n):\n",
    "            x = (batch[-wn_length - 1:], nhpp[:,:,-wn_length - 1:])\n",
    "            print (x[0].size(), x[1].size())\n",
    "            y = self.forward(x)\n",
    "            _, i = y.max(dim=1)\n",
    "            batch.append(i.data.tolist()[-1])\n",
    "        return batch\n",
    "    \n",
    "    def generate(self, input=None, n=100, temperature=None, estimate_time=False):\n",
    "        ## prepare output_buffer\n",
    "        output = self.preprocess(input)\n",
    "        output_buffer = []\n",
    "        for s, t, skip_scale, residue_scale, d in zip(self.conv_sigmoid, self.conv_tanh, self.skip_scale, self.residue_scale, self.dilations):\n",
    "            output, _ = self.residue_forward(output, s, t, skip_scale, residue_scale)\n",
    "            sz = 1 if d==2**(self.dilation_depth-1) else d*2\n",
    "            output_buffer.append(output[:,:,-sz-1:-1])\n",
    "        ## generate new \n",
    "        res = input.data.tolist()\n",
    "        for i in range(n):\n",
    "            output = Variable(torch.LongTensor(res[-2:]))\n",
    "            output = self.preprocess(output)\n",
    "            output_buffer_next = []\n",
    "            skip_connections = [] # save for generation purposes\n",
    "            for s, t, skip_scale, residue_scale, b in zip(self.conv_sigmoid, self.conv_tanh, self.skip_scale, self.residue_scale, output_buffer):\n",
    "                output, residue = self.residue_forward(output, s, t, skip_scale, residue_scale)\n",
    "                output = torch.cat([b, output], dim=2)\n",
    "                skip_connections.append(residue)\n",
    "                if i%20==0:\n",
    "                    output = output.clone()\n",
    "                output_buffer_next.append(output[:,:,-b.size(2):])\n",
    "            output_buffer = output_buffer_next\n",
    "            output = output[:,:,-1:]\n",
    "            # sum up skip connections\n",
    "            output = sum(skip_connections)\n",
    "            output = self.postprocess(output)\n",
    "            if temperature is None:\n",
    "                _, output = output.max(dim=1)\n",
    "            else:\n",
    "                output = output.div(temperature).exp().multinomial(1).squeeze()\n",
    "            res.append(output.data[-1])\n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xG1lqPHDGqut"
   },
   "outputs": [],
   "source": [
    "# Function which runs the algorithm\n",
    "\n",
    "\n",
    "# classes = 128\n",
    "# values = periodicise(classes, vals)\n",
    "# vals = ground truth, all dataset\n",
    "def run_algorithm_gpu(nhpp, n_residue, n_skip, dilation_depth, n_repeat,\n",
    "                      max_epoch, batch_size, learn_rate, graph_cst, batches, additional_features = 0,\n",
    "                      dbd_prediction = False, add_nhpp_FF = True, add_dropout = True):\n",
    "    print ('nhpp', nhpp[nhpp_nbr].size(1))\n",
    "    print ('additional features', additional_features)\n",
    "    print ('n_residue', n_residue)\n",
    "    print ('n_skip', n_skip)\n",
    "    print ('dilation_depth', dilation_depth)\n",
    "    print ('n_repeat', n_repeat)\n",
    "    print ('max_epoch', max_epoch)\n",
    "    print ('batch_size', batch_size)\n",
    "    print ('learning rate', learn_rate)\n",
    "    print ('add_nhpp_FF', add_nhpp_FF)\n",
    "    print ('add_dropout', add_dropout)\n",
    "    best_valid_epoch = 0\n",
    "    net = WaveNet(mu=1,n_residue=n_residue,n_skip=n_skip,dilation_depth=dilation_depth,\n",
    "                  n_repeat=n_repeat, nhpp = nhpp, nhpp_nbr = nhpp_nbr,\n",
    "                 additional_features = additional_features).cuda()\n",
    "    optimizer = optim.Adam(net.parameters(),lr=learn_rate)\n",
    "#     optimizer = optim.Adam(net.parameters(),lr=learn_rate)\n",
    "#     opti mizer = optim.SGD(net.parameters(), lr=learn_rate, momentum=0.9)\n",
    "    max_data = len(values)-batch_size\n",
    "    \n",
    "    \n",
    "    loss_fct = RMSELoss()\n",
    "    temp_loss = torch.Tensor(0)\n",
    "\n",
    "    loss_save = [] \n",
    "    valid_loss_save = []\n",
    "    valid_loss = 1000000\n",
    "    last_loss = 1000000\n",
    "    \n",
    "    sequence_lowering_loss = 0\n",
    "    \n",
    "    for epoch in range(max_epoch):\n",
    "        optimizer.zero_grad()\n",
    "        loss = 0\n",
    "        training_so_dropout = True\n",
    "        for j in range(int(batches)):\n",
    "            i = random.randint(0, len(values) - batch_size)\n",
    "            batch = torch.FloatTensor(values[i : i + batch_size]).cuda()\n",
    "\n",
    "            nhpp_sample = nhpp[nhpp_nbr][:,:, i : i + batch_size].cuda()\n",
    "#             print (nhpp_sample.size())\n",
    "            x = (batch[:-1], nhpp_sample[0,:,:-1])\n",
    "            logits = net(x)\n",
    "            sz = logits.size(0)\n",
    "            temp_loss = loss_fct(logits, batch[-sz:])\n",
    "            \n",
    "            loss = loss + temp_loss\n",
    "         \n",
    "        loss = loss/batches\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        loss_save.append(float(loss))\n",
    "        if (epoch % graph_cst == 0):\n",
    "          training_so_dropout = False\n",
    "      \n",
    "          if (dbd_prediction == False):\n",
    "#               print('-------test set, epoch {}--------'.format(epoch+1))\n",
    "              batch = torch.FloatTensor(values_valid).cuda()\n",
    "              nhpp_sample = nhpp_valid[nhpp_nbr]\n",
    "\n",
    "              x = (batch[:-1], nhpp_sample[0,:,:-1])\n",
    "\n",
    "              logits = net(x)\n",
    "              l = logits \n",
    "              sz = logits.size(0)\n",
    "              v_loss = loss_fct(logits, batch[-sz:])\n",
    "#             t_loss = test_generation(nt = net, nhpp = nhpp[nhpp_nbr], plot=False, prediction_window = 5) # For maenners dataset\n",
    "          else:    \n",
    "            diff, t_loss = test_generation(nt = net, nhpp = nhpp_test[nhpp_nbr], plot=False, prediction_window = 30)\n",
    "            \n",
    "          if (v_loss < valid_loss):\n",
    "            best_valid_epoch = epoch\n",
    "            valid_loss = v_loss\n",
    "            best_net = WaveNet(mu=1,n_residue=n_residue,n_skip=n_skip,dilation_depth=dilation_depth,\n",
    "                                n_repeat=n_repeat, nhpp = nhpp, nhpp_nbr = nhpp_nbr,\n",
    "                                additional_features = additional_features).cuda()\n",
    "            best_net.load_state_dict(copy.deepcopy(net.state_dict()))\n",
    "            \n",
    "            valid_loss_save.append(float(valid_loss))\n",
    "            \n",
    "            \n",
    "#           if(t_loss > last_loss):\n",
    "#             sequence_lowering_loss += 1\n",
    "#           else:\n",
    "#             sequence_lowering_loss = 0\n",
    "# #           print (sequence_lowering_loss, last_loss)\n",
    "#           last_loss = t_loss\n",
    "#           if(sequence_lowering_loss > 20):\n",
    "#               print ('epoch finished', epoch)\n",
    "#               break\n",
    "            \n",
    "            \n",
    "    \n",
    "            \n",
    "#             print (t_loss, test_loss)\n",
    "     \n",
    "    return best_net, loss_save, valid_loss_save, best_valid_epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sKH1irIjrLda"
   },
   "outputs": [],
   "source": [
    "\n",
    "# Function used to create the near history past points (nhpp for short)\n",
    "def past_points_collector(values, nhpp_nbr):\n",
    "  # Precomputing the allocation of each GMM's to each point and additional features\n",
    "  # gmm_batch_size - starting point of allocation\n",
    "  # every gmm_interval allocate the new batch of GMM's\n",
    "  \n",
    "\n",
    "  \n",
    "  nhpp = []\n",
    "    \n",
    "  if (nhpp_nbr > 0):\n",
    "    nhpp.extend(values[0 : nhpp_nbr])\n",
    "    nhpp = torch.FloatTensor(nhpp).unsqueeze(1).unsqueeze(0).cuda()\n",
    "    for i in range (nhpp_nbr + 1, len(values)):\n",
    "\n",
    "      nhpp_temp = []\n",
    "\n",
    "      nhpp_temp.extend(values[i - nhpp_nbr : i])\n",
    "      nhpp_temp = torch.FloatTensor(nhpp_temp).unsqueeze(1).unsqueeze(0).cuda()\n",
    "      \n",
    "      nhpp = torch.cat((nhpp, nhpp_temp), dim = 2)\n",
    "  \n",
    "  else:\n",
    "    nhpp = torch.FloatTensor(nhpp).unsqueeze(1).unsqueeze(0).cuda()\n",
    "    for i in range (0, len(values)):\n",
    "      nhpp_temp = torch.FloatTensor([]).unsqueeze(1).unsqueeze(0).cuda()\n",
    "      \n",
    "      nhpp = torch.cat((nhpp,nhpp_temp), dim = 2)\n",
    "\n",
    "      \n",
    "    \n",
    "    \n",
    "    \n",
    "  return nhpp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "cjocfKKBf8sJ",
    "outputId": "7f294e7e-b4f5-4657-ee80-7621c9d2aeac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "graph cst 20\n",
      "59999\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 140\n",
      "--------------- done 3.3333333333333335 % ----------------- time left 13705.402189016342\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 620\n",
      "--------------- done 6.666666666666667 % ----------------- time left 13269.66861820221\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 160\n",
      "--------------- done 10.0 % ----------------- time left 12831.175483703613\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 300\n",
      "--------------- done 13.333333333333334 % ----------------- time left 12353.318429470062\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 200\n",
      "--------------- done 16.666666666666664 % ----------------- time left 11871.777766942978\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 500\n",
      "--------------- done 20.0 % ----------------- time left 11402.32624053955\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 420\n",
      "--------------- done 23.333333333333332 % ----------------- time left 10912.567651748657\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 360\n",
      "--------------- done 26.666666666666668 % ----------------- time left 10457.623779773712\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 80\n",
      "--------------- done 30.0 % ----------------- time left 9969.659176826477\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 820\n",
      "--------------- done 33.33333333333333 % ----------------- time left 9497.527961730957\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 240\n",
      "--------------- done 36.666666666666664 % ----------------- time left 9027.378003358841\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 660\n",
      "--------------- done 40.0 % ----------------- time left 8541.767828464508\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 100\n",
      "--------------- done 43.333333333333336 % ----------------- time left 8063.844856500626\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 800\n",
      "--------------- done 46.666666666666664 % ----------------- time left 7593.519214630127\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 520\n",
      "--------------- done 50.0 % ----------------- time left 7113.032265901566\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 580\n",
      "--------------- done 53.333333333333336 % ----------------- time left 6627.2971839904785\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 260\n",
      "--------------- done 56.666666666666664 % ----------------- time left 6151.814757823944\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 520\n",
      "--------------- done 60.0 % ----------------- time left 5678.75688457489\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 720\n",
      "--------------- done 63.33333333333333 % ----------------- time left 5204.285047292709\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 460\n",
      "--------------- done 66.66666666666666 % ----------------- time left 4728.912217617035\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 980\n",
      "--------------- done 70.0 % ----------------- time left 4277.5340802669525\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 200\n",
      "--------------- done 73.33333333333333 % ----------------- time left 3808.7227573394775\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 480\n",
      "--------------- done 76.66666666666667 % ----------------- time left 3326.2450535297394\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 920\n",
      "--------------- done 80.0 % ----------------- time left 2854.6942462921143\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 780\n",
      "--------------- done 83.33333333333334 % ----------------- time left 2381.5328180789948\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 100\n",
      "--------------- done 86.66666666666667 % ----------------- time left 1897.0965814590454\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 860\n",
      "--------------- done 90.0 % ----------------- time left 1428.295030117035\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 700\n",
      "--------------- done 93.33333333333333 % ----------------- time left 949.4072728157043\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 540\n",
      "--------------- done 96.66666666666667 % ----------------- time left 473.8280384540558\n",
      "nhpp 84\n",
      "additional features 0\n",
      "n_residue 25\n",
      "n_skip 1916\n",
      "dilation_depth 12\n",
      "n_repeat 2\n",
      "max_epoch 1000\n",
      "batch_size 8376\n",
      "learning rate 0.001\n",
      "add_nhpp_FF False\n",
      "add_dropout False\n",
      "best validation epoch 100\n",
      "--------------- done 100.0 % ----------------- time left 0.0\n"
     ]
    }
   ],
   "source": [
    "# Applying algorithm to the data, takes a long ass time\n",
    "\n",
    "additional_features = 0\n",
    "prediction_train_size = 100\n",
    "\n",
    "import time\n",
    "\n",
    "\n",
    "n_skips = [2000]\n",
    "# n_skips = [300, 700]\n",
    "\n",
    "\n",
    "# learn_rates = [1e-3, 3e-3, 5e-3]\n",
    "learn_rates = [1e-3]\n",
    "\n",
    "# nhpp_nbrs = [0, 14, 28, 42, 56, 70, 84, 98] # tanke & tanke_ts & baerchen\n",
    "nhpp_nbrs = [84] # maenners\n",
    "\n",
    "# nhpp_nbrs = [0, 42]\n",
    "\n",
    "\n",
    "# dilation_depths = [6, 7] # tanke & tanke_ts & baerchen\n",
    "dilation_depths = [12] # maenners\n",
    "# dilation_depths = [6]\n",
    "\n",
    "training_so_dropout = True\n",
    "add_nhpp_FF = False\n",
    "add_dropout = False\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Constants\n",
    "\n",
    "number_of_experiments = 30 #constant saying how many experiments should be run for one setting of hyperparameters\n",
    "# it's used to calculate the mean standard deviation of the test loss in the case we want to make a comparison with \n",
    "# other models\n",
    "\n",
    "\n",
    "\n",
    "max_epoch = 1000\n",
    "batches = 5\n",
    "graph_cst = math.floor(max_epoch/50)\n",
    "print ('graph cst', graph_cst)\n",
    "n_residue = 25\n",
    "n_repeat = 2\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "calc_length = len(nhpp_nbrs)*len(dilation_depths)*len(n_skips)*len(learn_rates) * number_of_experiments\n",
    "calc_count = 0\n",
    "dbd_prediction = False\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "net = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(int))))\n",
    "nhpp = defaultdict(int)\n",
    "nhpp_test = defaultdict(int)\n",
    "nhpp_valid = defaultdict(int)\n",
    "losses = defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(\n",
    "                                lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(int))))))))\n",
    "\n",
    "\n",
    "print (len(values))\n",
    "\n",
    "\n",
    "for nhpp_nbr in nhpp_nbrs:\n",
    "  for learn_rate in learn_rates:\n",
    "    nhpp[nhpp_nbr] = past_points_collector(values = arma_train, nhpp_nbr = nhpp_nbr)\n",
    "    nhpp_test[nhpp_nbr] = past_points_collector(values = arma_test, nhpp_nbr = nhpp_nbr)\n",
    "    nhpp_valid[nhpp_nbr] = past_points_collector(values = arma_valid, nhpp_nbr = nhpp_nbr)\n",
    "\n",
    "    for n in range(len(n_skips)):\n",
    "      \n",
    "      n_skip = n_skips[n] - nhpp_nbr - additional_features\n",
    "\n",
    "        #   for n_skip in n_skips:\n",
    "\n",
    "      for dilation_depth in dilation_depths:\n",
    "            batch_size = 2**(dilation_depth+1) + nhpp_nbr + prediction_train_size\n",
    "            total_train_loss = torch.zeros(max_epoch)\n",
    "            total_valid_loss = []\n",
    "            best_valid_train_loss = []\n",
    "            total_test_loss = []\n",
    "    #     for nhpp_nbr in nhpps:\n",
    "\n",
    "\n",
    "            for repeat in range (number_of_experiments):\n",
    "\n",
    "              \n",
    "              start = time.time()\n",
    "              calc_count += 1\n",
    "\n",
    "\n",
    "              net[nhpp_nbr][n_skip][dilation_depth][learn_rate], loss_save, valid_loss, best_valid_epoch = run_algorithm_gpu(nhpp = nhpp, \n",
    "                                                   n_residue = n_residue, \n",
    "                                                   n_skip = n_skip, \n",
    "                                                   dilation_depth = dilation_depth,  \n",
    "                                                   n_repeat = n_repeat,\n",
    "                                                   batch_size = batch_size, \n",
    "                                                   learn_rate = learn_rate, \n",
    "                                                   max_epoch = max_epoch,\n",
    "                                                   graph_cst = graph_cst,\n",
    "                                                   additional_features = additional_features,\n",
    "                                                   dbd_prediction = dbd_prediction,\n",
    "                                                   batches = batches,\n",
    "                                                   add_nhpp_FF = add_nhpp_FF,\n",
    "                                                   add_dropout = add_dropout)\n",
    "          \n",
    "              print ('best validation epoch', best_valid_epoch)\n",
    "              \n",
    "              \n",
    "              # Calculating test loss\n",
    "              batch = torch.FloatTensor(values_test).cuda()\n",
    "              nhpp_sample = nhpp_test[nhpp_nbr]\n",
    "              x = (batch[:-1], nhpp_sample[0,:,:-1])\n",
    "              logits = net[nhpp_nbr][n_skip][dilation_depth][learn_rate](x)\n",
    "              sz = logits.size(0)\n",
    "              t_loss = loss_fct(logits, batch[-sz:])\n",
    "              \n",
    "              \n",
    "              \n",
    "          \n",
    "              total_train_loss += torch.Tensor(loss_save)\n",
    "              total_valid_loss.append(min(valid_loss))\n",
    "              best_valid_train_loss.append(loss_save[best_valid_epoch])\n",
    "              total_test_loss.append(float(t_loss))\n",
    "              \n",
    "              \n",
    "              \n",
    "              end = time.time()\n",
    "              print ('---------------', 'done', (calc_count/calc_length) * 100, \n",
    "                    '% -----------------', 'time left', (end-start) * (calc_length-calc_count))\n",
    "            \n",
    "            \n",
    "            total_train_loss = total_train_loss / number_of_experiments\n",
    "            total_valid_loss = np.mean(total_valid_loss) \n",
    "            best_valid_train_loss = np.mean(best_valid_train_loss) \n",
    "#             total_test_loss = total_test_loss\n",
    "            \n",
    "            \n",
    "            losses[nhpp_nbr][n_residue][n_skip][dilation_depth][n_repeat][prediction_train_size][learn_rate][max_epoch] = [total_train_loss, \n",
    "                                                                                                                np.mean(total_valid_loss),\n",
    "                                                                                                                np.mean(best_valid_train_loss),\n",
    "                                                                                                                np.mean(total_test_loss),\n",
    "                                                                                                                np.std(total_test_loss)]\n",
    "            \n",
    "              #         test_generation(first_points = 0, last_points = 60, prediction_window = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tzg822XwpLoz"
   },
   "outputs": [],
   "source": [
    "total_time_end = time.time()\n",
    "total_duration = total_time_start - total_time_end\n",
    "class l_class:\n",
    "  def __init__(self):\n",
    "\n",
    "        self.nhpp_nbr = 0\n",
    "\n",
    "        self.n_residue  = 0\n",
    "\n",
    "        self.n_skip  = 0\n",
    "\n",
    "        self.n_repeat   = 0\n",
    "        \n",
    "        self.prediction_train_size = 0\n",
    "        \n",
    "        self.dilation_depth = 0\n",
    "        \n",
    "        self.learn_rate = 0\n",
    "        \n",
    "        self.max_epoch = 0\n",
    "        \n",
    "        self.loss_train = []\n",
    "        self.loss_train_zero = []\n",
    "        \n",
    "        self.loss_valid = 0\n",
    "        self.loss_valid_zero = 0\n",
    "        \n",
    "        self.loss_test = 0\n",
    "        self.loss_test_zero = 0\n",
    "        \n",
    "        self.best_valid_train_loss = 0\n",
    "        self.best_valid_train_loss_zero = 0\n",
    "        \n",
    "        self.test_std = 0\n",
    "        \n",
    "        self.nbr_of_experiments = 0\n",
    "      \n",
    "        self.total_time_duration = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "ViWVOynzrkJW",
    "outputId": "2f75b858-2cc0-4c5b-8c65-4441e72b04d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test loss and test loss_zero 0.3456702490647634 0.000340361902083804\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-d6cc1bdf07f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m                     \u001b[0mobject_to_write\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlos_train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m                     \u001b[0mobject_to_write\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_train_zero\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlos_train_zero\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m                     \u001b[0mobject_to_write\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlos_valid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'los_train_zero' is not defined"
     ]
    }
   ],
   "source": [
    "# Cell which saves the results for later analysis\n",
    "# Showing the results and deciding which ones are good and which ones are not\n",
    "# write_file_title = \"\"\n",
    "if (make_dataset == True):\n",
    "  write_file_title = \"MA_\" + \"noise_\"+str(noise)+\"_results.txt\"\n",
    "\n",
    "else:\n",
    "  write_file_title = \"MA_\"\n",
    "\n",
    "  if (add_nhpp_FF == True):\n",
    "    write_file_title = write_file_title + \"FF_\"\n",
    "  if (add_dropout == True):\n",
    "    write_file_title = write_file_title + \"dropout_\"\n",
    "  write_file_title = write_file_title + file + \"_results.txt\"\n",
    "  \n",
    "test_stds = []\n",
    "\n",
    "f = open(write_file_title,\"wb\")\n",
    "object_list = []\n",
    "\n",
    "loss_fct = RMSELoss()\n",
    "min_los = 10000\n",
    "min_diff = 10000\n",
    "los_list = []\n",
    "for nhpp_nbr in losses.keys():\n",
    "  for n_residue in losses[nhpp_nbr].keys():\n",
    "    for n_skip in losses[nhpp_nbr][n_residue].keys():\n",
    "      for dilation_depth in losses[nhpp_nbr][n_residue][n_skip].keys():\n",
    "        for n_repeat in losses[nhpp_nbr][n_residue][n_skip][dilation_depth].keys():\n",
    "          for prediction_train_size in losses[nhpp_nbr][n_residue][n_skip][dilation_depth][n_repeat].keys():\n",
    "            for learn_rate in losses[nhpp_nbr][n_residue][n_skip][dilation_depth][n_repeat][prediction_train_size].keys():\n",
    "              for max_epoch in losses[nhpp_nbr][n_residue][n_skip][dilation_depth][n_repeat][prediction_train_size][learn_rate].keys():\n",
    "                  if( type(losses[nhpp_nbr][n_residue][n_skip][dilation_depth][n_repeat][prediction_train_size][learn_rate][max_epoch]) != int):\n",
    "                    \n",
    "                    # current results\n",
    "                    los_train = losses[nhpp_nbr][n_residue][n_skip][dilation_depth][n_repeat][prediction_train_size][learn_rate][max_epoch][0]\n",
    "                    los_valid = losses[nhpp_nbr][n_residue][n_skip][dilation_depth][n_repeat][prediction_train_size][learn_rate][max_epoch][1]\n",
    "                    best_valid_train_loss = losses[nhpp_nbr][n_residue][n_skip][dilation_depth][n_repeat][prediction_train_size][learn_rate][max_epoch][2]\n",
    "                    test_los = losses[nhpp_nbr][n_residue][n_skip][dilation_depth][n_repeat][prediction_train_size][learn_rate][max_epoch][3]\n",
    "                    test_std = losses[nhpp_nbr][n_residue][n_skip][dilation_depth][n_repeat][prediction_train_size][learn_rate][max_epoch][4]\n",
    "\n",
    "                    print ('test loss and test std', test_los, test_std)\n",
    "                  \n",
    "\n",
    "                    \n",
    "  \n",
    "                    # Writing to file\n",
    "                    object_to_write = l_class()\n",
    "                    \n",
    "                    object_to_write.nhpp_nbr      = nhpp_nbr\n",
    "\n",
    "                    object_to_write.n_residue       = n_residue\n",
    "\n",
    "                    object_to_write.n_skip      = n_skip\n",
    "\n",
    "                    object_to_write.n_repeat   = n_repeat\n",
    "\n",
    "                    object_to_write.prediction_train_size = n_repeat\n",
    "\n",
    "                    object_to_write.learn_rate = learn_rate\n",
    "                    \n",
    "                    object_to_write.dilation_depth = dilation_depth\n",
    "\n",
    "                    object_to_write.max_epoch = max_epoch\n",
    "                    \n",
    "                    object_to_write.loss_train = los_train\n",
    "                    \n",
    "                    object_to_write.loss_valid = los_valid\n",
    "                    \n",
    "                    object_to_write.loss_test = test_los\n",
    "\n",
    "                    object_to_write.best_valid_train_loss = best_valid_train_loss\n",
    "                    \n",
    "                    object_to_write.test_std = test_std\n",
    "                    \n",
    "                    object_to_write.nbr_of_experiments = number_of_experiments\n",
    "\n",
    "                    object_to_write.total_time_duration = total_duration\n",
    "                    \n",
    "                    object_list.append(object_to_write)\n",
    "                    \n",
    "                    \n",
    "pickle.dump(object_list, f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wiwtgp5nJYRO"
   },
   "outputs": [],
   "source": []
  }
 ],
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