research_lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense, LSTM\n",
    "from keras.callbacks import ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NUM_THREADS = 8\n",
    "# import tensorflow as tf\n",
    "# tf.config.threading.set_intra_op_parallelism_threads(num_threads=NUM_THREADS)\n",
    "# tf.config.threading.set_inter_op_parallelism_threads(num_threads=NUM_THREADS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_feather('data/dataset.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(df['VALUE'])\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.10363717 0.10378013 0.1040646  ... 0.62437476 0.62489797 0.62518959]\n"
     ]
    }
   ],
   "source": [
    "min_val = np.min(df['VALUE'])\n",
    "max_val = np.max(df['VALUE'])\n",
    "\n",
    "# Normalize the array\n",
    "train_data = (df['VALUE'].values - min_val) / (max_val - min_val)\n",
    "\n",
    "print(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[ 7925.93  7926.93  7928.92 ... 11568.63 11572.29 11574.33].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MinMaxScaler\n\u001b[1;32m      4\u001b[0m scaler \u001b[38;5;241m=\u001b[39m MinMaxScaler(feature_range\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m----> 5\u001b[0m scaled_data \u001b[38;5;241m=\u001b[39m \u001b[43mscaler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mVALUE\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m scaled_data\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/sklearn/utils/_set_output.py:157\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m    156\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 157\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    158\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    159\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m    160\u001b[0m         return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    161\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m    162\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m    163\u001b[0m         )\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/sklearn/base.py:916\u001b[0m, in \u001b[0;36mTransformerMixin.fit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[38;5;66;03m# non-optimized default implementation; override when a better\u001b[39;00m\n\u001b[1;32m    913\u001b[0m \u001b[38;5;66;03m# method is possible for a given clustering algorithm\u001b[39;00m\n\u001b[1;32m    914\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    915\u001b[0m     \u001b[38;5;66;03m# fit method of arity 1 (unsupervised transformation)\u001b[39;00m\n\u001b[0;32m--> 916\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtransform(X)\n\u001b[1;32m    917\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    918\u001b[0m     \u001b[38;5;66;03m# fit method of arity 2 (supervised transformation)\u001b[39;00m\n\u001b[1;32m    919\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:435\u001b[0m, in \u001b[0;36mMinMaxScaler.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[38;5;66;03m# Reset internal state before fitting\u001b[39;00m\n\u001b[1;32m    434\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()\n\u001b[0;32m--> 435\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpartial_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/sklearn/base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1145\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m   1147\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m   1148\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1150\u001b[0m     )\n\u001b[1;32m   1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:473\u001b[0m, in \u001b[0;36mMinMaxScaler.partial_fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    467\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    468\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMinMaxScaler does not support sparse input. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    469\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConsider using MaxAbsScaler instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    470\u001b[0m     )\n\u001b[1;32m    472\u001b[0m first_pass \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples_seen_\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 473\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    474\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    475\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfirst_pass\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    476\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mFLOAT_DTYPES\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    477\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    478\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    480\u001b[0m data_min \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnanmin(X, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    481\u001b[0m data_max \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnanmax(X, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/sklearn/base.py:605\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m    603\u001b[0m         out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m    604\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[0;32m--> 605\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    606\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n\u001b[1;32m    607\u001b[0m     out \u001b[38;5;241m=\u001b[39m _check_y(y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n",
      "File \u001b[0;32m/usr/local/lib/python3.10/site-packages/sklearn/utils/validation.py:938\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    936\u001b[0m     \u001b[38;5;66;03m# If input is 1D raise error\u001b[39;00m\n\u001b[1;32m    937\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 938\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    939\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 2D array, got 1D array instead:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124marray=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    940\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReshape your data either using array.reshape(-1, 1) if \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    941\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myour data has a single feature or array.reshape(1, -1) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    942\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mif it contains a single sample.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[1;32m    943\u001b[0m         )\n\u001b[1;32m    945\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_numeric \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(array\u001b[38;5;241m.\u001b[39mdtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUSV\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    946\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    947\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not compatible with arrays of bytes/strings.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    948\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConvert your data to numeric values explicitly instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    949\u001b[0m     )\n",
      "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[ 7925.93  7926.93  7928.92 ... 11568.63 11572.29 11574.33].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "# Scale the data\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_data = scaler.fit_transform(df['VALUE'])\n",
    "\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0.10363717, 0.10378013, 0.1040646 , 0.10417182, 0.10435051,\n",
      "       0.10438625, 0.10435051, 0.10431477, 0.10435051, 0.10452777,\n",
      "       0.10470647, 0.10467073, 0.10527542, 0.10556133, 0.10566712,\n",
      "       0.10541838, 0.10531116, 0.10523968, 0.1051339 , 0.10516964,\n",
      "       0.10516964, 0.1051339 , 0.1051339 , 0.10527542, 0.1051339 ,\n",
      "       0.10509816, 0.10509816, 0.10495521, 0.10502668, 0.10488373,\n",
      "       0.10484799, 0.10484799, 0.10484799, 0.10509816, 0.10509816,\n",
      "       0.10520394, 0.10499094, 0.10502668, 0.10491947, 0.10491947,\n",
      "       0.10484799, 0.1047422 , 0.10470647, 0.10459925, 0.1047422 ,\n",
      "       0.10481225, 0.10481225, 0.10499094, 0.10484799, 0.10488373,\n",
      "       0.10467073, 0.1044563 , 0.10456351, 0.1044563 , 0.10438625,\n",
      "       0.10438625, 0.10442056, 0.1044563 , 0.10392308, 0.10395882,\n",
      "       0.10402887, 0.10402887, 0.10410034, 0.10413608, 0.10427903,\n",
      "       0.10413608, 0.1040646 , 0.1038516 , 0.10356712, 0.10370865,\n",
      "       0.10353139, 0.10374439, 0.10370865, 0.10335269, 0.10324548,\n",
      "       0.10310395, 0.10299674, 0.102961  , 0.10345991, 0.10367291,\n",
      "       0.10374439, 0.10367291, 0.10353139, 0.10345991, 0.10331696,\n",
      "       0.10313969, 0.10267652, 0.10271226, 0.10281805, 0.10271226,\n",
      "       0.10267652, 0.10292526, 0.10306822, 0.10313969, 0.10281805,\n",
      "       0.10260505, 0.10253357, 0.10235631, 0.10224909, 0.10228483,\n",
      "       0.10153718, 0.10167871, 0.10189314, 0.10203466, 0.1020704 ,\n",
      "       0.10224909, 0.1020704 , 0.10192887, 0.10199892, 0.10235631,\n",
      "       0.10267652, 0.10281805, 0.10246209, 0.10242635, 0.10242635,\n",
      "       0.10235631, 0.10235631, 0.10253357, 0.10232057, 0.10232057,\n",
      "       0.10232057, 0.10232057, 0.10224909, 0.10167871, 0.10171444,\n",
      "       0.10142997, 0.10110975, 0.10118123, 0.10125127, 0.10110975,\n",
      "       0.10103827, 0.10004046, 0.09989893, 0.09997041, 0.10004046,\n",
      "       0.10021915, 0.1003621 , 0.10046789, 0.10046789, 0.10043215,\n",
      "       0.10032636, 0.10043215, 0.10039641, 0.10046789, 0.10061084,\n",
      "       0.1005751 , 0.09997041, 0.09940002, 0.0996845 , 0.09972024,\n",
      "       0.09989893, 0.10025489, 0.10018341, 0.10043215, 0.10050363,\n",
      "       0.10107401, 0.10132275, 0.10121554, 0.10128701, 0.10178592,\n",
      "       0.1018574 , 0.10210614, 0.10196461, 0.10210614, 0.10239062,\n",
      "       0.10232057, 0.10246209, 0.10285379, 0.10317543, 0.10310395,\n",
      "       0.10313969, 0.10264078, 0.10239062, 0.10203466, 0.10217761,\n",
      "       0.10214188, 0.10224909, 0.10239062, 0.10288952, 0.102961  ,\n",
      "       0.10320974, 0.10420756, 0.10495521, 0.10527542, 0.10509816,\n",
      "       0.10531116, 0.1051339 , 0.10509816, 0.10509816, 0.10502668,\n",
      "       0.10506242, 0.10488373, 0.10438625, 0.10452777, 0.10435051,\n",
      "       0.1044563 , 0.10467073, 0.10481225, 0.1051339 , 0.10556133,\n",
      "       0.10545411, 0.10559564, 0.10566712, 0.10559564, 0.10598876,\n",
      "       0.1071281 , 0.10744832, 0.10680646, 0.10719815, 0.10726963,\n",
      "       0.10766132, 0.10787575, 0.10808875, 0.10769706, 0.10780427,\n",
      "       0.10784001, 0.10780427, 0.10787575, 0.10791149, 0.10848044,\n",
      "       0.10901509, 0.10922809, 0.10922809, 0.10922809, 0.10922809,\n",
      "       0.10922809, 0.10922809, 0.10922809, 0.11086634, 0.1106176 ,\n",
      "       0.11175694, 0.11197137, 0.1115082 , 0.11214864, 0.1121129 ,\n",
      "       0.11282481, 0.11193564, 0.11154394, 0.11136525, 0.11079487,\n",
      "       0.10983422, 0.10897935, 0.10776854, 0.10748406, 0.1077328 ,\n",
      "       0.10855192, 0.10897935, 0.10947826, 0.11004722, 0.11008296,\n",
      "       0.10961979, 0.10865914, 0.10865914, 0.1086234 , 0.10837466,\n",
      "       0.10784001, 0.10808875, 0.10905083, 0.10965552, 0.11004722,\n",
      "       0.11065334, 0.11111651, 0.11097356, 0.11111651, 0.11104503,\n",
      "       0.1110093 , 0.11097356, 0.11079487, 0.1108306 , 0.11154394,\n",
      "       0.11271902, 0.11311072, 0.11325224, 0.11353815, 0.11399989,\n",
      "       0.11403563, 0.11350241, 0.11289629, 0.11303924, 0.11339519,\n",
      "       0.11314645, 0.11311072, 0.11296776, 0.11286055, 0.11271902,\n",
      "       0.11307498, 0.11293202, 0.1127905 , 0.11239738, 0.11243312,\n",
      "       0.11236307, 0.11264755, 0.11328798, 0.11425006, 0.11510492,\n",
      "       0.11549661, 0.1156024 , 0.11563814, 0.11563814, 0.1151764 ,\n",
      "       0.11464175, 0.1142858 , 0.11407137, 0.11378689, 0.11350241,\n",
      "       0.11382263, 0.11399989, 0.11392984, 0.11382263, 0.11375115,\n",
      "       0.11367967, 0.11364393, 0.11367967, 0.11353815, 0.11375115,\n",
      "       0.11396558, 0.11375115, 0.1138941 , 0.11421432, 0.11457027,\n",
      "       0.11489049, 0.11510492, 0.11521071, 0.1153894 , 0.11510492,\n",
      "       0.11489049, 0.11492623, 0.11464175, 0.11457027, 0.11442732,\n",
      "       0.11425006, 0.1142858 , 0.1141071 , 0.11439301, 0.11432153,\n",
      "       0.11442732, 0.11467749, 0.11503344, 0.11499771, 0.1151764 ,\n",
      "       0.11506918, 0.1151764 , 0.11542514, 0.11531792, 0.10110975,\n",
      "       0.10171444, 0.10381586, 0.10435051, 0.10417182, 0.10331696,\n",
      "       0.10303248, 0.10313969, 0.10388734, 0.10413608, 0.1047422 ,\n",
      "       0.10531116, 0.10659346, 0.10648624, 0.10705663, 0.10737684,\n",
      "       0.1073411 , 0.10705663, 0.10684219, 0.10641476, 0.10602307,\n",
      "       0.10559564, 0.10502668, 0.1040646 , 0.1038516 , 0.10342417,\n",
      "       0.10335269, 0.10367291, 0.10399313, 0.10360143, 0.10324548,\n",
      "       0.10249783, 0.10221335, 0.1020704 , 0.10078953, 0.10089532,\n",
      "       0.10089532, 0.10029062, 0.10011193, 0.09989893, 0.09961302,\n",
      "       0.09989893, 0.09975598, 0.09950724, 0.09893685, 0.09804625,\n",
      "       0.09719139, 0.09776177, 0.09790473, 0.09786899, 0.09758451,\n",
      "       0.0976903 , 0.09794047, 0.09861664, 0.0987939 , 0.09950724,\n",
      "       0.09989893, 0.09979172, 0.09972024, 0.09972024, 0.10025489,\n",
      "       0.10032636, 0.10039641, 0.10110975, 0.10125127, 0.10157292,\n",
      "       0.10164297, 0.10132275, 0.10093106, 0.10128701, 0.10135849,\n",
      "       0.10128701, 0.10125127, 0.10135849, 0.10135849, 0.10132275,\n",
      "       0.10132275, 0.10203466, 0.10249783, 0.10246209, 0.10239062,\n",
      "       0.10249783, 0.10239062, 0.10246209, 0.10199892, 0.10182166,\n",
      "       0.10171444, 0.10150144, 0.10196461, 0.10224909, 0.10242635,\n",
      "       0.10260505, 0.10303248, 0.10324548, 0.10324548, 0.10320974,\n",
      "       0.10303248, 0.10338843, 0.10345991, 0.10349565, 0.10417182,\n",
      "       0.10410034, 0.10431477, 0.10442056, 0.10420756, 0.10410034,\n",
      "       0.10392308, 0.10395882, 0.10410034, 0.10410034, 0.10417182,\n",
      "       0.10399313, 0.10356712, 0.10303248, 0.10306822, 0.10288952,\n",
      "       0.10292526, 0.10281805, 0.10285379, 0.10324548, 0.10338843,\n",
      "       0.10328122, 0.10317543, 0.10313969, 0.10288952, 0.10317543,\n",
      "       0.10324548, 0.10317543, 0.10292526, 0.10299674, 0.10253357,\n",
      "       0.10214188, 0.10228483, 0.10249783, 0.10256931, 0.10246209,\n",
      "       0.10260505, 0.10267652, 0.10278374, 0.10264078, 0.10278374,\n",
      "       0.10292526, 0.102748  , 0.10310395, 0.10306822, 0.10303248,\n",
      "       0.10278374, 0.10214188, 0.10199892, 0.10224909, 0.10217761,\n",
      "       0.10256931, 0.10285379, 0.10328122, 0.10310395, 0.10303248,\n",
      "       0.10278374, 0.10303248, 0.10313969, 0.10317543, 0.10320974,\n",
      "       0.10299674, 0.10320974, 0.10342417, 0.10338843, 0.10342417,\n",
      "       0.10353139, 0.10342417, 0.10349565, 0.10353139, 0.10353139,\n",
      "       0.10367291, 0.11499771, 0.11446306, 0.1144988 , 0.11457027,\n",
      "       0.11442732, 0.11457027, 0.11446306, 0.1147847 , 0.11535366,\n",
      "       0.1153894 , 0.11549661, 0.11553235, 0.1156024 , 0.11553235,\n",
      "       0.11553235, 0.11553235, 0.11542514, 0.11549661, 0.1156024 ,\n",
      "       0.11610131, 0.11660022, 0.11692043, 0.11784677, 0.11805977,\n",
      "       0.11820273, 0.11802404, 0.11773956, 0.11802404, 0.11805977,\n",
      "       0.1186659 , 0.11923485, 0.11909333, 0.11880742, 0.11852294,\n",
      "       0.11816699, 0.11813125, 0.11816699, 0.11813125, 0.11802404,\n",
      "       0.11798973, 0.1175966 , 0.1175623 , 0.11741934, 0.11752656,\n",
      "       0.11741934, 0.11734787, 0.11720491, 0.11699191, 0.11681322,\n",
      "       0.11642152, 0.11599409, 0.11620852, 0.11631574, 0.11645726,\n",
      "       0.11631574, 0.11642152, 0.11638722, 0.11674317, 0.11660022,\n",
      "       0.11660022, 0.11645726, 0.11563814, 0.1153894 , 0.11528218,\n",
      "       0.11496197, 0.11471323, 0.11496197, 0.11446306, 0.11403563,\n",
      "       0.1138941 , 0.11385836, 0.11392984, 0.1136082 , 0.11346667,\n",
      "       0.11357389, 0.11382263, 0.11392984, 0.11392984, 0.11378689,\n",
      "       0.11353815, 0.11339519, 0.11318219, 0.11318219, 0.1132165 ,\n",
      "       0.11311072, 0.11328798, 0.11307498, 0.11335946, 0.11325224,\n",
      "       0.11357389, 0.11357389, 0.1138941 , 0.1141071 , 0.11371541,\n",
      "       0.11353815, 0.11353815, 0.11350241, 0.11214864, 0.1118999 ,\n",
      "       0.11186416, 0.11182842, 0.1106176 , 0.10997717, 0.11029739,\n",
      "       0.11065334, 0.11086634, 0.11140099, 0.11129377, 0.1112223 ,\n",
      "       0.11111651, 0.11125804, 0.11136525, 0.1118999 , 0.11193564,\n",
      "       0.11186416, 0.1121129 , 0.11197137, 0.11154394, 0.1112223 ,\n",
      "       0.11129377, 0.11129377, 0.11143673, 0.1112223 , 0.11143673,\n",
      "       0.11140099, 0.11147247, 0.11147247, 0.11143673, 0.11118799,\n",
      "       0.1110093 , 0.11140099, 0.1115082 , 0.11147247, 0.11132951,\n",
      "       0.11143673, 0.11140099, 0.11125804, 0.11108077, 0.11093782,\n",
      "       0.11079487, 0.11054613, 0.10969126, 0.10965552, 0.10894361,\n",
      "       0.10808875, 0.10819597, 0.10865914, 0.10933531, 0.10912231,\n",
      "       0.10919235, 0.10905083, 0.10926383, 0.10894361, 0.10876635,\n",
      "       0.1088364 , 0.10840897, 0.10805301, 0.10766132, 0.10691367,\n",
      "       0.10662919, 0.10616602, 0.10470647, 0.10292526, 0.1018574 ,\n",
      "       0.10196461, 0.10228483, 0.10388734, 0.1047422 , 0.10531116,\n",
      "       0.1051339 , 0.1051339 , 0.10509816, 0.10552559, 0.10559564,\n",
      "       0.10591728, 0.10502668, 0.10488373, 0.10499094, 0.10502668,\n",
      "       0.10545411, 0.10566712, 0.10556133, 0.10570285, 0.10552559,\n",
      "       0.10538264, 0.10523968, 0.10538264, 0.10584581, 0.10581007,\n",
      "       0.10641476, 0.10634472, 0.10648624, 0.10677215, 0.10741258,\n",
      "       0.10755553, 0.1075198 , 0.10702089, 0.10638045, 0.10548985,\n",
      "       0.10556133, 0.10559564, 0.10581007, 0.10598876, 0.10677215,\n",
      "       0.10730536, 0.10784001, 0.10808875, 0.10794723, 0.10801727,\n",
      "       0.10812449, 0.10837466, 0.10848044, 0.10851618, 0.10873061,\n",
      "       0.10919235, 0.11008296, 0.11040317, 0.11076056, 0.11058186,\n",
      "       0.11043891, 0.11011869, 0.10979848, 0.1088364 , 0.10894361,\n",
      "       0.10894361, 0.10915805, 0.10929957, 0.10969126, 0.109514  ,\n",
      "       0.10965552, 0.11011869, 0.11029739, 0.11058186, 0.11058186,\n",
      "       0.11065334, 0.11026165, 0.10986995, 0.10940678, 0.10961979,\n",
      "       0.10905083, 0.10844471, 0.10766132, 0.10737684, 0.10769706,\n",
      "       0.1075198 , 0.1077328 , 0.10791149, 0.10808875, 0.10776854,\n",
      "       0.10726963, 0.10666493, 0.10648624, 0.10613029, 0.10584581,\n",
      "       0.1047422 , 0.10402887, 0.10420756, 0.10495521, 0.1064505 ,\n",
      "       0.10673641, 0.10716384, 0.10702089, 0.10726963, 0.10698515,\n",
      "       0.10748406, 0.10812449, 0.10915805, 0.10958548, 0.11001148,\n",
      "       0.11004722, 0.11015443, 0.11036886, 0.11043891, 0.11022591,\n",
      "       0.11036886, 0.11118799, 0.1112223 , 0.1112223 , 0.1112223 ,\n",
      "       0.1112223 , 0.1112223 , 0.1112223 , 0.1112223 , 0.11001148])]\n",
      "[0.10719814960680576]\n",
      "\n",
      "[array([0.10363717, 0.10378013, 0.1040646 , 0.10417182, 0.10435051,\n",
      "       0.10438625, 0.10435051, 0.10431477, 0.10435051, 0.10452777,\n",
      "       0.10470647, 0.10467073, 0.10527542, 0.10556133, 0.10566712,\n",
      "       0.10541838, 0.10531116, 0.10523968, 0.1051339 , 0.10516964,\n",
      "       0.10516964, 0.1051339 , 0.1051339 , 0.10527542, 0.1051339 ,\n",
      "       0.10509816, 0.10509816, 0.10495521, 0.10502668, 0.10488373,\n",
      "       0.10484799, 0.10484799, 0.10484799, 0.10509816, 0.10509816,\n",
      "       0.10520394, 0.10499094, 0.10502668, 0.10491947, 0.10491947,\n",
      "       0.10484799, 0.1047422 , 0.10470647, 0.10459925, 0.1047422 ,\n",
      "       0.10481225, 0.10481225, 0.10499094, 0.10484799, 0.10488373,\n",
      "       0.10467073, 0.1044563 , 0.10456351, 0.1044563 , 0.10438625,\n",
      "       0.10438625, 0.10442056, 0.1044563 , 0.10392308, 0.10395882,\n",
      "       0.10402887, 0.10402887, 0.10410034, 0.10413608, 0.10427903,\n",
      "       0.10413608, 0.1040646 , 0.1038516 , 0.10356712, 0.10370865,\n",
      "       0.10353139, 0.10374439, 0.10370865, 0.10335269, 0.10324548,\n",
      "       0.10310395, 0.10299674, 0.102961  , 0.10345991, 0.10367291,\n",
      "       0.10374439, 0.10367291, 0.10353139, 0.10345991, 0.10331696,\n",
      "       0.10313969, 0.10267652, 0.10271226, 0.10281805, 0.10271226,\n",
      "       0.10267652, 0.10292526, 0.10306822, 0.10313969, 0.10281805,\n",
      "       0.10260505, 0.10253357, 0.10235631, 0.10224909, 0.10228483,\n",
      "       0.10153718, 0.10167871, 0.10189314, 0.10203466, 0.1020704 ,\n",
      "       0.10224909, 0.1020704 , 0.10192887, 0.10199892, 0.10235631,\n",
      "       0.10267652, 0.10281805, 0.10246209, 0.10242635, 0.10242635,\n",
      "       0.10235631, 0.10235631, 0.10253357, 0.10232057, 0.10232057,\n",
      "       0.10232057, 0.10232057, 0.10224909, 0.10167871, 0.10171444,\n",
      "       0.10142997, 0.10110975, 0.10118123, 0.10125127, 0.10110975,\n",
      "       0.10103827, 0.10004046, 0.09989893, 0.09997041, 0.10004046,\n",
      "       0.10021915, 0.1003621 , 0.10046789, 0.10046789, 0.10043215,\n",
      "       0.10032636, 0.10043215, 0.10039641, 0.10046789, 0.10061084,\n",
      "       0.1005751 , 0.09997041, 0.09940002, 0.0996845 , 0.09972024,\n",
      "       0.09989893, 0.10025489, 0.10018341, 0.10043215, 0.10050363,\n",
      "       0.10107401, 0.10132275, 0.10121554, 0.10128701, 0.10178592,\n",
      "       0.1018574 , 0.10210614, 0.10196461, 0.10210614, 0.10239062,\n",
      "       0.10232057, 0.10246209, 0.10285379, 0.10317543, 0.10310395,\n",
      "       0.10313969, 0.10264078, 0.10239062, 0.10203466, 0.10217761,\n",
      "       0.10214188, 0.10224909, 0.10239062, 0.10288952, 0.102961  ,\n",
      "       0.10320974, 0.10420756, 0.10495521, 0.10527542, 0.10509816,\n",
      "       0.10531116, 0.1051339 , 0.10509816, 0.10509816, 0.10502668,\n",
      "       0.10506242, 0.10488373, 0.10438625, 0.10452777, 0.10435051,\n",
      "       0.1044563 , 0.10467073, 0.10481225, 0.1051339 , 0.10556133,\n",
      "       0.10545411, 0.10559564, 0.10566712, 0.10559564, 0.10598876,\n",
      "       0.1071281 , 0.10744832, 0.10680646, 0.10719815, 0.10726963,\n",
      "       0.10766132, 0.10787575, 0.10808875, 0.10769706, 0.10780427,\n",
      "       0.10784001, 0.10780427, 0.10787575, 0.10791149, 0.10848044,\n",
      "       0.10901509, 0.10922809, 0.10922809, 0.10922809, 0.10922809,\n",
      "       0.10922809, 0.10922809, 0.10922809, 0.11086634, 0.1106176 ,\n",
      "       0.11175694, 0.11197137, 0.1115082 , 0.11214864, 0.1121129 ,\n",
      "       0.11282481, 0.11193564, 0.11154394, 0.11136525, 0.11079487,\n",
      "       0.10983422, 0.10897935, 0.10776854, 0.10748406, 0.1077328 ,\n",
      "       0.10855192, 0.10897935, 0.10947826, 0.11004722, 0.11008296,\n",
      "       0.10961979, 0.10865914, 0.10865914, 0.1086234 , 0.10837466,\n",
      "       0.10784001, 0.10808875, 0.10905083, 0.10965552, 0.11004722,\n",
      "       0.11065334, 0.11111651, 0.11097356, 0.11111651, 0.11104503,\n",
      "       0.1110093 , 0.11097356, 0.11079487, 0.1108306 , 0.11154394,\n",
      "       0.11271902, 0.11311072, 0.11325224, 0.11353815, 0.11399989,\n",
      "       0.11403563, 0.11350241, 0.11289629, 0.11303924, 0.11339519,\n",
      "       0.11314645, 0.11311072, 0.11296776, 0.11286055, 0.11271902,\n",
      "       0.11307498, 0.11293202, 0.1127905 , 0.11239738, 0.11243312,\n",
      "       0.11236307, 0.11264755, 0.11328798, 0.11425006, 0.11510492,\n",
      "       0.11549661, 0.1156024 , 0.11563814, 0.11563814, 0.1151764 ,\n",
      "       0.11464175, 0.1142858 , 0.11407137, 0.11378689, 0.11350241,\n",
      "       0.11382263, 0.11399989, 0.11392984, 0.11382263, 0.11375115,\n",
      "       0.11367967, 0.11364393, 0.11367967, 0.11353815, 0.11375115,\n",
      "       0.11396558, 0.11375115, 0.1138941 , 0.11421432, 0.11457027,\n",
      "       0.11489049, 0.11510492, 0.11521071, 0.1153894 , 0.11510492,\n",
      "       0.11489049, 0.11492623, 0.11464175, 0.11457027, 0.11442732,\n",
      "       0.11425006, 0.1142858 , 0.1141071 , 0.11439301, 0.11432153,\n",
      "       0.11442732, 0.11467749, 0.11503344, 0.11499771, 0.1151764 ,\n",
      "       0.11506918, 0.1151764 , 0.11542514, 0.11531792, 0.10110975,\n",
      "       0.10171444, 0.10381586, 0.10435051, 0.10417182, 0.10331696,\n",
      "       0.10303248, 0.10313969, 0.10388734, 0.10413608, 0.1047422 ,\n",
      "       0.10531116, 0.10659346, 0.10648624, 0.10705663, 0.10737684,\n",
      "       0.1073411 , 0.10705663, 0.10684219, 0.10641476, 0.10602307,\n",
      "       0.10559564, 0.10502668, 0.1040646 , 0.1038516 , 0.10342417,\n",
      "       0.10335269, 0.10367291, 0.10399313, 0.10360143, 0.10324548,\n",
      "       0.10249783, 0.10221335, 0.1020704 , 0.10078953, 0.10089532,\n",
      "       0.10089532, 0.10029062, 0.10011193, 0.09989893, 0.09961302,\n",
      "       0.09989893, 0.09975598, 0.09950724, 0.09893685, 0.09804625,\n",
      "       0.09719139, 0.09776177, 0.09790473, 0.09786899, 0.09758451,\n",
      "       0.0976903 , 0.09794047, 0.09861664, 0.0987939 , 0.09950724,\n",
      "       0.09989893, 0.09979172, 0.09972024, 0.09972024, 0.10025489,\n",
      "       0.10032636, 0.10039641, 0.10110975, 0.10125127, 0.10157292,\n",
      "       0.10164297, 0.10132275, 0.10093106, 0.10128701, 0.10135849,\n",
      "       0.10128701, 0.10125127, 0.10135849, 0.10135849, 0.10132275,\n",
      "       0.10132275, 0.10203466, 0.10249783, 0.10246209, 0.10239062,\n",
      "       0.10249783, 0.10239062, 0.10246209, 0.10199892, 0.10182166,\n",
      "       0.10171444, 0.10150144, 0.10196461, 0.10224909, 0.10242635,\n",
      "       0.10260505, 0.10303248, 0.10324548, 0.10324548, 0.10320974,\n",
      "       0.10303248, 0.10338843, 0.10345991, 0.10349565, 0.10417182,\n",
      "       0.10410034, 0.10431477, 0.10442056, 0.10420756, 0.10410034,\n",
      "       0.10392308, 0.10395882, 0.10410034, 0.10410034, 0.10417182,\n",
      "       0.10399313, 0.10356712, 0.10303248, 0.10306822, 0.10288952,\n",
      "       0.10292526, 0.10281805, 0.10285379, 0.10324548, 0.10338843,\n",
      "       0.10328122, 0.10317543, 0.10313969, 0.10288952, 0.10317543,\n",
      "       0.10324548, 0.10317543, 0.10292526, 0.10299674, 0.10253357,\n",
      "       0.10214188, 0.10228483, 0.10249783, 0.10256931, 0.10246209,\n",
      "       0.10260505, 0.10267652, 0.10278374, 0.10264078, 0.10278374,\n",
      "       0.10292526, 0.102748  , 0.10310395, 0.10306822, 0.10303248,\n",
      "       0.10278374, 0.10214188, 0.10199892, 0.10224909, 0.10217761,\n",
      "       0.10256931, 0.10285379, 0.10328122, 0.10310395, 0.10303248,\n",
      "       0.10278374, 0.10303248, 0.10313969, 0.10317543, 0.10320974,\n",
      "       0.10299674, 0.10320974, 0.10342417, 0.10338843, 0.10342417,\n",
      "       0.10353139, 0.10342417, 0.10349565, 0.10353139, 0.10353139,\n",
      "       0.10367291, 0.11499771, 0.11446306, 0.1144988 , 0.11457027,\n",
      "       0.11442732, 0.11457027, 0.11446306, 0.1147847 , 0.11535366,\n",
      "       0.1153894 , 0.11549661, 0.11553235, 0.1156024 , 0.11553235,\n",
      "       0.11553235, 0.11553235, 0.11542514, 0.11549661, 0.1156024 ,\n",
      "       0.11610131, 0.11660022, 0.11692043, 0.11784677, 0.11805977,\n",
      "       0.11820273, 0.11802404, 0.11773956, 0.11802404, 0.11805977,\n",
      "       0.1186659 , 0.11923485, 0.11909333, 0.11880742, 0.11852294,\n",
      "       0.11816699, 0.11813125, 0.11816699, 0.11813125, 0.11802404,\n",
      "       0.11798973, 0.1175966 , 0.1175623 , 0.11741934, 0.11752656,\n",
      "       0.11741934, 0.11734787, 0.11720491, 0.11699191, 0.11681322,\n",
      "       0.11642152, 0.11599409, 0.11620852, 0.11631574, 0.11645726,\n",
      "       0.11631574, 0.11642152, 0.11638722, 0.11674317, 0.11660022,\n",
      "       0.11660022, 0.11645726, 0.11563814, 0.1153894 , 0.11528218,\n",
      "       0.11496197, 0.11471323, 0.11496197, 0.11446306, 0.11403563,\n",
      "       0.1138941 , 0.11385836, 0.11392984, 0.1136082 , 0.11346667,\n",
      "       0.11357389, 0.11382263, 0.11392984, 0.11392984, 0.11378689,\n",
      "       0.11353815, 0.11339519, 0.11318219, 0.11318219, 0.1132165 ,\n",
      "       0.11311072, 0.11328798, 0.11307498, 0.11335946, 0.11325224,\n",
      "       0.11357389, 0.11357389, 0.1138941 , 0.1141071 , 0.11371541,\n",
      "       0.11353815, 0.11353815, 0.11350241, 0.11214864, 0.1118999 ,\n",
      "       0.11186416, 0.11182842, 0.1106176 , 0.10997717, 0.11029739,\n",
      "       0.11065334, 0.11086634, 0.11140099, 0.11129377, 0.1112223 ,\n",
      "       0.11111651, 0.11125804, 0.11136525, 0.1118999 , 0.11193564,\n",
      "       0.11186416, 0.1121129 , 0.11197137, 0.11154394, 0.1112223 ,\n",
      "       0.11129377, 0.11129377, 0.11143673, 0.1112223 , 0.11143673,\n",
      "       0.11140099, 0.11147247, 0.11147247, 0.11143673, 0.11118799,\n",
      "       0.1110093 , 0.11140099, 0.1115082 , 0.11147247, 0.11132951,\n",
      "       0.11143673, 0.11140099, 0.11125804, 0.11108077, 0.11093782,\n",
      "       0.11079487, 0.11054613, 0.10969126, 0.10965552, 0.10894361,\n",
      "       0.10808875, 0.10819597, 0.10865914, 0.10933531, 0.10912231,\n",
      "       0.10919235, 0.10905083, 0.10926383, 0.10894361, 0.10876635,\n",
      "       0.1088364 , 0.10840897, 0.10805301, 0.10766132, 0.10691367,\n",
      "       0.10662919, 0.10616602, 0.10470647, 0.10292526, 0.1018574 ,\n",
      "       0.10196461, 0.10228483, 0.10388734, 0.1047422 , 0.10531116,\n",
      "       0.1051339 , 0.1051339 , 0.10509816, 0.10552559, 0.10559564,\n",
      "       0.10591728, 0.10502668, 0.10488373, 0.10499094, 0.10502668,\n",
      "       0.10545411, 0.10566712, 0.10556133, 0.10570285, 0.10552559,\n",
      "       0.10538264, 0.10523968, 0.10538264, 0.10584581, 0.10581007,\n",
      "       0.10641476, 0.10634472, 0.10648624, 0.10677215, 0.10741258,\n",
      "       0.10755553, 0.1075198 , 0.10702089, 0.10638045, 0.10548985,\n",
      "       0.10556133, 0.10559564, 0.10581007, 0.10598876, 0.10677215,\n",
      "       0.10730536, 0.10784001, 0.10808875, 0.10794723, 0.10801727,\n",
      "       0.10812449, 0.10837466, 0.10848044, 0.10851618, 0.10873061,\n",
      "       0.10919235, 0.11008296, 0.11040317, 0.11076056, 0.11058186,\n",
      "       0.11043891, 0.11011869, 0.10979848, 0.1088364 , 0.10894361,\n",
      "       0.10894361, 0.10915805, 0.10929957, 0.10969126, 0.109514  ,\n",
      "       0.10965552, 0.11011869, 0.11029739, 0.11058186, 0.11058186,\n",
      "       0.11065334, 0.11026165, 0.10986995, 0.10940678, 0.10961979,\n",
      "       0.10905083, 0.10844471, 0.10766132, 0.10737684, 0.10769706,\n",
      "       0.1075198 , 0.1077328 , 0.10791149, 0.10808875, 0.10776854,\n",
      "       0.10726963, 0.10666493, 0.10648624, 0.10613029, 0.10584581,\n",
      "       0.1047422 , 0.10402887, 0.10420756, 0.10495521, 0.1064505 ,\n",
      "       0.10673641, 0.10716384, 0.10702089, 0.10726963, 0.10698515,\n",
      "       0.10748406, 0.10812449, 0.10915805, 0.10958548, 0.11001148,\n",
      "       0.11004722, 0.11015443, 0.11036886, 0.11043891, 0.11022591,\n",
      "       0.11036886, 0.11118799, 0.1112223 , 0.1112223 , 0.1112223 ,\n",
      "       0.1112223 , 0.1112223 , 0.1112223 , 0.1112223 , 0.11001148]), array([0.10378013, 0.1040646 , 0.10417182, 0.10435051, 0.10438625,\n",
      "       0.10435051, 0.10431477, 0.10435051, 0.10452777, 0.10470647,\n",
      "       0.10467073, 0.10527542, 0.10556133, 0.10566712, 0.10541838,\n",
      "       0.10531116, 0.10523968, 0.1051339 , 0.10516964, 0.10516964,\n",
      "       0.1051339 , 0.1051339 , 0.10527542, 0.1051339 , 0.10509816,\n",
      "       0.10509816, 0.10495521, 0.10502668, 0.10488373, 0.10484799,\n",
      "       0.10484799, 0.10484799, 0.10509816, 0.10509816, 0.10520394,\n",
      "       0.10499094, 0.10502668, 0.10491947, 0.10491947, 0.10484799,\n",
      "       0.1047422 , 0.10470647, 0.10459925, 0.1047422 , 0.10481225,\n",
      "       0.10481225, 0.10499094, 0.10484799, 0.10488373, 0.10467073,\n",
      "       0.1044563 , 0.10456351, 0.1044563 , 0.10438625, 0.10438625,\n",
      "       0.10442056, 0.1044563 , 0.10392308, 0.10395882, 0.10402887,\n",
      "       0.10402887, 0.10410034, 0.10413608, 0.10427903, 0.10413608,\n",
      "       0.1040646 , 0.1038516 , 0.10356712, 0.10370865, 0.10353139,\n",
      "       0.10374439, 0.10370865, 0.10335269, 0.10324548, 0.10310395,\n",
      "       0.10299674, 0.102961  , 0.10345991, 0.10367291, 0.10374439,\n",
      "       0.10367291, 0.10353139, 0.10345991, 0.10331696, 0.10313969,\n",
      "       0.10267652, 0.10271226, 0.10281805, 0.10271226, 0.10267652,\n",
      "       0.10292526, 0.10306822, 0.10313969, 0.10281805, 0.10260505,\n",
      "       0.10253357, 0.10235631, 0.10224909, 0.10228483, 0.10153718,\n",
      "       0.10167871, 0.10189314, 0.10203466, 0.1020704 , 0.10224909,\n",
      "       0.1020704 , 0.10192887, 0.10199892, 0.10235631, 0.10267652,\n",
      "       0.10281805, 0.10246209, 0.10242635, 0.10242635, 0.10235631,\n",
      "       0.10235631, 0.10253357, 0.10232057, 0.10232057, 0.10232057,\n",
      "       0.10232057, 0.10224909, 0.10167871, 0.10171444, 0.10142997,\n",
      "       0.10110975, 0.10118123, 0.10125127, 0.10110975, 0.10103827,\n",
      "       0.10004046, 0.09989893, 0.09997041, 0.10004046, 0.10021915,\n",
      "       0.1003621 , 0.10046789, 0.10046789, 0.10043215, 0.10032636,\n",
      "       0.10043215, 0.10039641, 0.10046789, 0.10061084, 0.1005751 ,\n",
      "       0.09997041, 0.09940002, 0.0996845 , 0.09972024, 0.09989893,\n",
      "       0.10025489, 0.10018341, 0.10043215, 0.10050363, 0.10107401,\n",
      "       0.10132275, 0.10121554, 0.10128701, 0.10178592, 0.1018574 ,\n",
      "       0.10210614, 0.10196461, 0.10210614, 0.10239062, 0.10232057,\n",
      "       0.10246209, 0.10285379, 0.10317543, 0.10310395, 0.10313969,\n",
      "       0.10264078, 0.10239062, 0.10203466, 0.10217761, 0.10214188,\n",
      "       0.10224909, 0.10239062, 0.10288952, 0.102961  , 0.10320974,\n",
      "       0.10420756, 0.10495521, 0.10527542, 0.10509816, 0.10531116,\n",
      "       0.1051339 , 0.10509816, 0.10509816, 0.10502668, 0.10506242,\n",
      "       0.10488373, 0.10438625, 0.10452777, 0.10435051, 0.1044563 ,\n",
      "       0.10467073, 0.10481225, 0.1051339 , 0.10556133, 0.10545411,\n",
      "       0.10559564, 0.10566712, 0.10559564, 0.10598876, 0.1071281 ,\n",
      "       0.10744832, 0.10680646, 0.10719815, 0.10726963, 0.10766132,\n",
      "       0.10787575, 0.10808875, 0.10769706, 0.10780427, 0.10784001,\n",
      "       0.10780427, 0.10787575, 0.10791149, 0.10848044, 0.10901509,\n",
      "       0.10922809, 0.10922809, 0.10922809, 0.10922809, 0.10922809,\n",
      "       0.10922809, 0.10922809, 0.11086634, 0.1106176 , 0.11175694,\n",
      "       0.11197137, 0.1115082 , 0.11214864, 0.1121129 , 0.11282481,\n",
      "       0.11193564, 0.11154394, 0.11136525, 0.11079487, 0.10983422,\n",
      "       0.10897935, 0.10776854, 0.10748406, 0.1077328 , 0.10855192,\n",
      "       0.10897935, 0.10947826, 0.11004722, 0.11008296, 0.10961979,\n",
      "       0.10865914, 0.10865914, 0.1086234 , 0.10837466, 0.10784001,\n",
      "       0.10808875, 0.10905083, 0.10965552, 0.11004722, 0.11065334,\n",
      "       0.11111651, 0.11097356, 0.11111651, 0.11104503, 0.1110093 ,\n",
      "       0.11097356, 0.11079487, 0.1108306 , 0.11154394, 0.11271902,\n",
      "       0.11311072, 0.11325224, 0.11353815, 0.11399989, 0.11403563,\n",
      "       0.11350241, 0.11289629, 0.11303924, 0.11339519, 0.11314645,\n",
      "       0.11311072, 0.11296776, 0.11286055, 0.11271902, 0.11307498,\n",
      "       0.11293202, 0.1127905 , 0.11239738, 0.11243312, 0.11236307,\n",
      "       0.11264755, 0.11328798, 0.11425006, 0.11510492, 0.11549661,\n",
      "       0.1156024 , 0.11563814, 0.11563814, 0.1151764 , 0.11464175,\n",
      "       0.1142858 , 0.11407137, 0.11378689, 0.11350241, 0.11382263,\n",
      "       0.11399989, 0.11392984, 0.11382263, 0.11375115, 0.11367967,\n",
      "       0.11364393, 0.11367967, 0.11353815, 0.11375115, 0.11396558,\n",
      "       0.11375115, 0.1138941 , 0.11421432, 0.11457027, 0.11489049,\n",
      "       0.11510492, 0.11521071, 0.1153894 , 0.11510492, 0.11489049,\n",
      "       0.11492623, 0.11464175, 0.11457027, 0.11442732, 0.11425006,\n",
      "       0.1142858 , 0.1141071 , 0.11439301, 0.11432153, 0.11442732,\n",
      "       0.11467749, 0.11503344, 0.11499771, 0.1151764 , 0.11506918,\n",
      "       0.1151764 , 0.11542514, 0.11531792, 0.10110975, 0.10171444,\n",
      "       0.10381586, 0.10435051, 0.10417182, 0.10331696, 0.10303248,\n",
      "       0.10313969, 0.10388734, 0.10413608, 0.1047422 , 0.10531116,\n",
      "       0.10659346, 0.10648624, 0.10705663, 0.10737684, 0.1073411 ,\n",
      "       0.10705663, 0.10684219, 0.10641476, 0.10602307, 0.10559564,\n",
      "       0.10502668, 0.1040646 , 0.1038516 , 0.10342417, 0.10335269,\n",
      "       0.10367291, 0.10399313, 0.10360143, 0.10324548, 0.10249783,\n",
      "       0.10221335, 0.1020704 , 0.10078953, 0.10089532, 0.10089532,\n",
      "       0.10029062, 0.10011193, 0.09989893, 0.09961302, 0.09989893,\n",
      "       0.09975598, 0.09950724, 0.09893685, 0.09804625, 0.09719139,\n",
      "       0.09776177, 0.09790473, 0.09786899, 0.09758451, 0.0976903 ,\n",
      "       0.09794047, 0.09861664, 0.0987939 , 0.09950724, 0.09989893,\n",
      "       0.09979172, 0.09972024, 0.09972024, 0.10025489, 0.10032636,\n",
      "       0.10039641, 0.10110975, 0.10125127, 0.10157292, 0.10164297,\n",
      "       0.10132275, 0.10093106, 0.10128701, 0.10135849, 0.10128701,\n",
      "       0.10125127, 0.10135849, 0.10135849, 0.10132275, 0.10132275,\n",
      "       0.10203466, 0.10249783, 0.10246209, 0.10239062, 0.10249783,\n",
      "       0.10239062, 0.10246209, 0.10199892, 0.10182166, 0.10171444,\n",
      "       0.10150144, 0.10196461, 0.10224909, 0.10242635, 0.10260505,\n",
      "       0.10303248, 0.10324548, 0.10324548, 0.10320974, 0.10303248,\n",
      "       0.10338843, 0.10345991, 0.10349565, 0.10417182, 0.10410034,\n",
      "       0.10431477, 0.10442056, 0.10420756, 0.10410034, 0.10392308,\n",
      "       0.10395882, 0.10410034, 0.10410034, 0.10417182, 0.10399313,\n",
      "       0.10356712, 0.10303248, 0.10306822, 0.10288952, 0.10292526,\n",
      "       0.10281805, 0.10285379, 0.10324548, 0.10338843, 0.10328122,\n",
      "       0.10317543, 0.10313969, 0.10288952, 0.10317543, 0.10324548,\n",
      "       0.10317543, 0.10292526, 0.10299674, 0.10253357, 0.10214188,\n",
      "       0.10228483, 0.10249783, 0.10256931, 0.10246209, 0.10260505,\n",
      "       0.10267652, 0.10278374, 0.10264078, 0.10278374, 0.10292526,\n",
      "       0.102748  , 0.10310395, 0.10306822, 0.10303248, 0.10278374,\n",
      "       0.10214188, 0.10199892, 0.10224909, 0.10217761, 0.10256931,\n",
      "       0.10285379, 0.10328122, 0.10310395, 0.10303248, 0.10278374,\n",
      "       0.10303248, 0.10313969, 0.10317543, 0.10320974, 0.10299674,\n",
      "       0.10320974, 0.10342417, 0.10338843, 0.10342417, 0.10353139,\n",
      "       0.10342417, 0.10349565, 0.10353139, 0.10353139, 0.10367291,\n",
      "       0.11499771, 0.11446306, 0.1144988 , 0.11457027, 0.11442732,\n",
      "       0.11457027, 0.11446306, 0.1147847 , 0.11535366, 0.1153894 ,\n",
      "       0.11549661, 0.11553235, 0.1156024 , 0.11553235, 0.11553235,\n",
      "       0.11553235, 0.11542514, 0.11549661, 0.1156024 , 0.11610131,\n",
      "       0.11660022, 0.11692043, 0.11784677, 0.11805977, 0.11820273,\n",
      "       0.11802404, 0.11773956, 0.11802404, 0.11805977, 0.1186659 ,\n",
      "       0.11923485, 0.11909333, 0.11880742, 0.11852294, 0.11816699,\n",
      "       0.11813125, 0.11816699, 0.11813125, 0.11802404, 0.11798973,\n",
      "       0.1175966 , 0.1175623 , 0.11741934, 0.11752656, 0.11741934,\n",
      "       0.11734787, 0.11720491, 0.11699191, 0.11681322, 0.11642152,\n",
      "       0.11599409, 0.11620852, 0.11631574, 0.11645726, 0.11631574,\n",
      "       0.11642152, 0.11638722, 0.11674317, 0.11660022, 0.11660022,\n",
      "       0.11645726, 0.11563814, 0.1153894 , 0.11528218, 0.11496197,\n",
      "       0.11471323, 0.11496197, 0.11446306, 0.11403563, 0.1138941 ,\n",
      "       0.11385836, 0.11392984, 0.1136082 , 0.11346667, 0.11357389,\n",
      "       0.11382263, 0.11392984, 0.11392984, 0.11378689, 0.11353815,\n",
      "       0.11339519, 0.11318219, 0.11318219, 0.1132165 , 0.11311072,\n",
      "       0.11328798, 0.11307498, 0.11335946, 0.11325224, 0.11357389,\n",
      "       0.11357389, 0.1138941 , 0.1141071 , 0.11371541, 0.11353815,\n",
      "       0.11353815, 0.11350241, 0.11214864, 0.1118999 , 0.11186416,\n",
      "       0.11182842, 0.1106176 , 0.10997717, 0.11029739, 0.11065334,\n",
      "       0.11086634, 0.11140099, 0.11129377, 0.1112223 , 0.11111651,\n",
      "       0.11125804, 0.11136525, 0.1118999 , 0.11193564, 0.11186416,\n",
      "       0.1121129 , 0.11197137, 0.11154394, 0.1112223 , 0.11129377,\n",
      "       0.11129377, 0.11143673, 0.1112223 , 0.11143673, 0.11140099,\n",
      "       0.11147247, 0.11147247, 0.11143673, 0.11118799, 0.1110093 ,\n",
      "       0.11140099, 0.1115082 , 0.11147247, 0.11132951, 0.11143673,\n",
      "       0.11140099, 0.11125804, 0.11108077, 0.11093782, 0.11079487,\n",
      "       0.11054613, 0.10969126, 0.10965552, 0.10894361, 0.10808875,\n",
      "       0.10819597, 0.10865914, 0.10933531, 0.10912231, 0.10919235,\n",
      "       0.10905083, 0.10926383, 0.10894361, 0.10876635, 0.1088364 ,\n",
      "       0.10840897, 0.10805301, 0.10766132, 0.10691367, 0.10662919,\n",
      "       0.10616602, 0.10470647, 0.10292526, 0.1018574 , 0.10196461,\n",
      "       0.10228483, 0.10388734, 0.1047422 , 0.10531116, 0.1051339 ,\n",
      "       0.1051339 , 0.10509816, 0.10552559, 0.10559564, 0.10591728,\n",
      "       0.10502668, 0.10488373, 0.10499094, 0.10502668, 0.10545411,\n",
      "       0.10566712, 0.10556133, 0.10570285, 0.10552559, 0.10538264,\n",
      "       0.10523968, 0.10538264, 0.10584581, 0.10581007, 0.10641476,\n",
      "       0.10634472, 0.10648624, 0.10677215, 0.10741258, 0.10755553,\n",
      "       0.1075198 , 0.10702089, 0.10638045, 0.10548985, 0.10556133,\n",
      "       0.10559564, 0.10581007, 0.10598876, 0.10677215, 0.10730536,\n",
      "       0.10784001, 0.10808875, 0.10794723, 0.10801727, 0.10812449,\n",
      "       0.10837466, 0.10848044, 0.10851618, 0.10873061, 0.10919235,\n",
      "       0.11008296, 0.11040317, 0.11076056, 0.11058186, 0.11043891,\n",
      "       0.11011869, 0.10979848, 0.1088364 , 0.10894361, 0.10894361,\n",
      "       0.10915805, 0.10929957, 0.10969126, 0.109514  , 0.10965552,\n",
      "       0.11011869, 0.11029739, 0.11058186, 0.11058186, 0.11065334,\n",
      "       0.11026165, 0.10986995, 0.10940678, 0.10961979, 0.10905083,\n",
      "       0.10844471, 0.10766132, 0.10737684, 0.10769706, 0.1075198 ,\n",
      "       0.1077328 , 0.10791149, 0.10808875, 0.10776854, 0.10726963,\n",
      "       0.10666493, 0.10648624, 0.10613029, 0.10584581, 0.1047422 ,\n",
      "       0.10402887, 0.10420756, 0.10495521, 0.1064505 , 0.10673641,\n",
      "       0.10716384, 0.10702089, 0.10726963, 0.10698515, 0.10748406,\n",
      "       0.10812449, 0.10915805, 0.10958548, 0.11001148, 0.11004722,\n",
      "       0.11015443, 0.11036886, 0.11043891, 0.11022591, 0.11036886,\n",
      "       0.11118799, 0.1112223 , 0.1112223 , 0.1112223 , 0.1112223 ,\n",
      "       0.1112223 , 0.1112223 , 0.1112223 , 0.11001148, 0.10719815])]\n",
      "[0.10719814960680576, 0.10666493216130324]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Split the data into x_train and y_train data sets\n",
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(800, len(train_data)):\n",
    "    x_train.append(train_data[i-800:i])\n",
    "    y_train.append(train_data[i])\n",
    "    if i<= 801:\n",
    "        print(x_train)\n",
    "        print(y_train)\n",
    "        print()\n",
    "        \n",
    "# Convert the x_train and y_train to numpy arrays \n",
    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
    "\n",
    "# Reshape the data\n",
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "# x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "2047/2047 [==============================] - ETA: 0s - loss: 3.3293e-04\n",
      "Epoch 1: saving model to model/model_checkpoint_epoch_01.h5\n",
      "2047/2047 [==============================] - 5603s 3s/step - loss: 3.3293e-04\n",
      "Epoch 2/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2047/2047 [==============================] - ETA: 0s - loss: 4.4899e-06\n",
      "Epoch 2: saving model to model/model_checkpoint_epoch_02.h5\n",
      "2047/2047 [==============================] - 5581s 3s/step - loss: 4.4899e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7ff04db139d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build the LSTM model\n",
    "model = Sequential()\n",
    "model.add(LSTM(128, return_sequences=True, input_shape= (x_train.shape[1], 1)))\n",
    "model.add(LSTM(64, return_sequences=False))\n",
    "model.add(Dense(25))\n",
    "model.add(Dense(1))\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "checkpoint_callback = ModelCheckpoint(\n",
    "    filepath='model/model_checkpoint_epoch_{epoch:02d}.h5',  # I have added an epoch format to save with the epoch number\n",
    "    save_best_only=False,  # If set to True, only saves when the model is considered the \"best\"\n",
    "    verbose=1  # Verbosity mode\n",
    ")\n",
    "\n",
    "# Train the model\n",
    "model.fit(x_train, y_train, batch_size=512, epochs=2, callbacks=[checkpoint_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('model/lstm_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('model/lstm_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "850"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.utils import clean_list, resample_non_drop\n",
    "\n",
    "test_df = pd.read_excel('data/OIL_WEEKLY.xlsx', engine='openpyxl')\n",
    "test_list = test_df[test_df.columns[1]].tolist()\n",
    "test_list = clean_list(test_list)\n",
    "len(test_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fee26df6080>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if len(test_list) != 1000:\n",
    "    test_list = resample_non_drop(test_list, 1000)\n",
    "\n",
    "plt.plot(test_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_max = np.max(test_list)\n",
    "test_min = np.min(test_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fee26ce87c0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data = (test_list - test_min) / (test_max - test_min)\n",
    "plt.plot(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 649ms/step\n",
      "0.17811513\n",
      "1/1 [==============================] - 0s 69ms/step\n",
      "0.1806316\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.18240291\n",
      "1/1 [==============================] - 0s 65ms/step\n",
      "0.18368119\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.1846377\n",
      "1/1 [==============================] - 0s 59ms/step\n",
      "0.18538755\n",
      "1/1 [==============================] - 0s 60ms/step\n",
      "0.1860074\n",
      "1/1 [==============================] - 0s 61ms/step\n",
      "0.18654715\n",
      "1/1 [==============================] - 0s 61ms/step\n",
      "0.18703909\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.1875035\n",
      "1/1 [==============================] - 0s 61ms/step\n",
      "0.18795289\n",
      "1/1 [==============================] - 0s 61ms/step\n",
      "0.18839473\n",
      "1/1 [==============================] - 0s 62ms/step\n",
      "0.18883327\n",
      "1/1 [==============================] - 0s 67ms/step\n",
      "0.18927081\n",
      "1/1 [==============================] - 0s 60ms/step\n",
      "0.18970855\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.19014706\n",
      "1/1 [==============================] - 0s 60ms/step\n",
      "0.19058642\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.19102666\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.19146772\n",
      "1/1 [==============================] - 0s 67ms/step\n",
      "0.1919095\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.19235191\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.1927949\n",
      "1/1 [==============================] - 0s 62ms/step\n",
      "0.19323844\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.19368248\n",
      "1/1 [==============================] - 0s 69ms/step\n",
      "0.19412702\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.19457209\n",
      "1/1 [==============================] - 0s 65ms/step\n",
      "0.1950176\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.1954637\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.19591026\n",
      "1/1 [==============================] - 0s 69ms/step\n",
      "0.19635734\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.19680497\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.19725306\n",
      "1/1 [==============================] - 0s 60ms/step\n",
      "0.19770175\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.19815096\n",
      "1/1 [==============================] - 0s 61ms/step\n",
      "0.19860071\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.19905096\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.19950177\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.19995312\n",
      "1/1 [==============================] - 0s 62ms/step\n",
      "0.20040502\n",
      "1/1 [==============================] - 0s 68ms/step\n",
      "0.20085746\n",
      "1/1 [==============================] - 0s 66ms/step\n",
      "0.20131041\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.2017639\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.20221801\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.20267259\n",
      "1/1 [==============================] - 0s 60ms/step\n",
      "0.20312768\n",
      "1/1 [==============================] - 0s 60ms/step\n",
      "0.2035834\n",
      "1/1 [==============================] - 0s 61ms/step\n",
      "0.20403963\n",
      "1/1 [==============================] - 0s 62ms/step\n",
      "0.20449638\n",
      "1/1 [==============================] - 0s 65ms/step\n",
      "0.20495373\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.2054116\n",
      "1/1 [==============================] - 0s 62ms/step\n",
      "0.20587003\n",
      "1/1 [==============================] - 0s 62ms/step\n",
      "0.206329\n",
      "1/1 [==============================] - 0s 68ms/step\n",
      "0.20678851\n",
      "1/1 [==============================] - 0s 69ms/step\n",
      "0.20724858\n",
      "1/1 [==============================] - 0s 65ms/step\n",
      "0.20770924\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "0.20817046\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.20863222\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.20909452\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "0.20955738\n",
      "1/1 [==============================] - 0s 65ms/step\n",
      "0.21002086\n",
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    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fee27764d00>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_list = test_data[:800].tolist()\n",
    "\n",
    "for i in range(200):\n",
    "    x = np.array(prediction_list[-800:]).reshape(1, 800, 1)\n",
    "    predicted_price = model.predict(x)[0][0]\n",
    "    print(predicted_price)\n",
    "    prediction_list.append(predicted_price)\n",
    "\n",
    "plt.plot(prediction_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m16\u001b[39m,\u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(test_data, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTest\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m800\u001b[39m, \u001b[38;5;241m1000\u001b[39m), prediction_list[\u001b[38;5;241m800\u001b[39m:], label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrediction\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(test_data, label='Test', color='blue')\n",
    "plt.plot(np.arange(800, 1000), prediction_list[800:], label='Prediction', color='red')\n",
    "plt.grid(True)"
   ]
  }
 ],
 "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}