Urban-Traffic-Time-Series-Forecasting-DL Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "daf91751",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.239590Z",
     "iopub.status.busy": "2023-05-15T08:36:39.238564Z",
     "iopub.status.idle": "2023-05-15T08:36:39.338164Z",
     "shell.execute_reply": "2023-05-15T08:36:39.337253Z"
    },
    "papermill": {
     "duration": 0.112517,
     "end_time": "2023-05-15T08:36:39.341228",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.228711",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d4802a",
   "metadata": {
    "papermill": {
     "duration": 0.006089,
     "end_time": "2023-05-15T08:36:39.353746",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.347657",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Graph Neural Networks: An overview\n",
    "->https://towardsdatascience.com/graph-neural-networks-an-overview-dfd363b6ef87"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e3fc5bf",
   "metadata": {
    "papermill": {
     "duration": 0.006876,
     "end_time": "2023-05-15T08:36:39.366782",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.359906",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Notebook Credit to https://www.linkedin.com/in/joshua-adrian-cahyono-5230b814b/?original_referer="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6004f81d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.380870Z",
     "iopub.status.busy": "2023-05-15T08:36:39.380528Z",
     "iopub.status.idle": "2023-05-15T08:36:39.664536Z",
     "shell.execute_reply": "2023-05-15T08:36:39.663575Z"
    },
    "papermill": {
     "duration": 0.294012,
     "end_time": "2023-05-15T08:36:39.667285",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.373273",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "filename = \"/kaggle/input/urban-traffic-dataset-ml-practice-work/Urban traffic_dataset/traffic_dataset.mat\"\n",
    "mat_contents = sio.loadmat(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47868eb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.681757Z",
     "iopub.status.busy": "2023-05-15T08:36:39.681432Z",
     "iopub.status.idle": "2023-05-15T08:36:39.706765Z",
     "shell.execute_reply": "2023-05-15T08:36:39.705870Z"
    },
    "papermill": {
     "duration": 0.045999,
     "end_time": "2023-05-15T08:36:39.719677",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.673678",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__header__': b'MATLAB 5.0 MAT-file, Platform: PCWIN64, Created on: Sat Mar 14 15:49:14 2020', '__version__': '1.0', '__globals__': [], 'tra_X_tr': array([[<36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        ...,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>]],\n",
      "      dtype=object), 'tra_X_te': array([[<36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 539 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>,\n",
      "        <36x48 sparse matrix of type '<class 'numpy.float64'>'\n",
      "        \twith 540 stored elements in Compressed Sparse Column format>]],\n",
      "      dtype=object), 'tra_Y_tr': array([[0.05091079, 0.04764129, 0.04483886, ..., 0.37132181, 0.42456796,\n",
      "        0.42269967],\n",
      "       [0.04624007, 0.04950957, 0.052312  , ..., 0.37085474, 0.43811303,\n",
      "        0.50116768],\n",
      "       [0.05044372, 0.04437179, 0.05044372, ..., 0.25502102, 0.40168146,\n",
      "        0.37926203],\n",
      "       ...,\n",
      "       [0.02709014, 0.01868286, 0.02568893, ..., 0.10322279, 0.13872022,\n",
      "        0.1424568 ],\n",
      "       [0.03409622, 0.02195236, 0.03549743, ..., 0.15787015, 0.20364316,\n",
      "        0.21064923],\n",
      "       [0.04063522, 0.02989257, 0.05651565, ..., 0.91032228, 0.75198505,\n",
      "        0.83839327]]), 'tra_Y_te': array([[0.40168146, 0.38019617, 0.35544138, ..., 0.052312  , 0.0555815 ,\n",
      "        0.04203643],\n",
      "       [0.57309668, 0.61933676, 0.58991126, ..., 0.09107894, 0.07192901,\n",
      "        0.07239608],\n",
      "       [0.39701074, 0.41008874, 0.36384867, ..., 0.07940215, 0.06071929,\n",
      "        0.05931808],\n",
      "       ...,\n",
      "       [0.13311537, 0.19663709, 0.1914993 , ..., 0.08547408, 0.07800093,\n",
      "        0.06725829],\n",
      "       [0.20644559, 0.29332088, 0.30406352, ..., 0.12330687, 0.09388136,\n",
      "        0.09715086],\n",
      "       [0.76506305, 0.81363849, 0.79495563, ..., 0.06819243, 0.05277908,\n",
      "        0.04437179]]), 'tra_adj_mat': array([[0, 1, 0, ..., 0, 0, 0],\n",
      "       [1, 0, 1, ..., 0, 0, 0],\n",
      "       [0, 1, 0, ..., 0, 0, 0],\n",
      "       ...,\n",
      "       [0, 0, 0, ..., 0, 1, 0],\n",
      "       [0, 0, 0, ..., 1, 0, 0],\n",
      "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)}\n"
     ]
    }
   ],
   "source": [
    "print(mat_contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2778f986",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.741353Z",
     "iopub.status.busy": "2023-05-15T08:36:39.740997Z",
     "iopub.status.idle": "2023-05-15T08:36:39.746023Z",
     "shell.execute_reply": "2023-05-15T08:36:39.745114Z"
    },
    "papermill": {
     "duration": 0.018134,
     "end_time": "2023-05-15T08:36:39.749766",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.731632",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['__header__', '__version__', '__globals__', 'tra_X_tr', 'tra_X_te', 'tra_Y_tr', 'tra_Y_te', 'tra_adj_mat'])\n"
     ]
    }
   ],
   "source": [
    "print(mat_contents.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "581f620a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.768827Z",
     "iopub.status.busy": "2023-05-15T08:36:39.768019Z",
     "iopub.status.idle": "2023-05-15T08:36:39.774866Z",
     "shell.execute_reply": "2023-05-15T08:36:39.773990Z"
    },
    "papermill": {
     "duration": 0.018471,
     "end_time": "2023-05-15T08:36:39.777332",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.758861",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 0)\t0.09248014946286783\n",
      "  (1, 0)\t0.09715086408220458\n",
      "  (2, 0)\t0.11536665109761794\n",
      "  (3, 0)\t0.1120971508640822\n",
      "  (4, 0)\t0.12704343764595982\n",
      "  (5, 0)\t0.12517515179822514\n",
      "  (6, 0)\t0.14432508173750583\n",
      "  (7, 0)\t0.09855207846800561\n",
      "  (8, 0)\t0.14946286781877627\n",
      "  (9, 0)\t0.18262494161606727\n",
      "  (10, 0)\t0.14385801027557216\n",
      "  (11, 0)\t0.15973843998131715\n",
      "  (12, 0)\t0.16814572629612332\n",
      "  (13, 0)\t0.15366651097617937\n",
      "  (14, 0)\t0.19570294255021017\n",
      "  (15, 0)\t0.09528257823446988\n",
      "  (16, 0)\t0.1742176553012611\n",
      "  (17, 0)\t0.23353573096683794\n",
      "  (18, 0)\t0.1938346567024755\n",
      "  (19, 0)\t0.2606258757589911\n",
      "  (20, 0)\t0.2554880896777207\n",
      "  (21, 0)\t0.2727697337692667\n",
      "  (22, 0)\t0.067725361980383\n",
      "  (23, 0)\t0.05558150397010743\n",
      "  (24, 0)\t0.031293787949556284\n",
      "  :\t:\n",
      "  (22, 44)\t1.0\n",
      "  (23, 44)\t1.0\n",
      "  (24, 44)\t1.0\n",
      "  (25, 44)\t1.0\n",
      "  (4, 45)\t1.0\n",
      "  (5, 45)\t1.0\n",
      "  (6, 45)\t1.0\n",
      "  (7, 45)\t1.0\n",
      "  (15, 45)\t1.0\n",
      "  (16, 45)\t1.0\n",
      "  (17, 45)\t1.0\n",
      "  (18, 45)\t1.0\n",
      "  (19, 45)\t1.0\n",
      "  (20, 45)\t1.0\n",
      "  (21, 45)\t1.0\n",
      "  (26, 46)\t1.0\n",
      "  (27, 46)\t1.0\n",
      "  (28, 46)\t1.0\n",
      "  (29, 46)\t1.0\n",
      "  (35, 46)\t1.0\n",
      "  (30, 47)\t1.0\n",
      "  (31, 47)\t1.0\n",
      "  (32, 47)\t1.0\n",
      "  (33, 47)\t1.0\n",
      "  (34, 47)\t1.0\n"
     ]
    }
   ],
   "source": [
    "print(mat_contents['tra_X_tr'][0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f180f630",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.795782Z",
     "iopub.status.busy": "2023-05-15T08:36:39.795073Z",
     "iopub.status.idle": "2023-05-15T08:36:39.800881Z",
     "shell.execute_reply": "2023-05-15T08:36:39.800074Z"
    },
    "papermill": {
     "duration": 0.01716,
     "end_time": "2023-05-15T08:36:39.802841",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.785681",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train = mat_contents['tra_X_tr']\n",
    "y_train = mat_contents['tra_Y_tr']\n",
    "X_test = mat_contents['tra_X_te']\n",
    "y_test = mat_contents['tra_Y_te']\n",
    "adj_mat = mat_contents['tra_adj_mat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bf105bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.821161Z",
     "iopub.status.busy": "2023-05-15T08:36:39.820314Z",
     "iopub.status.idle": "2023-05-15T08:36:39.826460Z",
     "shell.execute_reply": "2023-05-15T08:36:39.825391Z"
    },
    "papermill": {
     "duration": 0.017215,
     "end_time": "2023-05-15T08:36:39.828380",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.811165",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1261)\n",
      "  (0, 0)\t0.09248014946286783\n",
      "  (1, 0)\t0.09715086408220458\n",
      "  (2, 0)\t0.11536665109761794\n",
      "  (3, 0)\t0.1120971508640822\n",
      "  (4, 0)\t0.12704343764595982\n",
      "  (5, 0)\t0.12517515179822514\n",
      "  (6, 0)\t0.14432508173750583\n",
      "  (7, 0)\t0.09855207846800561\n",
      "  (8, 0)\t0.14946286781877627\n",
      "  (9, 0)\t0.18262494161606727\n",
      "  (10, 0)\t0.14385801027557216\n",
      "  (11, 0)\t0.15973843998131715\n",
      "  (12, 0)\t0.16814572629612332\n",
      "  (13, 0)\t0.15366651097617937\n",
      "  (14, 0)\t0.19570294255021017\n",
      "  (15, 0)\t0.09528257823446988\n",
      "  (16, 0)\t0.1742176553012611\n",
      "  (17, 0)\t0.23353573096683794\n",
      "  (18, 0)\t0.1938346567024755\n",
      "  (19, 0)\t0.2606258757589911\n",
      "  (20, 0)\t0.2554880896777207\n",
      "  (21, 0)\t0.2727697337692667\n",
      "  (22, 0)\t0.067725361980383\n",
      "  (23, 0)\t0.05558150397010743\n",
      "  (24, 0)\t0.031293787949556284\n",
      "  :\t:\n",
      "  (22, 44)\t1.0\n",
      "  (23, 44)\t1.0\n",
      "  (24, 44)\t1.0\n",
      "  (25, 44)\t1.0\n",
      "  (4, 45)\t1.0\n",
      "  (5, 45)\t1.0\n",
      "  (6, 45)\t1.0\n",
      "  (7, 45)\t1.0\n",
      "  (15, 45)\t1.0\n",
      "  (16, 45)\t1.0\n",
      "  (17, 45)\t1.0\n",
      "  (18, 45)\t1.0\n",
      "  (19, 45)\t1.0\n",
      "  (20, 45)\t1.0\n",
      "  (21, 45)\t1.0\n",
      "  (26, 46)\t1.0\n",
      "  (27, 46)\t1.0\n",
      "  (28, 46)\t1.0\n",
      "  (29, 46)\t1.0\n",
      "  (35, 46)\t1.0\n",
      "  (30, 47)\t1.0\n",
      "  (31, 47)\t1.0\n",
      "  (32, 47)\t1.0\n",
      "  (33, 47)\t1.0\n",
      "  (34, 47)\t1.0\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_train[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e342caa9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.848708Z",
     "iopub.status.busy": "2023-05-15T08:36:39.848147Z",
     "iopub.status.idle": "2023-05-15T08:36:39.853759Z",
     "shell.execute_reply": "2023-05-15T08:36:39.852851Z"
    },
    "papermill": {
     "duration": 0.017534,
     "end_time": "2023-05-15T08:36:39.856343",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.838809",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 1261)\n",
      "[[0.05091079 0.04764129 0.04483886 ... 0.37132181 0.42456796 0.42269967]\n",
      " [0.04624007 0.04950957 0.052312   ... 0.37085474 0.43811303 0.50116768]\n",
      " [0.05044372 0.04437179 0.05044372 ... 0.25502102 0.40168146 0.37926203]\n",
      " ...\n",
      " [0.02709014 0.01868286 0.02568893 ... 0.10322279 0.13872022 0.1424568 ]\n",
      " [0.03409622 0.02195236 0.03549743 ... 0.15787015 0.20364316 0.21064923]\n",
      " [0.04063522 0.02989257 0.05651565 ... 0.91032228 0.75198505 0.83839327]]\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ddd3b50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.875432Z",
     "iopub.status.busy": "2023-05-15T08:36:39.874577Z",
     "iopub.status.idle": "2023-05-15T08:36:39.881268Z",
     "shell.execute_reply": "2023-05-15T08:36:39.880115Z"
    },
    "papermill": {
     "duration": 0.018583,
     "end_time": "2023-05-15T08:36:39.883489",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.864906",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 840)\n",
      "  (0, 0)\t0.05698271835590846\n",
      "  (1, 0)\t0.04950957496496964\n",
      "  (2, 0)\t0.05371321812237272\n",
      "  (3, 0)\t0.04904250350303596\n",
      "  (4, 0)\t0.05044371788883699\n",
      "  (5, 0)\t0.03923400280242877\n",
      "  (6, 0)\t0.0406352171882298\n",
      "  (7, 0)\t0.033162073797290983\n",
      "  (8, 0)\t0.12424100887435778\n",
      "  (9, 0)\t0.12097150864082204\n",
      "  (10, 0)\t0.10415693601120972\n",
      "  (11, 0)\t0.10602522185894442\n",
      "  (12, 0)\t0.12330686595049042\n",
      "  (13, 0)\t0.1200373657169547\n",
      "  (14, 0)\t0.11396543671181691\n",
      "  (15, 0)\t0.038766931340495095\n",
      "  (16, 0)\t0.07192900513778608\n",
      "  (17, 0)\t0.07753386268099019\n",
      "  (18, 0)\t0.0751985053713218\n",
      "  (19, 0)\t0.09201307800093414\n",
      "  (20, 0)\t0.08874357776739841\n",
      "  (21, 0)\t0.1200373657169547\n",
      "  (22, 0)\t0.03549743110695937\n",
      "  (23, 0)\t0.03409621672115834\n",
      "  (24, 0)\t0.014479215319943952\n",
      "  :\t:\n",
      "  (22, 44)\t1.0\n",
      "  (23, 44)\t1.0\n",
      "  (24, 44)\t1.0\n",
      "  (25, 44)\t1.0\n",
      "  (4, 45)\t1.0\n",
      "  (5, 45)\t1.0\n",
      "  (6, 45)\t1.0\n",
      "  (7, 45)\t1.0\n",
      "  (15, 45)\t1.0\n",
      "  (16, 45)\t1.0\n",
      "  (17, 45)\t1.0\n",
      "  (18, 45)\t1.0\n",
      "  (19, 45)\t1.0\n",
      "  (20, 45)\t1.0\n",
      "  (21, 45)\t1.0\n",
      "  (26, 46)\t1.0\n",
      "  (27, 46)\t1.0\n",
      "  (28, 46)\t1.0\n",
      "  (29, 46)\t1.0\n",
      "  (35, 46)\t1.0\n",
      "  (30, 47)\t1.0\n",
      "  (31, 47)\t1.0\n",
      "  (32, 47)\t1.0\n",
      "  (33, 47)\t1.0\n",
      "  (34, 47)\t1.0\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "print(X_test[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b913c338",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.903582Z",
     "iopub.status.busy": "2023-05-15T08:36:39.902978Z",
     "iopub.status.idle": "2023-05-15T08:36:39.909697Z",
     "shell.execute_reply": "2023-05-15T08:36:39.908347Z"
    },
    "papermill": {
     "duration": 0.01857,
     "end_time": "2023-05-15T08:36:39.911672",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.893102",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 840)\n",
      "[[0.40168146 0.38019617 0.35544138 ... 0.052312   0.0555815  0.04203643]\n",
      " [0.57309668 0.61933676 0.58991126 ... 0.09107894 0.07192901 0.07239608]\n",
      " [0.39701074 0.41008874 0.36384867 ... 0.07940215 0.06071929 0.05931808]\n",
      " ...\n",
      " [0.13311537 0.19663709 0.1914993  ... 0.08547408 0.07800093 0.06725829]\n",
      " [0.20644559 0.29332088 0.30406352 ... 0.12330687 0.09388136 0.09715086]\n",
      " [0.76506305 0.81363849 0.79495563 ... 0.06819243 0.05277908 0.04437179]]\n"
     ]
    }
   ],
   "source": [
    "print(y_test.shape)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7ef9612",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.932480Z",
     "iopub.status.busy": "2023-05-15T08:36:39.931659Z",
     "iopub.status.idle": "2023-05-15T08:36:39.938071Z",
     "shell.execute_reply": "2023-05-15T08:36:39.936850Z"
    },
    "papermill": {
     "duration": 0.019085,
     "end_time": "2023-05-15T08:36:39.940372",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.921287",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 36)\n",
      "[[0 1 0 ... 0 0 0]\n",
      " [1 0 1 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 1 0]\n",
      " [0 0 0 ... 1 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(adj_mat.shape)\n",
    "print(adj_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "420e4678",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.963344Z",
     "iopub.status.busy": "2023-05-15T08:36:39.963035Z",
     "iopub.status.idle": "2023-05-15T08:36:39.969924Z",
     "shell.execute_reply": "2023-05-15T08:36:39.969040Z"
    },
    "papermill": {
     "duration": 0.019301,
     "end_time": "2023-05-15T08:36:39.972177",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.952876",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['prev_1', 'prev_2', 'prev_3', 'prev_4', 'prev_5', 'prev_6', 'prev_7', 'prev_8', 'prev_9', 'prev_10', 'feata_1', 'feata_2', 'feata_3', 'feata_4', 'feata_5', 'hour_1', 'hour_2', 'hour_3', 'hour_4', 'hour_5', 'hour_6', 'hour_7', 'hour_8', 'hour_9', 'hour_10', 'hour_11', 'hour_12', 'hour_13', 'hour_14', 'hour_15', 'hour_16', 'hour_17', 'hour_18', 'hour_19', 'hour_20', 'hour_21', 'hour_22', 'hour_23', 'hour_24', 'no_roads', 'featb_1', 'featb_2', 'featb_3', 'featb_4', 'featc_1', 'featc_2', 'featc_3', 'featc_4']\n"
     ]
    }
   ],
   "source": [
    "feat_name = []\n",
    "for i in range(10):\n",
    "    feat_name.append(f'prev_{i+1}')\n",
    "\n",
    "for i in range(5):\n",
    "    feat_name.append(f'feata_{i+1}')\n",
    "    \n",
    "for i in range(24):\n",
    "    feat_name.append(f'hour_{i+1}')\n",
    "    \n",
    "feat_name.append(f'no_roads')\n",
    "\n",
    "for i in range(4):\n",
    "    feat_name.append(f'featb_{i+1}')\n",
    "    \n",
    "for i in range(4):\n",
    "    feat_name.append(f'featc_{i+1}')\n",
    "    \n",
    "print(feat_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47157afc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:39.991694Z",
     "iopub.status.busy": "2023-05-15T08:36:39.991154Z",
     "iopub.status.idle": "2023-05-15T08:36:41.522966Z",
     "shell.execute_reply": "2023-05-15T08:36:41.521860Z"
    },
    "papermill": {
     "duration": 1.543768,
     "end_time": "2023-05-15T08:36:41.525085",
     "exception": false,
     "start_time": "2023-05-15T08:36:39.981317",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1261it [00:01, 837.46it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'timestep': 0,\n",
       "  'location': 0,\n",
       "  'traffic': 0.05091078935077067,\n",
       "  'prev_1': 0.09248014946286783,\n",
       "  'prev_2': 0.0966837926202709,\n",
       "  'prev_3': 0.07006071929005138,\n",
       "  'prev_4': 0.0658570761326483,\n",
       "  'prev_5': 0.07846800560485755,\n",
       "  'prev_6': 0.07146193367585241,\n",
       "  'prev_7': 0.0541802895843064,\n",
       "  'prev_8': 0.044371788883699206,\n",
       "  'prev_9': 0.05464736104624007,\n",
       "  'prev_10': 0.05091078935077067,\n",
       "  'feata_1': 1.0,\n",
       "  'feata_2': 0.0,\n",
       "  'feata_3': 0.0,\n",
       "  'feata_4': 0.0,\n",
       "  'feata_5': 0.0,\n",
       "  'hour_1': 1.0,\n",
       "  'hour_2': 0.0,\n",
       "  'hour_3': 0.0,\n",
       "  'hour_4': 0.0,\n",
       "  'hour_5': 0.0,\n",
       "  'hour_6': 0.0,\n",
       "  'hour_7': 0.0,\n",
       "  'hour_8': 0.0,\n",
       "  'hour_9': 0.0,\n",
       "  'hour_10': 0.0,\n",
       "  'hour_11': 0.0,\n",
       "  'hour_12': 0.0,\n",
       "  'hour_13': 0.0,\n",
       "  'hour_14': 0.0,\n",
       "  'hour_15': 0.0,\n",
       "  'hour_16': 0.0,\n",
       "  'hour_17': 0.0,\n",
       "  'hour_18': 0.0,\n",
       "  'hour_19': 0.0,\n",
       "  'hour_20': 0.0,\n",
       "  'hour_21': 0.0,\n",
       "  'hour_22': 0.0,\n",
       "  'hour_23': 0.0,\n",
       "  'hour_24': 0.0,\n",
       "  'no_roads': 3.0,\n",
       "  'featb_1': 1.0,\n",
       "  'featb_2': 0.0,\n",
       "  'featb_3': 0.0,\n",
       "  'featb_4': 0.0,\n",
       "  'featc_1': 1.0,\n",
       "  'featc_2': 0.0,\n",
       "  'featc_3': 0.0,\n",
       "  'featc_4': 0.0},\n",
       " {'timestep': 0,\n",
       "  'location': 1,\n",
       "  'traffic': 0.04624007473143391,\n",
       "  'prev_1': 0.09715086408220458,\n",
       "  'prev_2': 0.102288650163475,\n",
       "  'prev_3': 0.08173750583839327,\n",
       "  'prev_4': 0.07659971975712283,\n",
       "  'prev_5': 0.07239607659971976,\n",
       "  'prev_6': 0.067725361980383,\n",
       "  'prev_7': 0.058851004203643156,\n",
       "  'prev_8': 0.06632414759458197,\n",
       "  'prev_9': 0.059785147127510506,\n",
       "  'prev_10': 0.05698271835590846,\n",
       "  'feata_1': 1.0,\n",
       "  'feata_2': 0.0,\n",
       "  'feata_3': 0.0,\n",
       "  'feata_4': 0.0,\n",
       "  'feata_5': 0.0,\n",
       "  'hour_1': 1.0,\n",
       "  'hour_2': 0.0,\n",
       "  'hour_3': 0.0,\n",
       "  'hour_4': 0.0,\n",
       "  'hour_5': 0.0,\n",
       "  'hour_6': 0.0,\n",
       "  'hour_7': 0.0,\n",
       "  'hour_8': 0.0,\n",
       "  'hour_9': 0.0,\n",
       "  'hour_10': 0.0,\n",
       "  'hour_11': 0.0,\n",
       "  'hour_12': 0.0,\n",
       "  'hour_13': 0.0,\n",
       "  'hour_14': 0.0,\n",
       "  'hour_15': 0.0,\n",
       "  'hour_16': 0.0,\n",
       "  'hour_17': 0.0,\n",
       "  'hour_18': 0.0,\n",
       "  'hour_19': 0.0,\n",
       "  'hour_20': 0.0,\n",
       "  'hour_21': 0.0,\n",
       "  'hour_22': 0.0,\n",
       "  'hour_23': 0.0,\n",
       "  'hour_24': 0.0,\n",
       "  'no_roads': 4.0,\n",
       "  'featb_1': 1.0,\n",
       "  'featb_2': 0.0,\n",
       "  'featb_3': 0.0,\n",
       "  'featb_4': 0.0,\n",
       "  'featc_1': 1.0,\n",
       "  'featc_2': 0.0,\n",
       "  'featc_3': 0.0,\n",
       "  'featc_4': 0.0},\n",
       " {'timestep': 0,\n",
       "  'location': 2,\n",
       "  'traffic': 0.05044371788883699,\n",
       "  'prev_1': 0.11536665109761794,\n",
       "  'prev_2': 0.11069593647828117,\n",
       "  'prev_3': 0.10555815039701075,\n",
       "  'prev_4': 0.09528257823446988,\n",
       "  'prev_5': 0.067725361980383,\n",
       "  'prev_6': 0.08033629145259225,\n",
       "  'prev_7': 0.06258757589911257,\n",
       "  'prev_8': 0.06165343297524521,\n",
       "  'prev_9': 0.05558150397010743,\n",
       "  'prev_10': 0.06165343297524521,\n",
       "  'feata_1': 1.0,\n",
       "  'feata_2': 0.0,\n",
       "  'feata_3': 0.0,\n",
       "  'feata_4': 0.0,\n",
       "  'feata_5': 0.0,\n",
       "  'hour_1': 1.0,\n",
       "  'hour_2': 0.0,\n",
       "  'hour_3': 0.0,\n",
       "  'hour_4': 0.0,\n",
       "  'hour_5': 0.0,\n",
       "  'hour_6': 0.0,\n",
       "  'hour_7': 0.0,\n",
       "  'hour_8': 0.0,\n",
       "  'hour_9': 0.0,\n",
       "  'hour_10': 0.0,\n",
       "  'hour_11': 0.0,\n",
       "  'hour_12': 0.0,\n",
       "  'hour_13': 0.0,\n",
       "  'hour_14': 0.0,\n",
       "  'hour_15': 0.0,\n",
       "  'hour_16': 0.0,\n",
       "  'hour_17': 0.0,\n",
       "  'hour_18': 0.0,\n",
       "  'hour_19': 0.0,\n",
       "  'hour_20': 0.0,\n",
       "  'hour_21': 0.0,\n",
       "  'hour_22': 0.0,\n",
       "  'hour_23': 0.0,\n",
       "  'hour_24': 0.0,\n",
       "  'no_roads': 3.0,\n",
       "  'featb_1': 1.0,\n",
       "  'featb_2': 0.0,\n",
       "  'featb_3': 0.0,\n",
       "  'featb_4': 0.0,\n",
       "  'featc_1': 1.0,\n",
       "  'featc_2': 0.0,\n",
       "  'featc_3': 0.0,\n",
       "  'featc_4': 0.0}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_list = []\n",
    "for timestep, feat_mat in tqdm.tqdm(enumerate(X_train[0])):\n",
    "    feat_mat = feat_mat.toarray()\n",
    "    for i in range(len(feat_mat)):\n",
    "        row_dict = {\n",
    "            \"timestep\" : timestep,\n",
    "            \"location\" : i,\n",
    "            \"traffic\"  : y_train[i][timestep]\n",
    "        } \n",
    "        for j in range(len(feat_mat[0])):\n",
    "            try:\n",
    "                row_dict[feat_name[j]] = feat_mat[i][j]\n",
    "            except:\n",
    "                row_dict[feat_name[j]] = 0\n",
    "        X_train_list.append(row_dict)\n",
    "X_train_list[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19973b6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:41.548769Z",
     "iopub.status.busy": "2023-05-15T08:36:41.547892Z",
     "iopub.status.idle": "2023-05-15T08:36:42.593077Z",
     "shell.execute_reply": "2023-05-15T08:36:42.592187Z"
    },
    "papermill": {
     "duration": 1.059169,
     "end_time": "2023-05-15T08:36:42.595222",
     "exception": false,
     "start_time": "2023-05-15T08:36:41.536053",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "840it [00:01, 819.14it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'timestep': 0,\n",
       "  'location': 0,\n",
       "  'traffic': 0.40168145726296123,\n",
       "  'prev_1': 0.05698271835590846,\n",
       "  'prev_2': 0.07146193367585241,\n",
       "  'prev_3': 0.08033629145259225,\n",
       "  'prev_4': 0.09808500700607192,\n",
       "  'prev_5': 0.14759458197104158,\n",
       "  'prev_6': 0.18496029892573565,\n",
       "  'prev_7': 0.2480149462867819,\n",
       "  'prev_8': 0.26015880429705746,\n",
       "  'prev_9': 0.3713218122372723,\n",
       "  'prev_10': 0.4245679588977114,\n",
       "  'feata_1': 0.0,\n",
       "  'feata_2': 0.0,\n",
       "  'feata_3': 0.0,\n",
       "  'feata_4': 1.0,\n",
       "  'feata_5': 0.0,\n",
       "  'hour_1': 0.0,\n",
       "  'hour_2': 0.0,\n",
       "  'hour_3': 0.0,\n",
       "  'hour_4': 1.0,\n",
       "  'hour_5': 0.0,\n",
       "  'hour_6': 0.0,\n",
       "  'hour_7': 0.0,\n",
       "  'hour_8': 0.0,\n",
       "  'hour_9': 0.0,\n",
       "  'hour_10': 0.0,\n",
       "  'hour_11': 0.0,\n",
       "  'hour_12': 0.0,\n",
       "  'hour_13': 0.0,\n",
       "  'hour_14': 0.0,\n",
       "  'hour_15': 0.0,\n",
       "  'hour_16': 0.0,\n",
       "  'hour_17': 0.0,\n",
       "  'hour_18': 0.0,\n",
       "  'hour_19': 0.0,\n",
       "  'hour_20': 0.0,\n",
       "  'hour_21': 0.0,\n",
       "  'hour_22': 0.0,\n",
       "  'hour_23': 0.0,\n",
       "  'hour_24': 0.0,\n",
       "  'no_roads': 3.0,\n",
       "  'featb_1': 1.0,\n",
       "  'featb_2': 0.0,\n",
       "  'featb_3': 0.0,\n",
       "  'featb_4': 0.0,\n",
       "  'featc_1': 1.0,\n",
       "  'featc_2': 0.0,\n",
       "  'featc_3': 0.0,\n",
       "  'featc_4': 0.0},\n",
       " {'timestep': 0,\n",
       "  'location': 1,\n",
       "  'traffic': 0.5730966837926202,\n",
       "  'prev_1': 0.04950957496496964,\n",
       "  'prev_2': 0.0541802895843064,\n",
       "  'prev_3': 0.08173750583839327,\n",
       "  'prev_4': 0.07473143390938813,\n",
       "  'prev_5': 0.12050443717888837,\n",
       "  'prev_6': 0.18916394208313872,\n",
       "  'prev_7': 0.25455394675385334,\n",
       "  'prev_8': 0.2746380196170014,\n",
       "  'prev_9': 0.37085474077533864,\n",
       "  'prev_10': 0.4381130312937879,\n",
       "  'feata_1': 0.0,\n",
       "  'feata_2': 0.0,\n",
       "  'feata_3': 0.0,\n",
       "  'feata_4': 1.0,\n",
       "  'feata_5': 0.0,\n",
       "  'hour_1': 0.0,\n",
       "  'hour_2': 0.0,\n",
       "  'hour_3': 0.0,\n",
       "  'hour_4': 1.0,\n",
       "  'hour_5': 0.0,\n",
       "  'hour_6': 0.0,\n",
       "  'hour_7': 0.0,\n",
       "  'hour_8': 0.0,\n",
       "  'hour_9': 0.0,\n",
       "  'hour_10': 0.0,\n",
       "  'hour_11': 0.0,\n",
       "  'hour_12': 0.0,\n",
       "  'hour_13': 0.0,\n",
       "  'hour_14': 0.0,\n",
       "  'hour_15': 0.0,\n",
       "  'hour_16': 0.0,\n",
       "  'hour_17': 0.0,\n",
       "  'hour_18': 0.0,\n",
       "  'hour_19': 0.0,\n",
       "  'hour_20': 0.0,\n",
       "  'hour_21': 0.0,\n",
       "  'hour_22': 0.0,\n",
       "  'hour_23': 0.0,\n",
       "  'hour_24': 0.0,\n",
       "  'no_roads': 4.0,\n",
       "  'featb_1': 1.0,\n",
       "  'featb_2': 0.0,\n",
       "  'featb_3': 0.0,\n",
       "  'featb_4': 0.0,\n",
       "  'featc_1': 1.0,\n",
       "  'featc_2': 0.0,\n",
       "  'featc_3': 0.0,\n",
       "  'featc_4': 0.0},\n",
       " {'timestep': 0,\n",
       "  'location': 2,\n",
       "  'traffic': 0.39701074264362446,\n",
       "  'prev_1': 0.05371321812237272,\n",
       "  'prev_2': 0.044371788883699206,\n",
       "  'prev_3': 0.06865950490425035,\n",
       "  'prev_4': 0.07286314806165343,\n",
       "  'prev_5': 0.10415693601120972,\n",
       "  'prev_6': 0.152732368052312,\n",
       "  'prev_7': 0.20037365716954694,\n",
       "  'prev_8': 0.25735637552545537,\n",
       "  'prev_9': 0.25502101821578704,\n",
       "  'prev_10': 0.40168145726296123,\n",
       "  'feata_1': 0.0,\n",
       "  'feata_2': 0.0,\n",
       "  'feata_3': 0.0,\n",
       "  'feata_4': 1.0,\n",
       "  'feata_5': 0.0,\n",
       "  'hour_1': 0.0,\n",
       "  'hour_2': 0.0,\n",
       "  'hour_3': 0.0,\n",
       "  'hour_4': 1.0,\n",
       "  'hour_5': 0.0,\n",
       "  'hour_6': 0.0,\n",
       "  'hour_7': 0.0,\n",
       "  'hour_8': 0.0,\n",
       "  'hour_9': 0.0,\n",
       "  'hour_10': 0.0,\n",
       "  'hour_11': 0.0,\n",
       "  'hour_12': 0.0,\n",
       "  'hour_13': 0.0,\n",
       "  'hour_14': 0.0,\n",
       "  'hour_15': 0.0,\n",
       "  'hour_16': 0.0,\n",
       "  'hour_17': 0.0,\n",
       "  'hour_18': 0.0,\n",
       "  'hour_19': 0.0,\n",
       "  'hour_20': 0.0,\n",
       "  'hour_21': 0.0,\n",
       "  'hour_22': 0.0,\n",
       "  'hour_23': 0.0,\n",
       "  'hour_24': 0.0,\n",
       "  'no_roads': 3.0,\n",
       "  'featb_1': 1.0,\n",
       "  'featb_2': 0.0,\n",
       "  'featb_3': 0.0,\n",
       "  'featb_4': 0.0,\n",
       "  'featc_1': 1.0,\n",
       "  'featc_2': 0.0,\n",
       "  'featc_3': 0.0,\n",
       "  'featc_4': 0.0}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_list = []\n",
    "for timestep, feat_mat in tqdm.tqdm(enumerate(X_test[0])):\n",
    "    feat_mat = feat_mat.toarray()\n",
    "    for i in range(len(feat_mat)):\n",
    "        row_dict = {\n",
    "            \"timestep\" : timestep,\n",
    "            \"location\" : i,\n",
    "            \"traffic\"  : y_test[i][timestep]\n",
    "        } \n",
    "        for j in range(len(feat_mat[0])):\n",
    "            try:\n",
    "                row_dict[feat_name[j]] = feat_mat[i][j]\n",
    "            except:\n",
    "                row_dict[feat_name[j]] = 0\n",
    "        X_test_list.append(row_dict)\n",
    "X_test_list[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28d96df0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:42.621756Z",
     "iopub.status.busy": "2023-05-15T08:36:42.619986Z",
     "iopub.status.idle": "2023-05-15T08:36:43.658093Z",
     "shell.execute_reply": "2023-05-15T08:36:43.657214Z"
    },
    "papermill": {
     "duration": 1.053147,
     "end_time": "2023-05-15T08:36:43.660258",
     "exception": false,
     "start_time": "2023-05-15T08:36:42.607111",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestep</th>\n",
       "      <th>location</th>\n",
       "      <th>traffic</th>\n",
       "      <th>prev_1</th>\n",
       "      <th>prev_2</th>\n",
       "      <th>prev_3</th>\n",
       "      <th>prev_4</th>\n",
       "      <th>prev_5</th>\n",
       "      <th>prev_6</th>\n",
       "      <th>prev_7</th>\n",
       "      <th>...</th>\n",
       "      <th>hour_24</th>\n",
       "      <th>no_roads</th>\n",
       "      <th>featb_1</th>\n",
       "      <th>featb_2</th>\n",
       "      <th>featb_3</th>\n",
       "      <th>featb_4</th>\n",
       "      <th>featc_1</th>\n",
       "      <th>featc_2</th>\n",
       "      <th>featc_3</th>\n",
       "      <th>featc_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.050911</td>\n",
       "      <td>0.092480</td>\n",
       "      <td>0.096684</td>\n",
       "      <td>0.070061</td>\n",
       "      <td>0.065857</td>\n",
       "      <td>0.078468</td>\n",
       "      <td>0.071462</td>\n",
       "      <td>0.054180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.046240</td>\n",
       "      <td>0.097151</td>\n",
       "      <td>0.102289</td>\n",
       "      <td>0.081738</td>\n",
       "      <td>0.076600</td>\n",
       "      <td>0.072396</td>\n",
       "      <td>0.067725</td>\n",
       "      <td>0.058851</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.050444</td>\n",
       "      <td>0.115367</td>\n",
       "      <td>0.110696</td>\n",
       "      <td>0.105558</td>\n",
       "      <td>0.095283</td>\n",
       "      <td>0.067725</td>\n",
       "      <td>0.080336</td>\n",
       "      <td>0.062588</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.044839</td>\n",
       "      <td>0.112097</td>\n",
       "      <td>0.102756</td>\n",
       "      <td>0.092013</td>\n",
       "      <td>0.083606</td>\n",
       "      <td>0.062121</td>\n",
       "      <td>0.070995</td>\n",
       "      <td>0.053713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.044839</td>\n",
       "      <td>0.127043</td>\n",
       "      <td>0.150864</td>\n",
       "      <td>0.131714</td>\n",
       "      <td>0.106025</td>\n",
       "      <td>0.099019</td>\n",
       "      <td>0.090612</td>\n",
       "      <td>0.069127</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestep  location   traffic    prev_1    prev_2    prev_3    prev_4  \\\n",
       "0         0         0  0.050911  0.092480  0.096684  0.070061  0.065857   \n",
       "1         0         1  0.046240  0.097151  0.102289  0.081738  0.076600   \n",
       "2         0         2  0.050444  0.115367  0.110696  0.105558  0.095283   \n",
       "3         0         3  0.044839  0.112097  0.102756  0.092013  0.083606   \n",
       "4         0         4  0.044839  0.127043  0.150864  0.131714  0.106025   \n",
       "\n",
       "     prev_5    prev_6    prev_7  ...  hour_24  no_roads  featb_1  featb_2  \\\n",
       "0  0.078468  0.071462  0.054180  ...      0.0       3.0      1.0      0.0   \n",
       "1  0.072396  0.067725  0.058851  ...      0.0       4.0      1.0      0.0   \n",
       "2  0.067725  0.080336  0.062588  ...      0.0       3.0      1.0      0.0   \n",
       "3  0.062121  0.070995  0.053713  ...      0.0       3.0      1.0      0.0   \n",
       "4  0.099019  0.090612  0.069127  ...      0.0       5.0      1.0      0.0   \n",
       "\n",
       "   featb_3  featb_4  featc_1  featc_2  featc_3  featc_4  \n",
       "0      0.0      0.0      1.0      0.0      0.0      0.0  \n",
       "1      0.0      0.0      1.0      0.0      0.0      0.0  \n",
       "2      0.0      0.0      1.0      0.0      0.0      0.0  \n",
       "3      0.0      0.0      1.0      0.0      0.0      0.0  \n",
       "4      0.0      0.0      0.0      1.0      0.0      0.0  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X_train = pd.DataFrame(X_train_list)\n",
    "df_X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4751099",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:43.686667Z",
     "iopub.status.busy": "2023-05-15T08:36:43.686031Z",
     "iopub.status.idle": "2023-05-15T08:36:44.328517Z",
     "shell.execute_reply": "2023-05-15T08:36:44.327557Z"
    },
    "papermill": {
     "duration": 0.657878,
     "end_time": "2023-05-15T08:36:44.330571",
     "exception": false,
     "start_time": "2023-05-15T08:36:43.672693",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestep</th>\n",
       "      <th>location</th>\n",
       "      <th>traffic</th>\n",
       "      <th>prev_1</th>\n",
       "      <th>prev_2</th>\n",
       "      <th>prev_3</th>\n",
       "      <th>prev_4</th>\n",
       "      <th>prev_5</th>\n",
       "      <th>prev_6</th>\n",
       "      <th>prev_7</th>\n",
       "      <th>...</th>\n",
       "      <th>hour_24</th>\n",
       "      <th>no_roads</th>\n",
       "      <th>featb_1</th>\n",
       "      <th>featb_2</th>\n",
       "      <th>featb_3</th>\n",
       "      <th>featb_4</th>\n",
       "      <th>featc_1</th>\n",
       "      <th>featc_2</th>\n",
       "      <th>featc_3</th>\n",
       "      <th>featc_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.401681</td>\n",
       "      <td>0.056983</td>\n",
       "      <td>0.071462</td>\n",
       "      <td>0.080336</td>\n",
       "      <td>0.098085</td>\n",
       "      <td>0.147595</td>\n",
       "      <td>0.184960</td>\n",
       "      <td>0.248015</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.573097</td>\n",
       "      <td>0.049510</td>\n",
       "      <td>0.054180</td>\n",
       "      <td>0.081738</td>\n",
       "      <td>0.074731</td>\n",
       "      <td>0.120504</td>\n",
       "      <td>0.189164</td>\n",
       "      <td>0.254554</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.397011</td>\n",
       "      <td>0.053713</td>\n",
       "      <td>0.044372</td>\n",
       "      <td>0.068660</td>\n",
       "      <td>0.072863</td>\n",
       "      <td>0.104157</td>\n",
       "      <td>0.152732</td>\n",
       "      <td>0.200374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.463335</td>\n",
       "      <td>0.049043</td>\n",
       "      <td>0.054647</td>\n",
       "      <td>0.070995</td>\n",
       "      <td>0.073797</td>\n",
       "      <td>0.098085</td>\n",
       "      <td>0.153199</td>\n",
       "      <td>0.205044</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.284914</td>\n",
       "      <td>0.050444</td>\n",
       "      <td>0.058851</td>\n",
       "      <td>0.053713</td>\n",
       "      <td>0.054647</td>\n",
       "      <td>0.068192</td>\n",
       "      <td>0.084073</td>\n",
       "      <td>0.119570</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestep  location   traffic    prev_1    prev_2    prev_3    prev_4  \\\n",
       "0         0         0  0.401681  0.056983  0.071462  0.080336  0.098085   \n",
       "1         0         1  0.573097  0.049510  0.054180  0.081738  0.074731   \n",
       "2         0         2  0.397011  0.053713  0.044372  0.068660  0.072863   \n",
       "3         0         3  0.463335  0.049043  0.054647  0.070995  0.073797   \n",
       "4         0         4  0.284914  0.050444  0.058851  0.053713  0.054647   \n",
       "\n",
       "     prev_5    prev_6    prev_7  ...  hour_24  no_roads  featb_1  featb_2  \\\n",
       "0  0.147595  0.184960  0.248015  ...      0.0       3.0      1.0      0.0   \n",
       "1  0.120504  0.189164  0.254554  ...      0.0       4.0      1.0      0.0   \n",
       "2  0.104157  0.152732  0.200374  ...      0.0       3.0      1.0      0.0   \n",
       "3  0.098085  0.153199  0.205044  ...      0.0       3.0      1.0      0.0   \n",
       "4  0.068192  0.084073  0.119570  ...      0.0       5.0      1.0      0.0   \n",
       "\n",
       "   featb_3  featb_4  featc_1  featc_2  featc_3  featc_4  \n",
       "0      0.0      0.0      1.0      0.0      0.0      0.0  \n",
       "1      0.0      0.0      1.0      0.0      0.0      0.0  \n",
       "2      0.0      0.0      1.0      0.0      0.0      0.0  \n",
       "3      0.0      0.0      1.0      0.0      0.0      0.0  \n",
       "4      0.0      0.0      0.0      1.0      0.0      0.0  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X_test = pd.DataFrame(X_test_list)\n",
    "df_X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1251253d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:44.359927Z",
     "iopub.status.busy": "2023-05-15T08:36:44.359582Z",
     "iopub.status.idle": "2023-05-15T08:36:44.376409Z",
     "shell.execute_reply": "2023-05-15T08:36:44.375366Z"
    },
    "papermill": {
     "duration": 0.035085,
     "end_time": "2023-05-15T08:36:44.378946",
     "exception": false,
     "start_time": "2023-05-15T08:36:44.343861",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   1   2   3   4   5   6   7   8   9   ...  26  27  28  29  30  31  32  \\\n",
       "0   0   1   0   0   0   0   0   0   0   0  ...   0   0   0   1   0   0   0   \n",
       "1   1   0   1   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "2   0   1   0   1   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "3   0   0   1   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   1   0   0   0   0  ...   1   0   0   0   0   1   0   \n",
       "\n",
       "   33  34  35  \n",
       "0   0   0   0  \n",
       "1   0   0   0  \n",
       "2   0   0   0  \n",
       "3   0   0   0  \n",
       "4   0   0   0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_adj = pd.DataFrame(adj_mat)\n",
    "df_adj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "90c86a3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:44.405963Z",
     "iopub.status.busy": "2023-05-15T08:36:44.405395Z",
     "iopub.status.idle": "2023-05-15T08:36:44.411807Z",
     "shell.execute_reply": "2023-05-15T08:36:44.410382Z"
    },
    "papermill": {
     "duration": 0.02195,
     "end_time": "2023-05-15T08:36:44.413799",
     "exception": false,
     "start_time": "2023-05-15T08:36:44.391849",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45396\n",
      "30240\n",
      "36\n"
     ]
    }
   ],
   "source": [
    "print(len(df_X_train))\n",
    "print(len(df_X_test))\n",
    "print(len(df_adj))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f2bffc8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:44.441459Z",
     "iopub.status.busy": "2023-05-15T08:36:44.440630Z",
     "iopub.status.idle": "2023-05-15T08:36:44.454322Z",
     "shell.execute_reply": "2023-05-15T08:36:44.453155Z"
    },
    "papermill": {
     "duration": 0.029408,
     "end_time": "2023-05-15T08:36:44.456401",
     "exception": false,
     "start_time": "2023-05-15T08:36:44.426993",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestep    419.500000\n",
       "location     17.500000\n",
       "traffic       0.321344\n",
       "prev_1        0.321839\n",
       "prev_2        0.322055\n",
       "prev_3        0.322254\n",
       "prev_4        0.322422\n",
       "prev_5        0.322534\n",
       "prev_6        0.322576\n",
       "prev_7        0.322547\n",
       "prev_8        0.322461\n",
       "prev_9        0.322281\n",
       "prev_10       0.322018\n",
       "feata_1       0.228571\n",
       "feata_2       0.217857\n",
       "feata_3       0.114286\n",
       "feata_4       0.210714\n",
       "feata_5       0.228571\n",
       "hour_1        0.038095\n",
       "hour_2        0.038095\n",
       "hour_3        0.038095\n",
       "hour_4        0.041667\n",
       "hour_5        0.042857\n",
       "hour_6        0.042857\n",
       "hour_7        0.042857\n",
       "hour_8        0.042857\n",
       "hour_9        0.042857\n",
       "hour_10       0.042857\n",
       "hour_11       0.042857\n",
       "hour_12       0.042857\n",
       "hour_13       0.041667\n",
       "hour_14       0.041667\n",
       "hour_15       0.042857\n",
       "hour_16       0.042857\n",
       "hour_17       0.042857\n",
       "hour_18       0.042857\n",
       "hour_19       0.042857\n",
       "hour_20       0.042857\n",
       "hour_21       0.042857\n",
       "hour_22       0.041667\n",
       "hour_23       0.038095\n",
       "hour_24       0.038095\n",
       "no_roads      3.527778\n",
       "featb_1       0.222222\n",
       "featb_2       0.388889\n",
       "featb_3       0.111111\n",
       "featb_4       0.277778\n",
       "featc_1       0.416667\n",
       "featc_2       0.305556\n",
       "featc_3       0.138889\n",
       "featc_4       0.138889\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X_test.mean() \n",
    "# 1-5\n",
    "# 6-29 is hour\n",
    "# 30 is no of roads\n",
    "# 31-34\n",
    "# 35-38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07d861a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:44.484244Z",
     "iopub.status.busy": "2023-05-15T08:36:44.483402Z",
     "iopub.status.idle": "2023-05-15T08:36:47.867133Z",
     "shell.execute_reply": "2023-05-15T08:36:47.866118Z"
    },
    "papermill": {
     "duration": 3.400462,
     "end_time": "2023-05-15T08:36:47.869835",
     "exception": false,
     "start_time": "2023-05-15T08:36:44.469373",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_X_train.to_csv(\"train.csv\")\n",
    "df_X_test.to_csv(\"test.csv\")\n",
    "df_adj.to_csv(\"adj_matrix.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5c9c5c3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-15T08:36:47.897258Z",
     "iopub.status.busy": "2023-05-15T08:36:47.896911Z",
     "iopub.status.idle": "2023-05-15T08:36:48.913026Z",
     "shell.execute_reply": "2023-05-15T08:36:48.912123Z"
    },
    "papermill": {
     "duration": 1.032305,
     "end_time": "2023-05-15T08:36:48.915271",
     "exception": false,
     "start_time": "2023-05-15T08:36:47.882966",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "\n",
    "def show_graph_with_labels(adjacency_matrix):\n",
    "    rows, cols = np.where(adjacency_matrix == 1)\n",
    "    edges = zip(rows.tolist(), cols.tolist())\n",
    "    gr = nx.Graph()\n",
    "    gr.add_edges_from(edges)\n",
    "    nx.draw(gr, node_size=200, with_labels=True)\n",
    "    plt.show()\n",
    "\n",
    "show_graph_with_labels(df_adj)"
   ]
  }
 ],
 "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": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 21.397203,
   "end_time": "2023-05-15T08:36:49.851127",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-05-15T08:36:28.453924",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}