test efficiency of algo_1_python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d8c341f4-72ec-41d9-8094-82d124d0d245",
   "metadata": {},
   "outputs": [],
   "source": [
    "exec(open(\"./funcs/mmj_cal_functions.py\").read())\n",
    "exec(open(\"./funcs/variant_of_Floyd_Warshall.py\").read())\n",
    "exec(open(\"./funcs/Prim_Minimum_Spanning_Tree.py\").read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "78874ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2baeabee-a859-49f8-a070-cf2135ae5563",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "test_data_145 = pickle.load(  open( \"./data/test_data_145.p\", \"rb\" ) ) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1744f75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2024-07-02 16:30:28'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from time import gmtime, strftime\n",
    "strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2959062f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "888ead07",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce38e6ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of points in data X: 120\n",
      "Time used: 13.589s\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data_id = 139\n",
    "X = test_data_145[data_id] \n",
    "print(f\"Number of points in data X: {len(X)}\" )\n",
    "\n",
    "start = time.time()\n",
    "X_mmj_matrix = cal_mmj_matrix_algo_1_python(X)\n",
    "end = time.time()\n",
    "time_used = end - start\n",
    "time_used = np.round(time_used, 3)\n",
    "\n",
    "print(f\"Time used: {time_used}s\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e309bc0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "956cc945",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of points in data X: 120\n",
      "Time used: 13.85s\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data_id = 139\n",
    "X = test_data_145[data_id] \n",
    "print(f\"Number of points in data X: {len(X)}\" )\n",
    "\n",
    "start = time.time()\n",
    "X_mmj_matrix = cal_mmj_matrix_algo_1_python(X)\n",
    "end = time.time()\n",
    "time_used = end - start\n",
    "time_used = np.round(time_used, 3)\n",
    "\n",
    "print(f\"Time used: {time_used}s\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0998f61",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9891310e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of points in data X: 120\n",
      "Time used: 13.451s\n"
     ]
    }
   ],
   "source": [
    "data_id = 139\n",
    "X = test_data_145[data_id] \n",
    "print(f\"Number of points in data X: {len(X)}\" )\n",
    "\n",
    "start = time.time()\n",
    "X_mmj_matrix = cal_mmj_matrix_algo_1_python(X)\n",
    "end = time.time()\n",
    "time_used = end - start\n",
    "time_used = np.round(time_used, 3)\n",
    "\n",
    "print(f\"Time used: {time_used}s\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9eaa08d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "36891539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of points in data X: 300\n",
      "Time used: 208.363s\n"
     ]
    }
   ],
   "source": [
    "data_id = 109\n",
    "X = test_data_145[data_id] \n",
    "print(f\"Number of points in data X: {len(X)}\" )\n",
    "\n",
    "start = time.time()\n",
    "X_mmj_matrix = cal_mmj_matrix_algo_1_python(X)\n",
    "end = time.time()\n",
    "time_used = end - start\n",
    "time_used = np.round(time_used, 3)\n",
    "\n",
    "print(f\"Time used: {time_used}s\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d39e725a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d69b50fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of points in data X: 500\n",
      "Time used: 990.308s\n"
     ]
    }
   ],
   "source": [
    "data_id = 18\n",
    "X = test_data_145[data_id] \n",
    "print(f\"Number of points in data X: {len(X)}\" )\n",
    "\n",
    "start = time.time()\n",
    "X_mmj_matrix = cal_mmj_matrix_algo_1_python(X)\n",
    "end = time.time()\n",
    "time_used = end - start\n",
    "time_used = np.round(time_used, 3)\n",
    "\n",
    "print(f\"Time used: {time_used}s\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e933f87a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad44d554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2024-07-02 16:51:08'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from time import gmtime, strftime\n",
    "strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33a5e788",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "539fcd66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of points in data X: 850\n",
      "Time used: 4681.911s\n"
     ]
    }
   ],
   "source": [
    "data_id = 19\n",
    "X = test_data_145[data_id] \n",
    "print(f\"Number of points in data X: {len(X)}\" )\n",
    "\n",
    "start = time.time()\n",
    "X_mmj_matrix = cal_mmj_matrix_algo_1_python(X)\n",
    "end = time.time()\n",
    "time_used = end - start\n",
    "time_used = np.round(time_used, 3)\n",
    "\n",
    "print(f\"Time used: {time_used}s\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86d1504f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dff24bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2024-07-02 18:09:10'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from time import gmtime, strftime\n",
    "strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61efb255",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "30d13132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of points in data X: 2500\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[13], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of points in data X: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(X)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m )\n\u001b[1;32m      5\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 6\u001b[0m X_mmj_matrix_algo_4 \u001b[38;5;241m=\u001b[39m \u001b[43mcal_mmj_matrix_algo_1_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m      8\u001b[0m time_used \u001b[38;5;241m=\u001b[39m end \u001b[38;5;241m-\u001b[39m start\n",
      "File \u001b[0;32m<string>:85\u001b[0m, in \u001b[0;36mcal_mmj_matrix_algo_1_python\u001b[0;34m(X, round_n)\u001b[0m\n",
      "File \u001b[0;32m<string>:64\u001b[0m, in \u001b[0;36mcal_n_mmj\u001b[0;34m(distance_matrix, mmj_matrix, n)\u001b[0m\n",
      "File \u001b[0;32m<string>:69\u001b[0m, in \u001b[0;36mupdate_mmj_ij\u001b[0;34m(distance_matrix, mmj_matrix, n, i, j)\u001b[0m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/core/fromnumeric.py:2810\u001b[0m, in \u001b[0;36mmax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m   2692\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_max_dispatcher)\n\u001b[1;32m   2693\u001b[0m \u001b[38;5;129m@set_module\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   2694\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue, initial\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue,\n\u001b[1;32m   2695\u001b[0m          where\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue):\n\u001b[1;32m   2696\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   2697\u001b[0m \u001b[38;5;124;03m    Return the maximum of an array or maximum along an axis.\u001b[39;00m\n\u001b[1;32m   2698\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2808\u001b[0m \u001b[38;5;124;03m    5\u001b[39;00m\n\u001b[1;32m   2809\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2810\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaximum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmax\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2811\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/core/fromnumeric.py:79\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m     77\u001b[0m     reduction \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(obj, method)\n\u001b[1;32m     78\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m---> 79\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m     80\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     81\u001b[0m     \u001b[38;5;66;03m# This branch is needed for reductions like any which don't\u001b[39;00m\n\u001b[1;32m     82\u001b[0m     \u001b[38;5;66;03m# support a dtype.\u001b[39;00m\n\u001b[1;32m     83\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "data_id = 16\n",
    "X = test_data_145[data_id] \n",
    "print(f\"Number of points in data X: {len(X)}\" )\n",
    "\n",
    "start = time.time()\n",
    "X_mmj_matrix = cal_mmj_matrix_algo_1_python(X)\n",
    "end = time.time()\n",
    "time_used = end - start\n",
    "time_used = np.round(time_used, 3)\n",
    "\n",
    "print(f\"Time used: {time_used}s\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "093af5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import gmtime, strftime\n",
    "strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f222bde6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6caf8bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_id = 35\n",
    "X = test_data_145[data_id] \n",
    "print(f\"Number of points in data X: {len(X)}\" )\n",
    "\n",
    "start = time.time()\n",
    "X_mmj_matrix = cal_mmj_matrix_algo_1_python(X)\n",
    "end = time.time()\n",
    "time_used = end - start\n",
    "time_used = np.round(time_used, 3)\n",
    "\n",
    "print(f\"Time used: {time_used}s\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a0e5227",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f72f93e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import gmtime, strftime\n",
    "strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "107f3ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_id = 136\n",
    "X = test_data_145[data_id] \n",
    "print(f\"Number of points in data X: {len(X)}\" )\n",
    "\n",
    "start = time.time()\n",
    "X_mmj_matrix = cal_mmj_matrix_algo_1_python(X)\n",
    "end = time.time()\n",
    "time_used = end - start\n",
    "time_used = np.round(time_used, 3)\n",
    "\n",
    "print(f\"Time used: {time_used}s\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2456ad3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47a63642",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "676ea46f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "697a90fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64106fde",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abeea006",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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