vaibhav-3.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 845,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uessMxu1gnLj"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy.special import gamma as gamma_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sCPrLvUFgnLu"
   },
   "outputs": [],
   "source": [
    "total_nodes = 100\n",
    "anchor_percent = 0.10\n",
    "N = int((1 - anchor_percent) * total_nodes) # unknown nodes\n",
    "M = total_nodes - N # anchor nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 847,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "wxMMDcRjoH-y",
    "outputId": "7b42d644-848b-4fe1-9d66-8be165afbfe3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VdHibg-FgnLz"
   },
   "outputs": [],
   "source": [
    "#anchor nodes and unknown nodes random placement\n",
    "X_unknown = []\n",
    "X_anchor = []\n",
    "\n",
    "for i in range(N):\n",
    "    x_unknown = np.random.randint(100)\n",
    "    y_unknown = np.random.randint(100)\n",
    "  \n",
    "    X_unknown.append([x_unknown, y_unknown])\n",
    "\n",
    "for i in range(M):\n",
    "    x_anchor = np.random.randint(100)\n",
    "    y_anchor = np.random.randint(100)\n",
    "\n",
    "    X_anchor.append([x_anchor, y_anchor])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 849,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "lNUcoyZcgnL4",
    "outputId": "b3cdd7dc-d378-4da9-ddfb-5920f7e20efe"
   },
   "outputs": [],
   "source": [
    "# X_unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 850,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "fYry7Fmec7od",
    "outputId": "1a32397c-8297-4ec8-d32e-ea953e3b054b"
   },
   "outputs": [],
   "source": [
    "# len(X_unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 851,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_iBfgzSKIszT",
    "outputId": "12e40ad2-6c90-4bf8-fb8f-93075ac1affc"
   },
   "outputs": [],
   "source": [
    "# X_anchor[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 852,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lY4nYJPZgnL-"
   },
   "outputs": [],
   "source": [
    "X_unknown = np.array(X_unknown)\n",
    "X_anchor = np.array(X_anchor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 853,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "s23KYDujKIOC",
    "outputId": "42884811-9f24-43a2-f978-46410ee5d68e"
   },
   "outputs": [],
   "source": [
    "X_anchor_og = X_anchor.copy()\n",
    "X_unknown_bak = X_unknown.copy()\n",
    "# print(X_anchor[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 854,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "colab_type": "code",
    "id": "WB5isTpZgnMD",
    "outputId": "9229d487-8f64-43ef-83be-934d86a16c92"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(X_unknown[:, 0], X_unknown[:, 1], 'ro', label=\"Unknown Node\")\n",
    "plt.plot(X_anchor[:, 0], X_anchor[:, 1], 'go', label=\"Anchor Node\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 855,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OuESlBcggnMJ"
   },
   "outputs": [],
   "source": [
    "t_range = 25 # transmission range\n",
    "# t_range_best = \n",
    "gamma = 0.1 # noise factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 856,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D2tzFZbVgnMP"
   },
   "outputs": [],
   "source": [
    "#objective function need to minimize\n",
    "def objective(x, y):\n",
    "    l = []\n",
    "    for j in range(len(X_anchor)): # for every anchor nodes\n",
    "        dij = np.power((np.power((x - X_anchor[j][0]), 2) + np.power((y - X_anchor[j][1]), 2)), 0.5)\n",
    "        dij_dash = dij + np.random.normal(0, (gamma*dij))\n",
    "        \n",
    "        if dij < t_range:\n",
    "            l.append(X_anchor[j])\n",
    "    if len(l) >= 3:\n",
    "#         print(l, 'anchor nodes')\n",
    "#         print(X_unknown[i], 'unknown nodes')\n",
    "        m = len(l)\n",
    "        rerror = []\n",
    "        for ancn in l:\n",
    "            dij = np.power((np.power((x - ancn[0]), 2) + np.power((y - ancn[1]), 2)), 0.5)\n",
    "            dij_dash = dij + (gamma * gamma * dij *dij)\n",
    "            rerror.append(np.power((dij - dij_dash), 2))\n",
    "\n",
    "        return np.sum(rerror) / m\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 857,
   "metadata": {},
   "outputs": [],
   "source": [
    "v_x = []\n",
    "for i in range(100):\n",
    "    for j in range(100):\n",
    "        if objective(i, j):\n",
    "            v_x.append(objective(i, j))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 858,
   "metadata": {},
   "outputs": [],
   "source": [
    "# v_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 859,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = np.max(v_x) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 860,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.45130000000001"
      ]
     },
     "execution_count": 860,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 861,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "zYnhaii7IQTB",
    "outputId": "db5a7b36-9724-49c4-ed74-d1404a666978"
   },
   "outputs": [],
   "source": [
    "# X_unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 862,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MHxfImvMgnMT"
   },
   "outputs": [],
   "source": [
    "# for i in range(N): # for every unknown nodes\n",
    "# objective(84, 23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 863,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JkWbL4TwgnMX"
   },
   "outputs": [],
   "source": [
    "alpha_min = 0.9\n",
    "alpha_max = 1.0\n",
    "pa_min = 0.05\n",
    "pa_max = 0.25\n",
    "X_min = 0\n",
    "X_max = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 864,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "f3Xqu1x6gnMc",
    "outputId": "9bbef04e-9390-4ccb-e49d-36a101398a03"
   },
   "outputs": [],
   "source": [
    "N_itertotal = 100\n",
    "# print(X_unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 865,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f7tsdU3RgnMg"
   },
   "outputs": [],
   "source": [
    "fitness_values = []\n",
    "for i in range(len(X_unknown)): # for every Unknown node\n",
    "    fitness_values.append(objective(X_unknown[i][0], X_unknown[i][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 866,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "I1huh9aKgnMl",
    "outputId": "f7514027-0c5e-4832-e9c0-d825913ef902"
   },
   "outputs": [],
   "source": [
    "# fitness_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 867,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qy9Nr1G69Sct"
   },
   "outputs": [],
   "source": [
    "#vk\n",
    "f_values_legal = []\n",
    "for i in range (len(fitness_values)):\n",
    "    if fitness_values[i] is None:\n",
    "        pass\n",
    "    else:\n",
    "        f_values_legal.append(fitness_values[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 868,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "2Gtfd9f3-CLd",
    "outputId": "49ecb8b6-05b1-4b89-eaec-db01080b4167"
   },
   "outputs": [],
   "source": [
    "#vk\n",
    "# f_values_legal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 869,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "jB0RgrOuIUvQ",
    "outputId": "be20c284-bcaf-4674-8978-819fd3c582b3"
   },
   "outputs": [],
   "source": [
    "# print(X_unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 870,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Trto0rKngnMr"
   },
   "outputs": [],
   "source": [
    "# import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 871,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jvo02QjqgnMv"
   },
   "outputs": [],
   "source": [
    "def alpha(n_iter):\n",
    "    return alpha_max - ((n_iter/ N_itertotal) * (alpha_max - alpha_min))\n",
    "\n",
    "# Levy Flight\n",
    "def levy():\n",
    "    # lamda = np.random.uniform(1.1,3)\n",
    "    lamda = 1.5\n",
    "    temp = np.power(((gamma_function(1 + lamda) * np.sin(np.pi * (lamda /2))) / (gamma_function((1 + lamda)/2) * lamda * np.power(2, ((lamda - 1)/2)) )), 1/lamda)\n",
    "    u = np.random.normal(0, temp)\n",
    "    v = np.random.normal(0,1)\n",
    "    r = u / (np.power(abs(v), (1/lamda)))\n",
    "\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 872,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yszCF80S4qYP",
    "outputId": "4d4a473b-4277-4668-d3e8-821fc0c226af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.405506862191242"
      ]
     },
     "execution_count": 872,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "levy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 873,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "7okLl3Ut4wEd",
    "outputId": "4c84d373-a0d7-4088-9269-2d198ecf685f"
   },
   "outputs": [],
   "source": [
    "# print(X_unknown)\n",
    "# print(len(X_unknown))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 874,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DGZCWqdXtA0x"
   },
   "outputs": [],
   "source": [
    "# np.zeros([N, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 875,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "colab_type": "code",
    "id": "1upSTslDgnM_",
    "outputId": "01e47c92-2f1c-4422-db86-7a57aba3bc3b",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[        inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf  4.10056667         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf  8.053              inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf 16.7742             inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf]\n",
      "[        inf 11.90345            inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "  9.58968333         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf 11.94365714         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      " 11.94365714         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf 11.90345            inf         inf         inf         inf]\n",
      "[        inf         inf 18.67453333         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf 18.4147\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf 12.6114             inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      " 21.62686667         inf         inf         inf         inf         inf\n",
      " 18.149725           inf         inf         inf 12.78965            inf\n",
      "         inf 17.669225           inf         inf         inf 19.684\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf 16.122225           inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf]\n",
      "[      inf       inf       inf       inf       inf  2.991675       inf\n",
      "  3.514925       inf       inf       inf       inf       inf 16.7613\n",
      "       inf       inf       inf       inf       inf       inf       inf\n",
      "       inf       inf       inf       inf       inf       inf       inf\n",
      "       inf  8.968025 16.7613         inf       inf       inf       inf\n",
      "  4.95664        inf       inf       inf       inf       inf       inf\n",
      "       inf       inf       inf       inf       inf       inf       inf\n",
      "  3.514925       inf       inf       inf       inf       inf       inf\n",
      "       inf       inf]\n",
      "[        inf         inf         inf 13.00635            inf         inf\n",
      "         inf         inf         inf         inf  5.83776667         inf\n",
      "         inf         inf  6.0687      6.0687             inf         inf\n",
      "         inf         inf         inf  6.0687             inf         inf\n",
      "         inf         inf         inf         inf  7.0692             inf\n",
      "  6.0687             inf         inf         inf         inf         inf\n",
      "         inf         inf         inf 13.26283333  5.83776667         inf\n",
      "         inf  7.0692             inf  6.0687             inf         inf\n",
      "         inf]\n",
      "[   inf    inf    inf    inf    inf    inf    inf    inf    inf    inf\n",
      "    inf    inf    inf    inf 8.3341    inf    inf    inf    inf    inf\n",
      "    inf    inf    inf    inf    inf    inf    inf    inf    inf    inf\n",
      "    inf    inf    inf    inf    inf    inf    inf    inf]\n",
      "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n",
      " inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:52: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n",
      " inf inf inf inf inf inf inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n",
      " inf inf inf inf inf inf inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n",
      " inf inf inf inf inf inf inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n",
      " inf inf inf inf inf inf inf inf inf inf inf inf]\n",
      "[        inf         inf         inf         inf         inf 15.59546667\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf]\n",
      "[    inf     inf     inf     inf     inf     inf     inf     inf     inf\n",
      "     inf     inf     inf     inf     inf     inf 6.79433     inf     inf\n",
      "     inf     inf     inf     inf     inf     inf     inf     inf     inf\n",
      "     inf]\n",
      "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n",
      " inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n",
      " inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n",
      " inf inf inf inf inf inf inf]\n",
      "[        inf         inf  6.17666364         inf 13.3224             inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf\n",
      "         inf         inf         inf         inf         inf         inf]\n",
      "[0.9768     1.7225     0.9768            inf        inf 0.9768\n",
      " 1.77156667 4.2952            inf 1.7225            inf 1.7225\n",
      "        inf        inf        inf        inf        inf        inf\n",
      "        inf        inf 1.77156667]\n",
      "[inf inf inf inf inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf inf inf inf inf]\n",
      "[      inf       inf       inf       inf       inf       inf 14.727325\n",
      "       inf       inf       inf       inf]\n",
      "[9.3727    inf    inf 5.1887    inf    inf    inf    inf    inf]\n",
      "[inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf inf]\n",
      "[inf inf inf inf]\n",
      "[inf inf inf inf]\n",
      "[inf inf inf inf]\n",
      "[inf inf inf inf]\n",
      "[inf inf inf inf]\n",
      "[inf inf inf inf]\n",
      "[inf inf inf inf]\n",
      "[inf inf inf inf]\n",
      "[inf inf inf inf]\n"
     ]
    }
   ],
   "source": [
    "X_unknown_org1 = np.zeros([N, 2])\n",
    "X_unknown_org2 = np.zeros([N, 2])\n",
    "\n",
    "ales = []\n",
    "\n",
    "N_iter = 1\n",
    "while (N_iter < N_itertotal) : # termination conditions\n",
    "  X_js = []\n",
    "  for i in range(len(X_unknown)): # For every unknown node\n",
    "    X_j = X_unknown[i]\n",
    "    X_j[0] = X_unknown[i][0] + alpha(N_iter) * levy()\n",
    "    X_j[1] = X_unknown[i][1] + alpha(N_iter) * levy()\n",
    "    # print(X_j)\n",
    "    # print(X_j[0])\n",
    "    if X_j[0] > X_max or X_j[1] > X_max:\n",
    "      X_j = [X_max, X_max]\n",
    "    \n",
    "    if X_j[0] < X_min or X_j[1] < X_min:\n",
    "      X_j = [X_min, X_min]\n",
    "\n",
    "    F_j = objective(X_j[0], X_j[1]) or 0\n",
    "    rand_k = np.random.randint(0, len(X_unknown))\n",
    "    F_k = objective(X_unknown[rand_k][0], X_unknown[rand_k][1]) or 0\n",
    "\n",
    "    if F_j > F_k:\n",
    "      X_j[0] = X_unknown[rand_k][0]\n",
    "      X_j[1] = X_unknown[rand_k][1]\n",
    "      F_j = F_k\n",
    "\n",
    "    X_js.append(X_j)\n",
    "    # print(X_j)\n",
    "    #end if\n",
    "  #End For\n",
    "  # print(X_js)\n",
    "\n",
    "  Obj_X_Js = []\n",
    "  for i in range(len(X_js)):\n",
    "    Obj_X_Js.append(objective(X_js[i][0], X_js[i][1]))\n",
    "\n",
    "  # print(Obj_X_Js)\n",
    "\n",
    "  # best solution selection\n",
    "  Obj_X_Js = np.array([np.inf if i is None else i for i in Obj_X_Js])\n",
    "  print(Obj_X_Js)\n",
    "  F_min = Obj_X_Js[np.argmin(Obj_X_Js)]\n",
    "  # print(F_min)\n",
    "\n",
    "  # mutation probability\n",
    "  Pa_j = []\n",
    "\n",
    "  for i in Obj_X_Js:\n",
    "    K = i - F_min\n",
    "    if K < 1:\n",
    "      Pa_j.append(pa_min + (pa_max - pa_min) * K)\n",
    "    else:\n",
    "      Pa_j.append(pa_max / N_iter)\n",
    "\n",
    "  # print(Pa_j)\n",
    "\n",
    "  # new random solutions generation\n",
    "  for i in range(len(Pa_j)):\n",
    "    rand_temp = np.random.uniform(0, 1)\n",
    "    if rand_temp < Pa_j[i]:\n",
    "      my_x = np.random.randint(100)\n",
    "      my_y = np.random.randint(100)\n",
    "      X_js[i] = [my_x, my_y]\n",
    "    #end if\n",
    "  #end for\n",
    "\n",
    "\n",
    "\n",
    "  # finding best solution after raplacement with new random solutions\n",
    "  # Make copy of X_unknown\n",
    "  X_unknown_copy = X_unknown.copy()\n",
    "  X_unknown = X_js\n",
    "\n",
    "#   Mark\n",
    "  Obj_X_unknown = []\n",
    "  for i in range(len(X_unknown)):\n",
    "    Obj_X_unknown.append(objective(X_unknown[i][0], X_unknown[i][1]))\n",
    "\n",
    "  Obj_X_unknown = np.array([np.inf if i is None else i for i in Obj_X_unknown])\n",
    "  # print(Obj_X_unknown)\n",
    "  F_best_arg = np.argmin(Obj_X_unknown)\n",
    "\n",
    "  F_best_args = []\n",
    "\n",
    "  for i in range(len(Obj_X_unknown)):\n",
    "    if Obj_X_unknown[i] < threshold:\n",
    "        F_best_args.append(i)\n",
    "\n",
    "  # Updating Anchor List\n",
    "  for i in F_best_args:\n",
    "    X_anchor_temp = list(X_anchor)\n",
    "    X_anchor_temp.append(X_js[i])\n",
    "    X_anchor = np.array(X_anchor_temp)\n",
    "    \n",
    "  # Updating Unknown List    \n",
    "  X_unknown_temp = []\n",
    "  for i in range(len(X_unknown)):\n",
    "    if i in F_best_args:\n",
    "      ales.append([X_unknown[i], X_unknown_copy[i]])\n",
    "    else:\n",
    "      X_unknown_temp.append(X_unknown[i])\n",
    "    \n",
    "\n",
    "  X_unknown = np.array(X_unknown_temp)\n",
    "\n",
    "\n",
    "  # 1. When no unknown nodes left are localisable\n",
    "  if len(X_unknown) == 0:\n",
    "        break\n",
    "\n",
    "  N_iter += 1\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 876,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 765
    },
    "colab_type": "code",
    "id": "9HpyrC86J2d9",
    "outputId": "b2404803-a3a3-4504-df9f-03d009e27367"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 876,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 877,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OwPqBTH93D0i"
   },
   "outputs": [],
   "source": [
    "X_unknown = np.array(X_unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 878,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4O1smTkm3VtD",
    "outputId": "9de7c42b-533f-4911-966f-f40d3f8ac942"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 878,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 879,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "zR9fJGGniNQR",
    "outputId": "ffa19851-adf7-4003-cee4-670a46f9a016"
   },
   "outputs": [],
   "source": [
    "# X_anchor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 883,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "colab_type": "code",
    "id": "vxxOHLqEo0pk",
    "outputId": "ddb4039b-38dc-4502-84d5-faa19e00b6f8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(X_unknown_bak[:80, 0], X_unknown_bak[:80, 1], 'ro', label=\"Unknown Nodes\")\n",
    "plt.plot(X_anchor[M:, 0], X_anchor[M:, 1], 'bo', label=\"Localized Nodes\")\n",
    "plt.plot(X_anchor_og[:, 0], X_anchor_og[:, 1], 'g+', label=\"Anchor Nodes\")\n",
    "# plt.plot(X_unknown[:10, 0], X_unknown[:10, 1], 'yo', label=\"Left Nodes\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 884,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "a1Eu9Ucirjgh"
   },
   "outputs": [],
   "source": [
    "# Localization Success Ratio\n",
    "def lsr():\n",
    "    return ((len(X_anchor) - M) / len(X_unknown_bak)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 885,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "YBEsDkMhco5h",
    "outputId": "8dd37362-3392-4e91-e23f-85e29347af8f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.55555555555556"
      ]
     },
     "execution_count": 885,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 890,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "172.34883720930233"
      ]
     },
     "execution_count": 890,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average Localization Error\n",
    "\n",
    "ales\n",
    "\n",
    "eculidean_dists = []\n",
    "\n",
    "for al in ales:\n",
    "    estimated = al[0]\n",
    "    real = al[1]\n",
    "    eculidean_dists.append( np.sqrt((estimated[0] - real[0])**2 + (estimated[1] - real[1])**2) )\n",
    "#     print(al[0])\n",
    "\n",
    "np.sum(ales)/len(ales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 891,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.47"
      ]
     },
     "execution_count": 891,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3 * 99 + 50 )/ 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4142135623730951"
      ]
     },
     "execution_count": 841,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j54-osTRc-Wd"
   },
   "outputs": [],
   "source": [
    "# 1. When no unknown nodes left are localisable\n",
    "# 2. Add multiple unknown nodes into anchor nodes\n",
    "# 3. Capture results"
   ]
  }
 ],
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