vaibhav_4_0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sCPrLvUFgnLu"
   },
   "outputs": [],
   "source": [
    "total_nodes = 20\n",
    "anchor_percent = 0.90\n",
    "N = int((1 - anchor_percent) * total_nodes) # unknown nodes\n",
    "M = total_nodes - N # anchor nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "wxMMDcRjoH-y",
    "outputId": "5e987bce-12fd-40ca-8e55-cdaa71cf199e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "print(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VdHibg-FgnLz"
   },
   "outputs": [],
   "source": [
    "# anchor nodes random placement\n",
    "X_anchor = []\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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oaZOzIfkcOxK"
   },
   "outputs": [],
   "source": [
    "# unknown nodes random placement\n",
    "X_unknown = []\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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s23KYDujKIOC"
   },
   "outputs": [],
   "source": [
    "# back up of original anchors and unknowns\n",
    "X_anchor_og = X_anchor.copy()\n",
    "X_unknown_bak = X_unknown.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "colab_type": "code",
    "id": "WB5isTpZgnMD",
    "outputId": "cb04dfde-d3b3-4f3b-927c-8f68a989df73"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(X_unknown[:, 0], X_unknown[:, 1], 'r.', label=\"Unknown Node\")\n",
    "plt.plot(X_anchor[:, 0], X_anchor[:, 1], 'g+', label=\"Anchor Node\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OuESlBcggnMJ"
   },
   "outputs": [],
   "source": [
    "t_range = 10 # transmission range\n",
    "# t_range_best = \n",
    "gamma = 0.1 # noise factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "iBo2XpEakNZJ",
    "outputId": "8a564d4b-1126-47b2-98b2-04ca554de21c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 206,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_anchor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "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 + np.random.normal(0, (gamma*dij))\n",
    "            rerror.append(np.power((dij - dij_dash), 2))\n",
    "\n",
    "        return np.sum(rerror) / m\n",
    "        return dij"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Bv0LZYYklbbX"
   },
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mfFZaJyblbbb"
   },
   "outputs": [],
   "source": [
    "# v_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "m52cbss7lbbf",
    "outputId": "7454e70b-e860-4135-a1a1-ce27d7bca530"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.239011137138283\n"
     ]
    }
   ],
   "source": [
    "# threshold = ((np.max(v_x) - np.min(v_x))/2) #rejected\n",
    "# threshold = np.min(v_x) + 4\n",
    "mean_vx = np.mean(v_x)\n",
    "threshold = (mean_vx - np.min(v_x))/2\n",
    "# threshold = mean_vx - np.min(v_x)\n",
    "print(threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cWkvhksiKWHq",
    "outputId": "4d8bbe39-5fd0-4500-bffc-14fc3f48b478"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61.71645222941157\n"
     ]
    }
   ],
   "source": [
    "max_v = np.max(v_x)\n",
    "print(max_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cVzrE1nTQvYF",
    "outputId": "bd5f8048-a1ea-4f7c-c172-20a70488f021"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.819084657748936\n"
     ]
    }
   ],
   "source": [
    "mean_vx = np.mean(v_x)\n",
    "print(mean_vx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "B6pYreK9OEdy",
    "outputId": "ba950ae7-050f-48f1-b008-4b9bd27571fd"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(v_x, color = 'blue', edgecolor = 'black', bins = int(180/5))\n",
    "# plt.bar(range(len(v_x)), v_x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zYnhaii7IQTB"
   },
   "outputs": [],
   "source": [
    "# X_unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "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": 0,
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f3Xqu1x6gnMc"
   },
   "outputs": [],
   "source": [
    "N_itertotal = 100\n",
    "# print(X_unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "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": 219,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "I1huh9aKgnMl",
    "outputId": "7d483d49-cc2e-4d4a-8fd2-53790a96e6ac"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9.77457526000512]"
      ]
     },
     "execution_count": 219,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitness_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "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": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2Gtfd9f3-CLd"
   },
   "outputs": [],
   "source": [
    "#vk\n",
    "# f_values_legal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jB0RgrOuIUvQ"
   },
   "outputs": [],
   "source": [
    "# print(X_unknown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Trto0rKngnMr"
   },
   "outputs": [],
   "source": [
    "# import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "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": 225,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yszCF80S4qYP",
    "outputId": "3cb1815b-4ae8-466d-dfa1-210392c08219"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4958444684484066"
      ]
     },
     "execution_count": 225,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "levy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7okLl3Ut4wEd"
   },
   "outputs": [],
   "source": [
    "# print(X_unknown)\n",
    "# print(len(X_unknown))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DGZCWqdXtA0x"
   },
   "outputs": [],
   "source": [
    "# np.zeros([N, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ovucPqEsU2-a"
   },
   "outputs": [],
   "source": [
    "# state_root = [anc_state,unknown_state]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 527
    },
    "colab_type": "code",
    "id": "1upSTslDgnM_",
    "outputId": "1faa4d87-a0d0-4a90-ec00-eb013284a279",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[]\n",
      "[0]\n",
      "check 3.7197031917645176\n"
     ]
    }
   ],
   "source": [
    "# X_unknown_org1 = np.zeros([N, 2])\n",
    "# X_unknown_org2 = np.zeros([N, 2])\n",
    "\n",
    "ales = []\n",
    "X_calc = [] # final coordinates\n",
    "\n",
    "N_iter = 1\n",
    "while (N_iter < N_itertotal) : # termination condition\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",
    "    my_x = np.random.randint(100)\n",
    "    my_y = np.random.randint(100)\n",
    "    X_rand = [my_x, my_y]\n",
    "    if X_j[0] > X_max and X_j[1] > X_max:\n",
    "      # X_j = [X_max, X_max]\n",
    "      X_j = X_rand\n",
    "    elif X_j[0] > X_max and X_min < X_j[1] < X_max:\n",
    "      # X_j = [X_max, X_j[1]]\n",
    "      X_j = X_rand\n",
    "    elif X_j[0] > X_max and X_j[1] < X_min:\n",
    "      # X_j = [X_max, X_min]\n",
    "      X_j = X_rand\n",
    "    elif X_min < X_j[0] < X_max and X_j[1] < X_min:\n",
    "      # X_j = [X_j[0], X_min]\n",
    "      X_j = X_rand\n",
    "    elif X_j[0] < X_min and X_j[1] < X_min:\n",
    "      # X_j = [X_min, X_min]\n",
    "      X_j = X_rand\n",
    "    elif X_j[0] < X_min and X_min < X_j[1] < X_max:\n",
    "      # X_j = [X_min, X_j[1]]\n",
    "      X_j = X_rand\n",
    "    elif X_j[0] < X_min and X_j[1] > X_max:\n",
    "      # X_j = [X_min, X_max]\n",
    "      X_j = X_rand\n",
    "    elif X_min < X_j[0] < X_max and X_j[1] > X_max:\n",
    "      # X_j = [X_j[0], X_max]\n",
    "      X_j = X_rand\n",
    "\n",
    "    # Comparing random walked solution with unknown nodes' solution\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",
    "    # X_js is a list of current iteration's solutions\n",
    "    # print(X_j)\n",
    "    #end if\n",
    "  #End For\n",
    "  # print(X_js)\n",
    "\n",
    "  # Fitness values of current iteration's solutions\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 according to mutation probability\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 replacement 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",
    "  # Calculating fitness values of random replacement updated (mutated) unknown nodes\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) # Index of the best valued solution\n",
    "\n",
    "  F_best_args = []\n",
    "  for i in range(len(Obj_X_unknown)):\n",
    "    if Obj_X_unknown[i] < threshold:\n",
    "        F_best_args.append(i)\n",
    "\n",
    "  print(F_best_args)\n",
    "\n",
    "\n",
    "  # Updating Anchor List\n",
    "  for i in F_best_args:\n",
    "    print('check', Obj_X_unknown[i])\n",
    "    X_anchor_temp = list(X_anchor)\n",
    "    X_anchor_temp.append(X_unknown[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",
    "      X_calc.append(X_unknown[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": 230,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9HpyrC86J2d9",
    "outputId": "c9ee5e0e-9113-4a8a-eb3f-2708bd0a041e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 230,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OwPqBTH93D0i"
   },
   "outputs": [],
   "source": [
    "X_unknown = np.array(X_unknown)\n",
    "X_calc = np.array(X_calc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "4O1smTkm3VtD",
    "outputId": "bbb70901-246c-4cec-d1b2-656244ca8667"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(len(X_unknown))\n",
    "print(len(X_calc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "bgQLlElnJtPO",
    "outputId": "af6da084-be51-4ac4-8442-edbe74d85d1e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.418593319315795]\n",
      "13.418593319315795\n"
     ]
    }
   ],
   "source": [
    "# fitness values of final coordinates (solutions)\n",
    "Obj_X_calc = []\n",
    "for i in range (len(X_calc)):\n",
    "  Obj_X_calc.append(objective(X_calc[i][0], X_calc[i][1]))\n",
    "\n",
    "print(Obj_X_calc)\n",
    "X_calc_avg = np.average(Obj_X_calc)\n",
    "print(X_calc_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "aq9NsCx9qVAC",
    "outputId": "363bed6a-3e2b-4578-827d-12225e440e4e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "q = 0\n",
    "for i in range (len(Obj_X_calc)):\n",
    "  if Obj_X_calc[i] >= threshold:\n",
    "    # print(i)\n",
    "    # print(Obj_X_calc[i])\n",
    "    q += 1\n",
    "print(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TL19iYMS8q4g"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "colab_type": "code",
    "id": "vxxOHLqEo0pk",
    "outputId": "3d3fa0ac-e0fc-47da-a9ea-35d0b818df08"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "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",
    "if len(X_unknown) != 0 :\n",
    "  plt.plot(X_unknown[:, 0], X_unknown[:, 1], 'yo', label=\"Left Nodes\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "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": 160,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "JzLo4SA92Fum",
    "outputId": "6f707184-5164-4ac7-951e-10d6f1c041a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "19\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(len(X_anchor))\n",
    "print(M)\n",
    "print(len(X_unknown_bak))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "YBEsDkMhco5h",
    "outputId": "360793ed-39d6-4c52-c22b-b4312a39234f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 161,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VTReSgzg2sme"
   },
   "outputs": [],
   "source": [
    "# For observations (retaining original topology)\n",
    "X_anchor = X_anchor_og\n",
    "X_unknown = X_unknown_bak\n",
    "# print(len(X_unknown))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9nhPHPjUlbdZ",
    "outputId": "b4e898af-a67e-456b-c006-b9bc04d4cff3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170.0"
      ]
     },
     "execution_count": 163,
     "metadata": {
      "tags": []
     },
     "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": 0,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xcl3E9CO0JW6"
   },
   "outputs": [],
   "source": [
    "#plotly\n",
    "#seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ifeaL19kToTB"
   },
   "source": [
    "### PLOTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "NEppbZjQgy3J",
    "outputId": "080a338f-a015-4393-920d-19ccda2019c8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IMPROVED\n",
    "# lsr graph (Anc. Ratio)\n",
    "anc_density = [10, 20, 30, 40, 50, 60]\n",
    "lsr_100 = [16.6666, 31.25, 100, 100, 100, 100]\n",
    "lsr_200 = [92.7777, 100, 100, 100, 100, 100]\n",
    "lsr_300 = [100, 100, 100, 100, 100, 100]\n",
    "lsr_400 = [100, 100, 100, 100, 100, 100]\n",
    "\n",
    "plt.xlabel(\"Anchor Ratio (in %)\")\n",
    "# plt.ylabel(\"Localization Success Ratio\")\n",
    "plt.title(\"LSR\")\n",
    "\n",
    "plt.plot(anc_density, lsr_100, label=\"Node Density=100\")\n",
    "plt.plot(anc_density, lsr_100, 'bo')\n",
    "plt.plot(anc_density,lsr_200, label=\"Node Density=200\")\n",
    "plt.plot(anc_density, lsr_200, 'yo')\n",
    "plt.plot(anc_density,lsr_300, label=\"Node Density=300\")\n",
    "plt.plot(anc_density, lsr_300, 'go')\n",
    "plt.plot(anc_density,lsr_400, label=\"Node Density=400\")\n",
    "plt.plot(anc_density, lsr_400, 'ro')\n",
    "\n",
    "plt.legend(shadow='true')\n",
    "plt.axis([5,65,0,105])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "1Fsfo4cCur6G",
    "outputId": "c26d678a-891b-470e-d436-2bc8a337d08b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IMPROVED\n",
    "# lsr graph (T Range)\n",
    "trans_range = [10, 20, 30, 40, 50]\n",
    "lsr_100 = [0, 17.5, 32.5, 100, 100]\n",
    "lsr_200 = [8.125, 63.7499, 100, 100, 100]\n",
    "lsr_300 = [17.91, 80.833, 100, 100, 100]\n",
    "lsr_400 = [31.5625, 87.5, 100, 100, 100]\n",
    "\n",
    "plt.xlabel(\"Communication Range (meters)\")\n",
    "# plt.ylabel(\"Localization Succes Ratio\")\n",
    "plt.title(\"LSR\")\n",
    "\n",
    "plt.plot(trans_range, lsr_100, label=\"Node Density=100\")\n",
    "plt.plot(trans_range, lsr_100, 'bo')\n",
    "plt.plot(trans_range, lsr_200, label=\"Node Density=200\")\n",
    "plt.plot(trans_range, lsr_200, 'yo')\n",
    "plt.plot(trans_range, lsr_300, label=\"Node Density=300\")\n",
    "plt.plot(trans_range, lsr_300, 'go')\n",
    "plt.plot(trans_range, lsr_400, label=\"Node Density=400\")\n",
    "plt.plot(trans_range, lsr_400, 'ro')\n",
    "\n",
    "plt.legend(shadow='true')\n",
    "plt.axis([5,55,-5,105])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "f-m6iwh-0SBw",
    "outputId": "6250b6ea-8d39-4012-a8ec-d8ddba6c5c72"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BASE\n",
    "# lsr graph (Anc. Ratio)\n",
    "anc_density = [10, 20, 30, 40, 50, 60]\n",
    "lsr_100 = [97, 99.35, 99.73, 99.99, 100, 100]\n",
    "lsr_200 = [99.71, 99.90, 100, 100, 100, 100]\n",
    "lsr_300 = [99.94, 99.98, 100, 100, 100, 100]\n",
    "lsr_400 = [100, 100, 100, 100, 100, 100]\n",
    "\n",
    "plt.xlabel(\"Anchor Ratio (in %)\")\n",
    "# plt.ylabel(\"Localization Success Ratio\")\n",
    "plt.title(\"LSR\")\n",
    "\n",
    "plt.plot(anc_density, lsr_100, label=\"Node Density=100\")\n",
    "plt.plot(anc_density, lsr_100, 'bo')\n",
    "plt.plot(anc_density,lsr_200, label=\"Node Density=200\")\n",
    "plt.plot(anc_density, lsr_200, 'yo')\n",
    "plt.plot(anc_density,lsr_300, label=\"Node Density=300\")\n",
    "plt.plot(anc_density, lsr_300, 'go')\n",
    "plt.plot(anc_density,lsr_400, label=\"Node Density=400\")\n",
    "plt.plot(anc_density, lsr_400, 'ro')\n",
    "\n",
    "plt.legend(shadow='true')\n",
    "plt.axis([5,65,96,101])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "CTdSZV3NyHQd",
    "outputId": "c1d7b07f-ec4e-4ffc-df9f-b1f85770bb11"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BASE\n",
    "# lsr graph (T Range)\n",
    "trans_range = [10, 20, 30, 40, 50]\n",
    "lsr_100 = [20, 98, 99.9, 100, 100]\n",
    "lsr_200 = [51, 100, 100, 100, 100]\n",
    "lsr_300 = [94, 100, 100, 100, 100]\n",
    "lsr_400 = [94.5, 100, 100, 100, 100]\n",
    "\n",
    "plt.xlabel(\"Communication Range (meters)\")\n",
    "# plt.ylabel(\"Localization Success Ratio\")\n",
    "plt.title(\"LSR\")\n",
    "\n",
    "plt.plot(trans_range, lsr_100, label=\"Node Density=100\")\n",
    "plt.plot(trans_range, lsr_100, 'bo')\n",
    "plt.plot(trans_range, lsr_200, label=\"Node Density=200\")\n",
    "plt.plot(trans_range, lsr_200, 'yo')\n",
    "plt.plot(trans_range, lsr_300, label=\"Node Density=300\")\n",
    "plt.plot(trans_range, lsr_300, 'go')\n",
    "plt.plot(trans_range, lsr_400, label=\"Node Density=400\")\n",
    "plt.plot(trans_range, lsr_400, 'ro')\n",
    "\n",
    "plt.legend(shadow='true')\n",
    "plt.axis([5,55,10,110])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "colab_type": "code",
    "id": "viTjfh_jOzFt",
    "outputId": "f0660b1d-567e-4e40-dcee-4b28a48aac3f"
   },
   "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": [
    "t_range = 10\n",
    "total_nodes = 100\n",
    "anchor_percent = 0.20\n",
    "N = int((1 - anchor_percent) * total_nodes) # unknown nodes\n",
    "M = total_nodes - N # anchor nodes\n",
    "\n",
    "# anchor nodes random placement\n",
    "X_anchor = []\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])\n",
    "\n",
    "# unknown nodes random placement\n",
    "X_unknown = []\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",
    "X_unknown = np.array(X_unknown)\n",
    "X_anchor = np.array(X_anchor)\n",
    "\n",
    "#plot\n",
    "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",
    "\n",
    "X_all = []\n",
    "for i in X_unknown:\n",
    "    X_all.append(i)\n",
    "for i in X_anchor:\n",
    "    X_all.append(i)\n",
    "X_all = np.array(X_all)\n",
    "    \n",
    "for i in X_all:\n",
    "    for j in X_all:\n",
    "            if not all (i==j):\n",
    "                d = np.power(((i[0] - j[0])**2 + (i[1] - j[1])**2), 0.5)\n",
    "                if d < t_range:\n",
    "                    plt.plot([i[0], j[0]], [i[1], j[1]], '-', color=\"#98c95e\", linewidth=1)\n",
    "\n",
    "\n",
    "        \n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.29906994, 0.92395142])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 816
    },
    "colab_type": "code",
    "id": "4Vt6ugogmd98",
    "outputId": "7c2e1561-80ac-4aa9-c350-4626eaf2be5b"
   },
   "outputs": [],
   "source": [
    "# !cat /proc/meminfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 955
    },
    "colab_type": "code",
    "id": "bEQ4pRH0TTO7",
    "outputId": "d3805f00-540c-4a25-aee3-e5a06d8746ff"
   },
   "outputs": [],
   "source": [
    "# !cat /proc/cpuinfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "YPSLUuNlTfC7",
    "outputId": "3aed114e-d45b-4731-a137-e579158cba02"
   },
   "outputs": [],
   "source": [
    "# !df -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yAKHERALTiix"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "vaibhav-3.1.ipynb",
   "provenance": []
  },
  "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",
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