Eigentrust_normalized.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   ],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "small =  {0: [2, 8],\n",
    " 1: [2, 3, 5, 6, 7, 9],\n",
    " 2: [0, 1, 3, 4, 5, 9],\n",
    " 3: [1, 2, 4, 7, 8],\n",
    " 4: [2, 3, 6],\n",
    " 5: [1, 2],\n",
    " 6: [1, 4],\n",
    " 7: [1, 3],\n",
    " 8: [0, 3],\n",
    " 9: [1, 2]}\n",
    "\n",
    "G = nx.from_dict_of_lists(small)\n",
    "\n",
    "creditw_truth = np.random.random(10) < 0.5\n",
    "print(creditw_truth)\n",
    "\n",
    "p_true_posit = 0.90\n",
    "p_false_posit = 0.1\n",
    "std_dev = 0.05\n",
    "\n",
    "G_adj = nx.to_numpy_matrix(G)\n",
    "print(G_adj)\n",
    "local_trust = np.array(G_adj.copy() )\n",
    "\n",
    "for (x,y), element in np.ndenumerate(G_adj):\n",
    "    if element:\n",
    "        if creditw_truth[y]:\n",
    "            rec = min(np.random.normal(p_true_posit, std_dev),1)\n",
    "        else:\n",
    "            rec = max(np.random.normal(p_false_posit, std_dev),0)\n",
    "        local_trust[x][y] = rec\n",
    "    # else, not connected\n",
    "np.set_printoptions(precision=3)\n",
    "print(local_trust)\n",
    "\n",
    "local_trust = np.array(local_trust)\n",
    "print(np.sum(local_trust, axis=1))\n",
    "normalized_local_trust = (local_trust.T / np.sum(local_trust, axis=1)).T\n",
    "print(normalized_local_trust)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "\n",
      "2.313762857920563e-08\n",
      "[0.008 0.327 0.026 0.214 0.009 0.07  0.067 0.133 0.075 0.07 ]\n",
      "\n",
      "\n",
      "1.946895753074833e-08\n",
      "[0.008 0.327 0.026 0.214 0.009 0.07  0.067 0.133 0.075 0.07 ]\n",
      "\n",
      "\n",
      "1.6381986015914104e-08\n",
      "[0.008 0.327 0.026 0.214 0.009 0.07  0.067 0.133 0.075 0.07 ]\n",
      "\n",
      "\n",
      "1.3784480519254982e-08\n",
      "[0.008 0.327 0.026 0.214 0.009 0.07  0.067 0.133 0.075 0.07 ]\n",
      "\n",
      "\n",
      "1.1598832064237115e-08\n",
      "[0.008 0.327 0.026 0.214 0.009 0.07  0.067 0.133 0.075 0.07 ]\n",
      "\n",
      "\n",
      "9.759736963387026e-09\n",
      "[0.008 0.327 0.026 0.214 0.009 0.07  0.067 0.133 0.075 0.07 ]\n",
      "\n",
      "\n",
      "8.212246348299233e-09\n",
      "[0.008 0.327 0.026 0.214 0.009 0.07  0.067 0.133 0.075 0.07 ]\n",
      "\n",
      "\n",
      "6.910123705410345e-09\n",
      "[0.008 0.327 0.026 0.214 0.009 0.07  0.067 0.133 0.075 0.07 ]\n",
      "\n",
      "\n",
      "5.8144638279595755e-09\n",
      "[0.008 0.327 0.026 0.214 0.009 0.07  0.067 0.133 0.075 0.07 ]\n",
      "\n",
      "\n",
      "4.892530269210052e-09\n",
      "[0.008 0.327 0.026 0.214 0.009 0.07  0.067 0.133 0.075 0.07 ]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "e = np.average(normalized_local_trust, axis=0)\n",
    "C = normalized_local_trust\n",
    "print(e)\n",
    "\n",
    "t = np.dot(C.T, e)\n",
    "print('t')\n",
    "\n",
    "scores = t.copy()\n",
    "for i in range(100):\n",
    "    new_scores = np.dot(C.T, scores)\n",
    "    dist = np.linalg.norm(new_scores - scores)\n",
    "    scores = new_scores\n",
    "    print(dist)\n",
    "    print(scores)\n",
    "    print('\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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