exercise_40_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import numpy as np\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_vector(filename):\n",
    "   v = {}\n",
    "   with open(filename, 'r') as f:\n",
    "      for line in f:\n",
    "         fields = line.strip().split('\\t')\n",
    "         value = float(fields[1])\n",
    "         if value > 0: \n",
    "            v[int(fields[0])] = value\n",
    "   v = {x: v[x] / sum(v.values()) for x in v}\n",
    "   return v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.read_edgelist(\"1/data.txt\", delimiter = \"\\t\", nodetype = int)\n",
    "\n",
    "v1 = read_vector(\"1/vector1.txt\")\n",
    "v2 = read_vector(\"1/vector2.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.296478772596982\n"
     ]
    }
   ],
   "source": [
    "spls = dict(nx.shortest_path_length(G))\n",
    "\n",
    "print(sum(v1[origin] * v2[dest] * spls[dest][origin] for origin in v1 for dest in v2))"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}