exercise_40_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "from scipy.sparse import csgraph"
   ]
  },
  {
   "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",
    "         v[int(fields[0])] = float(fields[1])\n",
    "   v = np.array([v[n] for n in G.nodes])\n",
    "   v /= v.sum()\n",
    "   return v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.read_edgelist(\"data.txt\", delimiter = \"\\t\", nodetype = int)\n",
    "\n",
    "v1 = read_vector(\"vector1.txt\")\n",
    "v2 = read_vector(\"vector2.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = nx.adjacency_matrix(G).todense().astype(float)\n",
    "L = np.linalg.pinv(csgraph.laplacian(np.matrix(A), normed = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.14953767659245623\n"
     ]
    }
   ],
   "source": [
    "diff = v1 - v2\n",
    "print(np.sqrt(diff.T.dot(np.array(L).dot(diff))))"
   ]
  }
 ],
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