{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# import paths\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, \"../../\")\n",
    "from gklearn.utils.graphfiles import loadDataset\n",
    "# from ged.GED import ged\n",
    "# from ged.costfunctions import RiesenCostFunction, BasicCostFunction\n",
    "# from ged.bipartiteGED import computeBipartiteCostMatrix, getOptimalMapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
      "  if cb.is_numlike(alpha):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4]\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "dataset, y = loadDataset(\"../../datasets/acyclic/dataset_bps.ds\")\n",
    "nx.draw_networkx(dataset[12])\n",
    "plt.show()\n",
    "print(list(dataset[12][3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'BasicCostFunction' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-a41ca3b574d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasicCostFunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mged_distances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'BasicCostFunction' is not defined"
     ]
    }
   ],
   "source": [
    "cf = BasicCostFunction(1,3,1,3)\n",
    "N=len(dataset)\n",
    "N=10\n",
    "ged_distances = np.zeros((N,N))\n",
    "for i in range(0,N):\n",
    "    for j in range(i,N):\n",
    "          ged_distances[j,i] = ged_distances[i,j] = ged(dataset[i],dataset[j],cf=cf, method='Riesen')[0]\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ged' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-4405a37a6d30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mG2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mG2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mG2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Basic'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mG2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Riesen'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ged' is not defined"
     ]
    }
   ],
   "source": [
    "G1=dataset[12]\n",
    "G2=dataset[12]\n",
    "\n",
    "print(ged(G1,G2))\n",
    "print(ged(G1,G2,method='Basic'))\n",
    "print(ged(G1,G2,method='Riesen'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: {0: [0], 3: [0, 3], 1: [0, 3, 1], 4: [0, 3, 4], 2: [0, 3, 4, 2]}, 1: {1: [1], 3: [1, 3], 0: [1, 3, 0], 4: [1, 3, 4], 2: [1, 3, 4, 2]}, 2: {2: [2], 4: [2, 4], 3: [2, 4, 3], 0: [2, 4, 3, 0], 1: [2, 4, 3, 1]}, 3: {3: [3], 0: [3, 0], 1: [3, 1], 4: [3, 4], 2: [3, 4, 2]}, 4: {4: [4], 2: [4, 2], 3: [4, 3], 0: [4, 3, 0], 1: [4, 3, 1]}}\n",
      "[[0. 2. 3. 1. 2.]\n",
      " [2. 0. 3. 1. 2.]\n",
      " [3. 3. 0. 2. 1.]\n",
      " [1. 1. 2. 0. 1.]\n",
      " [2. 2. 1. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "l = nx.shortest_path(G1)\n",
    "print(l)\n",
    "\n",
    "distances = np.zeros((G1.number_of_nodes(),G1.number_of_nodes()))\n",
    "for i in l.keys():\n",
    "    for j in l[i].keys():\n",
    "        distances[i,j] = len(l[i][j])-1\n",
    "\n",
    "print(distances)\n"
   ]
  }
 ],
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