Notebooks/5_find_shortest_path.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_graph(adjacency_list, colored_nodes):\n",
    "    G = nx.Graph(adjacency_list)\n",
    "    pos = nx.spring_layout(G)  # You can use different layout algorithms\n",
    "\n",
    "    # Specify colors for nodes\n",
    "    node_colors = ['red' if node in colored_nodes else 'skyblue' for node in G.nodes]\n",
    "\n",
    "    # Draw the graph\n",
    "    nx.draw(G, pos, with_labels=True, font_weight='bold', node_size=700, node_color=node_colors, font_color='black', font_size=8)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "def getGraph(edges):\n",
    "    Graph = {}\n",
    "    \n",
    "    for nodeA,nodeB in edges: \n",
    "        \n",
    "        if nodeA not in Graph:\n",
    "            Graph[nodeA] = []\n",
    "        if nodeB not in Graph:\n",
    "            Graph[nodeB] = []\n",
    "            \n",
    "        Graph[nodeA].append(nodeB)\n",
    "        Graph[nodeB].append(nodeA)\n",
    "    \n",
    "    \n",
    "    return Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edges = [['w','x'],['x','y'],['z','y'],['z','v'],['w','v']]\n",
    "\n",
    "\n",
    "adjacency_list = getGraph(edges=edges)\n",
    "\n",
    "plt.figure(dpi=150)\n",
    "plot_graph(adjacency_list,['w','y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shortest path is :  2\n",
      "The shortest path is :  -1\n"
     ]
    }
   ],
   "source": [
    "# For this the Breadth First Traversal algorithm is more convenient as it explores \n",
    "# more evenly all the different directions.\n",
    "\n",
    "def countShortPartBFT(graph,start,destination):\n",
    "    \n",
    "    \n",
    "    queue = [[start,0]]\n",
    "    visited = set(start)\n",
    "    \n",
    "    while queue:\n",
    "        \n",
    "        current,count = queue.pop(0)\n",
    "        \n",
    "        if current == destination:\n",
    "            return count\n",
    "        \n",
    "        for neighbor in graph[current]:\n",
    "            if neighbor not in visited:\n",
    "                queue.append([neighbor,count+1])\n",
    "                visited.add(neighbor)\n",
    "    \n",
    "    return -1\n",
    "\n",
    "\n",
    "print('The shortest path is : ', countShortPartBFT(adjacency_list,'y','w'))\n",
    "print('The shortest path is : ', countShortPartBFT(adjacency_list,'y','a'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "FFMachine",
   "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": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}