03-search_lsg.ipynb

{
 "cells": [
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Manuelles Ausrechnen\n",
    "Bevor wir die Suchalgorithmen implementieren, soll es in dieser Aufgabe darum gehen, die Algorithmen einmal \"per Hand\", mit Papier und Stift, durchzurechnen.\n",
    "\n",
    "![grafik.png](attachment:grafik.png)\n",
    "\n",
    "* Benutzen Sie die folgenden Suchalgorithmen um einen Weg zwischen dem Start und dem Ziel zu finden. Notieren Sie Ihre Berechnungen für jeden Schritt. Geben Sie Insbesondere den Inhalt des Stacks / der Queue in jedem Schritt an.\n",
    "    * Breitensuche\n",
    "    * Tiefensuche \n",
    "    * A* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Breitensuche: Reihenfolge der besuchten Knoten: 1 → 2 → 7 → 4 → 5 → 6 → Ziel\n",
    "* Tiefensuche: Reihenfolge der besuchten Knoten: 1 → 4 → 8 → Ziel\n",
    "* A*: \n",
    "\n",
    "A. f(1) = 85 + 272 = 357, f(2) = 217 + 219 = 436, f(7) = 173 + 383\n",
    "= 556 ⇒ Wähle Knoten 1\n",
    "\n",
    "B. f(2) = 217 + 219 = 436, f(7) = 173 + 383 = 556, f(4) = 85 + 80 + 253 = 418 ⇒ Wähle Knoten 4\n",
    "\n",
    "C. f(2) = 217 + 219 = 436, f(7) = 173 + 383 = 556, f(8) = 85 + 80 + 250 + 57 = 472 ⇒ Wähle Knoten 2\n",
    "\n",
    "D. f(7) = 173 + 383 = 556, f(8) = 85 + 80 + 250 + 57 = 472, f(5) =\n",
    "217 + 186 + 318 = 721, f(6)= 217 + 103 + 150 = 470 ⇒ Wähle Knoten 6\n",
    "\n",
    "E. f(7) = 173 + 383 = 556, f(8) = 85 + 80 + 250 + 57 = 472, f(5) =\n",
    "217 + 186 + 318 = 721, f(3) = 320 + 183 + 189 = 692 ⇒ Wähle Knoten 8\n",
    "\n",
    "F. f(7) = 173 + 383 = 556, f(Ziel) = 487, f(3) = 320 + 183 + 189 =\n",
    "692 ⇒ Wähle Ziel, fertig (Weg zum Ziel 2-6-Ziel, mit Kosten 487)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Navigation in der Staubsauger-Welt\n",
    "\n",
    "Nun schauen wir uns an, wie die Suchalgorithmen benutzt werden können, um einen Roboter kürzeste Wege finden zu lassen.\n",
    "\n",
    "Wir nehmen an, dass der Roboter eine Karte der Umgebung hat, und weiß, an welchem Punkt er sich befindet. Um das Ganze einfacher zu machen, nehmen wir außerdem an, dass die Karte ein einfaches 2D-Raster von Zellen ist. Der Roboter kann jede Zelle entweder betreten (`0`) oder die Zelle ist blockiert (`1`). \n",
    "In unserer Implementierung ist die Karte eine Matrix von Integers. Zusätzlich gibt es Convenience-Funktionen, um die Nachbarn einer Zelle zu erhalten, und die Distanz zwischen zwei Zellen auszurechnen.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(5, 1), (4, 0), (4, 2)]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "def distance(x, y):\n",
    "    return (((x[0]-y[0]))**2+((x[1]-y[1])**2))\n",
    "\n",
    "\n",
    "class Map:\n",
    "    def __init__(self,m : np.ndarray) -> None:\n",
    "        self.m = m\n",
    "    def neighbors(self,cell):\n",
    "        nrow,ncol = m.shape\n",
    "        x,y = cell\n",
    "        nb = []\n",
    "        if x>0:\n",
    "            if m[x-1,y]==0:\n",
    "                nb = nb + [(x-1,y)]\n",
    "        if x<(nrow-1):\n",
    "            if m[x+1,y]==0:\n",
    "                nb = nb + [(x+1,y)]\n",
    "        if y>0:\n",
    "            if m[x,y-1]==0:\n",
    "                nb = nb + [(x,y-1)]\n",
    "        if y<(ncol-1):\n",
    "            if m[x,y+1]==0:\n",
    "                nb = nb + [(x,y+1)]\n",
    "        return nb\n",
    "    \n",
    "    \n",
    "    \n",
    "m = np.array([[0,1,0,0,0,0,0],[0,1,0,1,0,0,1],[0,1,0,1,0,1,0],[0,1,0,1,0,0,0],[0,0,0,1,1,0,0],[0,0,0,1,1,0,0]])\n",
    "mm = Map(m)\n",
    "mm.neighbors((4,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aufgabe\n",
    "\n",
    "* Implementieren Sie Tiefensuche, um einen Weg vom Start zum Ziel zu finden. Als Ausgabe soll ein Dictionary ausgegeben werden, dass für jeden (besuchten) Knoten den Vorgänger angibt.\n",
    "* Implementieren Sie eine Funktion `make_path(came_from,start,goal)`, die ein solches Dictionary in einen Weg vom Start zum Ziel (als Liste von Zellen) umwandelt. Ändern Sie Ihre Implementierung der Tiefensuche entsprechend ab, sodass direkt der Pfad ausgegeben wird.\n",
    "* Implementieren Sie den A*-Algorithmus, um von einem gegebenen Start den kürzesten Weg zum Ziel zu finden. Auch hier soll der Pfad als Liste von Zellen ausgegeben werden. \n",
    "* Testen Sie die Implementierungen mit der Karte `mm`, indem Sie den Weg von `(0,0)` zu `(5,6)` mit beiden Algorithmen ausrechnen. \n",
    "\n",
    "Die Implementierungen eines Stacks und einer Priority Queue (Prioritätswarteschlange) sind bereits gegeben und können von Ihnen verwendet werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import heapq\n",
    "import math\n",
    "\n",
    "class PriorityQueue:\n",
    "    def __init__(self):\n",
    "        self.elements = []\n",
    "    \n",
    "    def empty(self):\n",
    "        return len(self.elements) == 0\n",
    "    \n",
    "    def put(self, item, priority):\n",
    "        heapq.heappush(self.elements, (priority, item))\n",
    "    \n",
    "    def get(self):\n",
    "        return heapq.heappop(self.elements)[1]\n",
    "    \n",
    "class Stack:\n",
    "    def __init__(self):\n",
    "        self.elements = []\n",
    "    \n",
    "    def empty(self):\n",
    "        return len(self.elements) == 0\n",
    "    \n",
    "    def put(self, x):\n",
    "        self.elements.append(x)\n",
    "    \n",
    "    def get(self):\n",
    "        return self.elements.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dfs(map : Map,start,goal):\n",
    "    frontier = Stack()\n",
    "    frontier.put(start)\n",
    "    came_from = {}\n",
    "    came_from[start] = None\n",
    "    \n",
    "    while not frontier.empty():\n",
    "        current = frontier.get()\n",
    "        for next in map.neighbors(current):\n",
    "            if next not in came_from:\n",
    "                frontier.put(next)\n",
    "                came_from[next] = current\n",
    "    \n",
    "    return make_path(came_from,start,goal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_path(came_from,start,goal):\n",
    "    path=[goal]\n",
    "    if start==goal:\n",
    "        return [start]\n",
    "    nex = came_from[goal]\n",
    "    while nex != start:\n",
    "        path.append(nex)\n",
    "        nex = came_from[nex]\n",
    "    path.reverse()\n",
    "    return [start] + path\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def astar(map : Map, start, goal):\n",
    "    frontier = PriorityQueue()\n",
    "    frontier.put(start, 0)\n",
    "    came_from = {}\n",
    "    cost_so_far = {}\n",
    "    came_from[start] = None\n",
    "    cost_so_far[start] = 0\n",
    "    \n",
    "    while not frontier.empty():\n",
    "        current = frontier.get()\n",
    "        #print(\"Visiting: \",end=\"\")\n",
    "        #print(current,end=\"\")\n",
    "        #print(\" with cost: \",end=\"\")\n",
    "        #print(cost_so_far[current])\n",
    "        if current == goal:\n",
    "            #print(\"Goal found: \",end=\"\")\n",
    "            #print(goal)\n",
    "            break\n",
    "        \n",
    "        #calculate new cost for each neighbor\n",
    "        nn = map.neighbors(current)\n",
    "        for nexttile in nn:\n",
    "            nextcost = distance(current,goal)\n",
    "            new_cost = cost_so_far[current] + nextcost\n",
    "            if nexttile not in cost_so_far or new_cost < cost_so_far[nexttile]:\n",
    "                cost_so_far[nexttile] = new_cost\n",
    "                #print(\"Heuristic between \",goal,\" and \",nexttile,\": \",end=\"\" )\n",
    "                #print(distance(nexttile,goal))\n",
    "                priority = new_cost # + distance(nexttile,goal)\n",
    "                #print(\"Adding \",nexttile,\" with priority \",end=\"\")\n",
    "                #print(priority)\n",
    "                frontier.put(nexttile, priority)\n",
    "                came_from[nexttile] = current\n",
    "    return make_path(came_from,start,goal) #, cost_so_far\n",
    "\n",
    "\n",
    "path1 = dfs(mm,(0,0),(5,6))\n",
    "path2 = astar(mm,(0,0),(5,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (4, 1), (4, 2), (3, 2), (2, 2), (1, 2), (0, 2), (0, 3), (0, 4), (1, 4), (2, 4), (3, 4), (3, 5), (3, 6), (4, 6), (5, 6)]\n",
      "[(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (4, 1), (4, 2), (3, 2), (2, 2), (1, 2), (0, 2), (0, 3), (0, 4), (1, 4), (2, 4), (3, 4), (3, 5), (4, 5), (4, 6), (5, 6)]\n"
     ]
    }
   ],
   "source": [
    "print(path1)\n",
    "print(path2)"
   ]
  },
  {
   "attachments": {
    "grafik.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aufgabe\n",
    "Visualisieren Sie die Karte, und den gefundenen Pfad. Tip: Nutzen Sie das Paket `matplotlib`.\n",
    "\n",
    "Das Ergebnis kann beispielsweise so aussehen:\n",
    "![grafik.png](attachment:grafik.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 560x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 560x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def drawMap(map,path = None):\n",
    "    plt.matshow(map)\n",
    "    if path is not None:\n",
    "        x =  [x[0] for x in path]\n",
    "        y = [x[1] for x in path]\n",
    "        fig = plt.plot(y,x)\n",
    "\n",
    "\n",
    "drawMap(mm.m,path1)\n",
    "drawMap(mm.m,path2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}