was_ist_die_perzeptron_lernregel.ipynb

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  {
   "source": [
    "# Was ist die Perzeptron Lernregel\n",
    "## Daten Laden"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "[[37.92655435 23.90101111  1.        ]\n [35.88942857 22.73639281  1.        ]\n [29.49674574 21.42168559  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "feature = np.array([[37.92655435, 23.90101111],\n",
    "                    [35.88942857, 22.73639281],\n",
    "                    [29.49674574, 21.42168559],\n",
    "                    [32.48016326, 21.7340484 ],\n",
    "                    [38.00676226, 24.37202837],\n",
    "                    [30.73073988, 22.69832608],\n",
    "                    [35.93672343, 21.07445241],\n",
    "                    [38.65212459, 20.57099727],\n",
    "                    [35.52041768, 21.74519457],\n",
    "                    [37.69535497, 20.33073640],\n",
    "                    [33.00699292, 22.57063861],\n",
    "                    [33.73140934, 23.81730782],\n",
    "                    [43.85053380, 20.05153803],\n",
    "                    [32.95555986, 24.12153986],\n",
    "                    [36.38192916, 19.20280266],\n",
    "                    [36.54270168, 20.45388966],\n",
    "                    [33.08246118, 22.20524015],\n",
    "                    [31.76866280, 21.01201139],\n",
    "                    [42.24260825, 20.44394610],\n",
    "                    [29.04450264, 22.46633771],\n",
    "                    [30.04284328, 21.54561621],\n",
    "                    [18.95626707, 19.66737753],\n",
    "                    [18.60176718, 17.74023009],\n",
    "                    [12.85314993, 18.42746953],\n",
    "                    [28.62450072, 17.94781944],\n",
    "                    [21.00655655, 19.33438286],\n",
    "                    [17.33580556, 18.81696459],\n",
    "                    [31.17129195, 17.23625014],\n",
    "                    [19.36176482, 20.67772798],\n",
    "                    [27.26581705, 16.71312863],\n",
    "                    [21.19107828, 19.00673617],\n",
    "                    [19.08131597, 15.24401994],\n",
    "                    [26.69761925, 17.05937466],\n",
    "                    [4.44136559 , 3.52432493 ],\n",
    "                    [10.26395607, 1.07729281 ],\n",
    "                    [7.39058439 , 3.44234423 ],\n",
    "                    [4.23565118 , 4.28840232 ],\n",
    "                    [3.87875761 , 5.12407692 ],\n",
    "                    [15.12959925, 6.26045879 ],\n",
    "                    [5.93041263 , 1.70841905 ],\n",
    "                    [4.25054779 , 5.01371294 ],\n",
    "                    [2.15139117 , 4.16668657 ],\n",
    "                    [2.38283228 , 3.83347914 ]])\n",
    "\n",
    "feature = np.concatenate((feature, np.ones(43).reshape(43,1)), axis=1)\n",
    "print(feature[0:3,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(43,)\n"
     ]
    }
   ],
   "source": [
    "labels = np.concatenate((np.ones(21), np.zeros(22)))\n",
    "print(labels.shape)"
   ]
  },
  {
   "source": [
    "## Visualisieren"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize(feature, labels, w, x=None, w_old=None): \n",
    "    # Hier werden x und w_old als None definiert, sodass man sie beim Aufrufen nicht \n",
    "    # unbedingt angeben muss. Sie könnten auch als [1,2,3,4] o.Ä. definiert werden, \n",
    "    # aber None macht es einfacher für die if-Statements\n",
    "\n",
    "    _, ax = plt.subplots()\n",
    "    plt.title('Trainingsdaten')\n",
    "    plt.xlabel('Grösse [cm]')\n",
    "    plt.ylabel('Länge [cm]')\n",
    "    plt.scatter(feature[:,0], feature[:,1], c=labels)\n",
    "\n",
    "    # Linie\n",
    "    x0 = np.array([min(feature[:,1]), max(feature[:,0])])\n",
    "    if w[1] != 0:\n",
    "        x1 = -(w[0] * x0 + w[2]) / w[1]\n",
    "        plt.plot(x0, x1, label='Gewichte')\n",
    "        if w[1] > 0:\n",
    "            ax.fill_between(x0, x1, x1+5, alpha=0.2,label='Hund')\n",
    "        else:\n",
    "            ax.fill_between(x0, x1, x1-5, alpha=0.2,label='Hund')\n",
    "\n",
    "    if x is not None:\n",
    "        plt.scatter(x[0], x[1],c='r',marker='x',label='Falsch klass.')\n",
    "    \n",
    "    if w_old is not None and w_old[1] != 0:\n",
    "        x1_new = -(w_old[0] * x0 + w_old[2]) / w_old[1]\n",
    "        plt.plot(x0, x1_new, 'r',label='Alten Gewichte')\n",
    "        if w_old[1] > 0:\n",
    "            ax.fill_between(x0, x1_new, x1_new+5, alpha=0.2)\n",
    "        else:\n",
    "            ax.fill_between(x0, x1_new, x1_new-5, alpha=0.2)\n",
    "\n",
    "    plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1.1))\n",
    "    plt.show()"
   ]
  },
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "np.random.seed(5) # damit die 'zufälligen' Zahlen immer gleich sind\n",
    "\n",
    "# gewichte zufällig initialsieren\n",
    "w = np.random.rand(feature.shape[1])\n",
    "visualize(feature, labels, w)"
   ]
  },
  {
   "source": [
    "## Perzeptron Lernregel"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perzeptron(w, x):\n",
    "    if np.dot(w, x) > 0:\n",
    "        return 1.0\n",
    "    else:\n",
    "        return 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = feature[0]\n",
    "label = labels[0]\n",
    "\n",
    "ausgabe = perzeptron(w, x)\n",
    "\n",
    "if ausgabe == label:\n",
    "    pass\n",
    "elif ausgabe == 1 and label == 0:\n",
    "    w += x\n",
    "elif ausgabe == 0 and label == 1:\n",
    "    w -= x"
   ]
  },
  {
   "source": [
    "## Auflösung der letzten Aufgabe"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Das ist ein Hund!\n"
     ]
    }
   ],
   "source": [
    "hund = perzeptron(w, x)\n",
    "if hund == 1:\n",
    "    print(\"Das ist ein Hund!\")\n",
    "elif hund == 0:\n",
    "    print(\"Das ist kein Hund.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Das ist ein Hund!\n"
     ]
    }
   ],
   "source": [
    "if perzeptron(w, x):\n",
    "    print(\"Das ist ein Hund!\")\n",
    "else:\n",
    "    print(\"Das ist kein Hund.\")"
   ]
  }
 ]
}