wie_definiert_man_funktionen.ipynb

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  {
   "source": [
    "# Wie schreibt man eine Funktion?\n",
    "## Funktionen in Python "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def meine_funktion():\n",
    "    print(\"Die Funktion wurde aufgerufen.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Die Funktion wurde aufgerufen.\n"
     ]
    }
   ],
   "source": [
    "meine_funktion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def meine_funktion(input_text):\n",
    "    print(\"Die Funktion wurde aufgerufen \" + input_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Die Funktion wurde aufgerufen mit diesem Text als Argument.\n"
     ]
    }
   ],
   "source": [
    "meine_funktion(\"mit diesem Text als Argument.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def neue_funktion(input_text):\n",
    "    return \"Die Funktion wurde aufgerufen \" + input_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Die Funktion wurde aufgerufen mit diesem Text als Argument.\n"
     ]
    }
   ],
   "source": [
    "txt = \"mit diesem Text als Argument.\"\n",
    "voller_text = neue_funktion(txt)\n",
    "print(voller_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Eine Kugel mit dem Radius 5 hat das Volumen 523.598\nIn der Formel benutzt man pi = 3.14159\n"
     ]
    }
   ],
   "source": [
    "def kugel_volumen(radius, kommastellen):\n",
    "    pi = 3.14159\n",
    "    volumen = (4 / 3) * pi * radius ** 3\n",
    "    volumen = round(volumen, kommastellen)\n",
    "    \n",
    "    return pi, radius, volumen\n",
    "\n",
    "pi, r, vol = kugel_volumen(5, 3)\n",
    "print(\"Eine Kugel mit dem Radius \" + str(r) + \n",
    "        \" hat das Volumen \" + str(vol) +\n",
    "        \"\\nIn der Formel benutzt man pi = \" + str(pi))"
   ]
  },
  {
   "source": [
    "## Auflösung der letzten Aufgabe\n",
    "### Mittelwert"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def einfacher_mittelwert(a, b):\n",
    "    return (a + b) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "5.45\n"
     ]
    }
   ],
   "source": [
    "mittelwert = einfacher_mittelwert(2.5, 8.4)\n",
    "print(mittelwert)"
   ]
  },
  {
   "source": [
    "Für alle Feature ist das aber nicht so geil...\n",
    "Da kann man anstatt ein plus zwischen alle auch die sum-Funktion verwenden."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mittelwert(feature, spalte):\n",
    "    return sum(feature[:,spalte]) / len(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "3.3488372093023258\n"
     ]
    }
   ],
   "source": [
    "print(mittelwert(feature, 0))"
   ]
  },
  {
   "source": [
    "### Linie Plotten"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linie_plotten(feature, labels, w):\n",
    "\n",
    "  x0 = np.array([0, max(feature[:,1])])\n",
    "  x1 = -(w[0] * x0 + w[2]) / w[1]\n",
    "\n",
    "  plt.scatter(feature[:,1], feature[:,2], c=labels)\n",
    "  plt.xlabel('Grösse [cm]')\n",
    "  plt.ylabel('Länge [cm]')\n",
    "  plt.title('Trainingsdaten')\n",
    "\n",
    "  plt.plot(x0, x1)\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "linie_plotten(feature, label, w)"
   ]
  }
 ]
}