From 22e18e94e3ef79467e3968714c87ad303a7c83a8 Mon Sep 17 00:00:00 2001
From: Kevin Huestis <kevin.huestis@tum.de>
Date: Fri, 5 Mar 2021 11:28:50 +0100
Subject: [PATCH] Wie definiert man Klassen?

---
 wie_definiert_man_klassen.ipynb     | 390 ++++++++++++++++++++++++++++
 wie_nutzt_man_while_schleifen.ipynb |  44 +---
 2 files changed, 398 insertions(+), 36 deletions(-)
 create mode 100644 wie_definiert_man_klassen.ipynb

diff --git a/wie_definiert_man_klassen.ipynb b/wie_definiert_man_klassen.ipynb
new file mode 100644
index 0000000..c5fbcc4
--- /dev/null
+++ b/wie_definiert_man_klassen.ipynb
@@ -0,0 +1,390 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Wie definiert man Klassen?\n",
+    "### Objekte in Python"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[1, 3, 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "liste = [1, 2, 3, 4]\n",
+    "liste.remove(2)\n",
+    "print(liste)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zahl = 2\n",
+    "# zahl.remove(2)\n",
+    "# Fehlermeldung"
+   ]
+  },
+  {
+   "source": [
+    "### Klassen"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Perzeptron:\n",
+    "    trainiert = False\n",
+    "\n",
+    "test = Perzeptron()\n",
+    "print(test.trainiert)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Perzeptron:\n",
+    "    trainiert = False\n",
+    "    def __init__(self, max_epochs):\n",
+    "        trainiert = True\n",
+    "        self.max_epochs = max_epochs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "100\n200\n"
+     ]
+    }
+   ],
+   "source": [
+    "test2 = Perzeptron(100)\n",
+    "test3 = Perzeptron(200)\n",
+    "print(test2.max_epochs)\n",
+    "print(test3.max_epochs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "False\nFalse\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(test2.trainiert)\n",
+    "print(test3.trainiert)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Perzeptron:\n",
+    "\n",
+    "    def __init__(self, max_epochs):\n",
+    "        self.trainiert = False\n",
+    "        self.max_epochs = max_epochs"
+   ]
+  },
+  {
+   "source": [
+    "## Perzeptron\n",
+    "### Daten vorbereiten"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "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": 10,
+   "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": [
+    "## Perzptron als Klasse"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Perzeptron():\n",
+    "\n",
+    "    def __init__(self, max_epochs):\n",
+    "        self.w = None\n",
+    "        self.skalierungsfaktor = None\n",
+    "        self.trainiert = False\n",
+    "        self.max_epochs = max_epochs\n",
+    "        self.fehler = np.zeros(max_epochs)\n",
+    "\n",
+    "    def perzeptron(self, x):\n",
+    "        if self.trainiert: x /= self.skalierungsfaktor\n",
+    "        return 1 if np.dot(self.w, x) > 0 else 0\n",
+    "\n",
+    "    def lernschritt(self, feature, labels):\n",
+    "        # 1. Daten Normalisieren\n",
+    "        self.skalierungsfaktor = np.max(feature, 0)\n",
+    "        feature /= self.skalierungsfaktor\n",
+    "        # 2. Training\n",
+    "        iter = 0\n",
+    "        self.w = np.random.rand(feature.shape[1])\n",
+    "        while iter < self.max_epochs:\n",
+    "            for x, label in zip(feature, labels):\n",
+    "                delta = label - self.perzeptron(x)\n",
+    "                if delta != 0: # falsch klassifiziert\n",
+    "                    self.fehler[iter] += 1\n",
+    "                    self.w += (delta * x)    \n",
+    "            if self.fehler[iter] == 0:\n",
+    "                self.trainiert = True\n",
+    "                self.visualize(feature, labels)\n",
+    "                break\n",
+    "            iter += 1\n",
+    "        else:\n",
+    "            print(\"Es wurde keine Lösung gefunden.\")\n",
+    "\n",
+    "    def visualize(self, feature, labels):\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, cmap='coolwarm')\n",
+    "        x0 = np.array([0, 1])\n",
+    "        w = self.w\n",
+    "        if w[1] != 0:\n",
+    "            x1 = -(w[0] * x0 + w[2]) / w[1]\n",
+    "            plt.plot(x0, x1, color='g', label='Gewichte')\n",
+    "            if w[1] > 0:\n",
+    "                ax.fill_between(x0, x1, x1+2, alpha=0.2, color='g', label='Hund')\n",
+    "            else:\n",
+    "                ax.fill_between(x0, x1, x1-1, alpha=0.2, color='g', label='Hund')\n",
+    "            ax.set_ylim([0, max(feature[:,1])*1.1])\n",
+    "        plt.legend()\n",
+    "        plt.show()\n",
+    "\n",
+    "    def falsche_klassifikationen(self):\n",
+    "        plt.plot(range(self.max_epochs), self.fehler)\n",
+    "        plt.xlabel('Epoche')\n",
+    "        plt.ylabel('Falsche Klassifikationen')\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "perzeptron = Perzeptron(100)\n",
+    "perzeptron.lernschritt(feature, labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"262.19625pt\" version=\"1.1\" viewBox=\"0 0 385.78125 262.19625\" width=\"385.78125pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 262.19625 \nL 385.78125 262.19625 \nL 385.78125 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 43.78125 224.64 \nL 378.58125 224.64 \nL 378.58125 7.2 \nL 43.78125 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_1\">\n      <defs>\n       <path d=\"M 0 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CRi2CPEYNTSSMcBqhrIk5lgm1CESWgzQtgrOBNxHsN7wLOC58viJ1B4K0d6WJLYLOTN4UXUvd8wg6q2+OULkmtQiymEcw26NFMEKl3ev7m643yXnlcRFZRJpRQw8Br1qCsiyJRixHAOnnAnQqsoS5APvqLTasqfY8tt129jVayf3uo0woS7jLXhfuc5B2j4Uk3S2CNdUSZqOOGkqeR9B2mG22WVNdfBlwEclHmrWGpoDfB7Ywf9G5/55fsfLTaLWplq1TSaadC9C9/k50LCzed540B6Gz1s5IY/NbVMtGrTLXsJusVYLlIBrtTkJ6EK22s7/RnnedZtH+zqPkCHp/fzP1lgKByBilyRF8Gfge8C+s4CRxJAgE8Yoz3VyA6YRZvGnnAiT1j3fW2hnlLrtrdE9QvrncwzCBIAp467rOO+pqqfH9nuPnhOD7O2jdip+rKLJipQkEk+7+rtxLskQaLZ9XGaXdF2Cm3qRkwZ18/Njgvf7HJ42YgdH3Le4eiQRzwSbt0trdOktBdJd1xNFI3es0RecM3lvx9xciK1qaZPEVZnZG7iVZIvWEFkH6ZG9lXr972pE/SS0CGH3f4pn6/NVQYfR9i6cTciGQQYtgduHKpdqlTGR5SBMIziEIBvvNbE/481jeBctLo9mmVp6rzNPuCxAM/+xxR7vI8UmJ5uj5qDOLF7QIRlwpdDqhLx8YPUcQ26+4c07tWyyyLKQZNbRhKQqyVBqtNtXK/BZBmn0BpuutxLvk4L1FWgRR5TrR3Y0z2pj/mYS77PkrhQ5xzvrCXAgEZf/VdH2oc8L8/Yo751SLQGRZSLtV5UuYW1bi2+5+RX5FyteCHEFsX4B+y0HPzCa0CFLuu9uzRTBRYfeedIvWJZmuNzn0cWvmvTbqSqFJK5pCUPZdv9o31Dlh/n7F8XPC6Hssi8ho0iw695cE3UO3hD/nhK+tSAtyBLF9AfrpXj00fuxiw0d7dbeMPGoo4S571JVCk/Y4gGgG9mijhnp+f+oaEhmrNC2CM4Dj3L0NYGYXAT8B3p1nwfLSaHXlCFLOBZipt3h81xDHWrlEJcW+u726W0YdNTSdcJc96kqhPVsEE5XR1hpKGjWkFoHIspBq0TlgY+zx49IcYGZHmNm3zOxWM7vZzM5J+IyZ2blmttPMbjSzE1KWZ2hJ8wggxVyA2eaCETpp993tVblmMmqoV4tgyLv36LhcRg11fX9rq2oRiCwHaVoEfwH8xMy+BRhBruA9KY5rAn/o7teZ2QZgh5ld7e63xD5zOnB0+PMs4Pzwd24azYXzCCDFXICEMfvR8Yu2CGZbnSWv5x0bjhoaZjkId08cNbSmUsZslBZB73kEjZZTb7bnzWROK6lFUCrZyAFGREaXZtTQ58zs28BvEASCd7n7AymOux+4P3y8x8xuJVi4Lh4IzgQu9mDVsR+Z2UYzOzQ8Nhf1VpsDanPrAkWzbz/6zds55IA1vQ7j4en6grvv6Pgf3vkw7/jCDT2PvWHXI0xWywsq+7W1Mq2280dfuJFFFi9doOWOO6ztKlOpZKytlvnaTQ9w/yODJ3dvuu8xyiXrLNPdKWt49/7OL94wL5CmNVNvLSgrBC2Nb972II/MNAY+Z1rr11R412nHaBkLkR56BgIzO8bdb4t11+wKfz/RzJ7o7tel/SNmtgU4Hrim663DgPheB7vC1+YFAjPbBmwD2Lx5c9o/m6g7R/CkTes45pAN3PHgXu54cG/P46bWT3DiUQcteP2U/3AwV910Pz/Y+VDfv/v8px684LUTNh/I5oMm+eEd/Y/tZfNBk5yweeOC10996hPYcfcvFy1TL6cec/CCoHXc5o0c+fhJfnzXL4c65xEHrWXrkQcueP15TzmYH+x8aOiyLma22ebh6TovfsahPPPIhf9+IgLWawlgM7vQ3beFXULd3N2fn+oPmK0HvgN80N0v63rvK8BfuPv3w+ffAN7p7jt6nW/r1q2+ffv2NH860W/93+/wa1PrOf/Vzxz6HLJy/OsdD/FfP3ENn/v9kzj51x4/7uKIjI2Z7XD3rUnv9WwRuPu28OHp7r6/64S9+1Dmf64KXApc0h0EQruAI2LPDwfuS3PuYXXPI5DVLeriivahEJGF0tSI/5rytXks6Fv4JHCru3+4x8cuB14bjh46CXg0z/wAQL3ZViAokKoCgcii+uUIDiHor19rZscTJIoBDgAmU5z7OcBrgJ+a2fXha+8FNgO4+wXAlQTzFHYCM8DrhriGgTRabWqV4TZskZVHgUBkcf1GDb0Q+G8E3TXxO/o9BBV6X2G/f98aNxwttKTbXnbPI5DVLQr69Za2wxTppV+O4CLgIjN7qbtfuoRlypVyBMXSaRE01SIQ6SXNPIJLzexFwNOBNbHX/zTPguWle60hWd3UNSSyuDSLzl0AvAJ4C0FXz8uBI3MuVy7cfcE8AlndFAhEFpfm1vjZ7v5a4Ffu/gHgZOYP+VwxWu1gNq5aBMURDR9VjkCktzQ1YrROwYyZPRFoAEflV6T8NMLKoDrEWjmyMlXDZHFTLQKRntIsOneFmW0E/gq4DnDg73ItVU7qYWWgFkFxqGtIZHFpAsH/cfdZ4FIzu4IgYbwiV+9qdAKBcgRFUSlp+KjIYtLcGl8WLhVBGBDWAlfnWqqcNNQiKByzYCVVtQhEektTI34J+IKZlcNVRL9Ouv0Ilp1GM8wRKBAUSrVsmkcg0keaeQSfMLMaQUDYArzB3Rdda2g5qqtrqJCqFbUIRPrpt9bQH8SfEgwZvR44ycxO6rOQ3LIVVQbdm67I6lYtl5QjEOmjX4tgQ9fzf+rx+oqhHEExKUcg0l+/tYY+sJQFWQqaR1BM1bIpEIj00a9r6CPu/jYz+2eCuQPzuPtLci1ZDjR8tJiqahGI9NWva+ji8PdfL0VBloJyBMVULZeoN5UjEOmlXyD4K+BU4Ax3f9cSlSdXyhEUk0YNifTXLxAcamb/CXiJmX2erk1m3P26XEuWg7rmERRSTTkCkb76BYL3A+9m4Q5lEOQMnp9XofLS6RrSVpWFohyBSH/9Rg19Efiimf2xu//ZEpYpN+oaKqZqucR0vTXuYogsW/1GDR3j7rcBXzGzE7rfX4ldQwoExVQtl7TEhEgf/bqG/gDYBvxNwnsrsmsoml2qQFAstYpyBCL99Osa2hb+ft7SFSdf0V2hho8Wi3IEIv2l2bP45Wa2IXz8v8zsMjM7Pv+iZa/TNaRkcaFUSqXOrHIRWSjNrfEfu/seM/tN4IXARcAF+RYrH8oRFFOtYp2VZ0VkoTQ1YjTc4kXA+e7+ZaC22EFm9ikze9DMburx/ilm9qiZXR/+vD99sYcT5QiiXaukGNQ1JNJfmkDw72b2/4DfBa40s4mUx30aOG2Rz3zP3Y8Lf/40xTlH0mi1qZVLmCkQFIlGDYn0l6ZC/13ga8Bp7v4IcBDwjsUOcvfvAr8crXjZajTbWnCugIIWgXIEIr2kCQSHAl9x99vN7BTg5cCPM/r7J5vZDWb2VTN7eq8Pmdk2M9tuZtt379499B9rtNpagrqAauUgR+CuYCCSJE2teCnQMrMnA58EjgL+IYO/fR1wpLsfC3yUYCvMRO5+obtvdfetU1NTQ//BesuVKC6g6N+82VYgEEmSplZsu3sT+C/AR9z97QSthJG4+2Puvjd8fCVQNbNNo563nyhHIMUStQKVMBZJlqZWbJjZK4HXAleEr1VH/cNmdoiFWVszOzEsy8OjnrefRks5giKKWgQN7UkgkqjfEhOR1wFnAx9097vM7Cjgs4sdZGafA04BNpnZLuBPCAOIu18AvAx4o5k1gX3AWZ5zJ24QCNQiKJpaGPw1l0Ak2aKBwN1vAd4ae34X8JcpjnvlIu+fB5yXooyZqTeVIyiiTotAgUAk0aKBwMyOBv4CeBqwJnrd3Z+UY7lyoVFDxaRAINJfmlrx74HzgSbwPIK9jD+TZ6HyEiSLlSMomrlksXIEIknSBIK17v4NwNz95+7+v1mBS1CDcgRFFQV/tQhEkqVJFu83sxJwu5m9Gfh34OB8i5WPesuZrCkQFI26hkT6S1Mrvg2YJEgYPxN4DfB7eRYqL1piopgUCET6SzNq6Nrw4V6CoaQrlrqGiin6N69rHoFIon57Fv8zwZaUidz9JbmUKEcKBMVUqyhHINJPvxbBXy9ZKZZIQ2sNFZK6hkT66xcIau5+ddIbZvYh4Dv5FCk/9Va7c3coxaFAINJfv9vjj5nZi+IvmFnJzD4NHJtrqXKirqFi6uQINI9AJFG/FsFvAVeZ2YS7X2Zma4EvAI8Bv70kpctYMGpIgaBoap1F59QiEEnSMxC4+91m9gLga2Z2MMGw0Wvc/Q+WrHQZa7SVIyiiqpLFIn31GzV0QvjwnQTLSlwNfDZ63d2vy7942XF3LTFRUMoRiPTXr2vob2KPbwSeEHvNWWHLTLTajjtqERSQcgQi/fXrGnreUhYkb9GCY1p9tHhqahGI9FWYWjHalEQtguKJlhVRslgkWWFqxehuUDmC4imXDDO1CER6KVwgUIugeMyMarmkHIFID4vWihZ4tZm9P3y+OdxsfkWJNi5XICimWrmkFoFID2lqxY8DJwPRHsR7gI/lVqKcdHIEShYXUrVsCgQiPaTZmOZZ7n6Cmf0EwN1/ZWa1nMuVOeUIiq2qFoFIT2lujxtmViZcktrMpoAV93+UcgTFVi2XtB+BSA9pasVzgX8CDjazDwLfB/4811LlQIGg2GoVtQhEekmzQ9klZrYDOBUw4Hfc/dbcS5axupLFhaYcgUhvaWvF2wlaBZcD02a2ebEDzOxTZvagmd3U430zs3PNbKeZ3Rhb2ygXnRyB9iMoJOUIRHpLM3z0LcAvCBaduwL4Svh7MZ8GTuvz/unA0eHPNuD8FOccmrqGik3zCER6SzNq6BzgKe7+8CAndvfvmtmWPh85E7jY3R34kZltNLND3f3+Qf5OWgoExVYrl7TEhEgPaWrFe4FHc/jbh4XnjuwKX1vAzLaZ2XYz27579+6h/tiGNVWO37yR9RNpYp+sNtWKcgQivfTbjyDagOZO4Ntm9hVgNnrf3T884t9O6qxPbLu7+4XAhQBbt24dqn3/nCdv4jlP3jTMobIKVMsl9u5vjrsYIstSv9vjDeHve8KfWviTlV3AEbHnhwP3ZXh+kY5KSTkCkV767UfwgZz/9uXAm83s88CzgEfzyg+I1NQ1JNJTmlFDV5vZxtjzA83saymO+xzwQ+ApZrbLzF5vZmeb2dnhR64k6HbaCXwC+J9DXYFICho+KtJbmszplLs/Ej0J1xo6eLGD3P2Vi7zvwJtS/H2RkVXLJZrqGhJJlGbUUCs+gczMjqRHUldkuQrmEahFIJIkTYvgfcD3zew74fPnEkwAE1kxalpiQqSnNGsNXRUu/3ASwZDPt7v7Q7mXTCRDVU0oE+kpTbL4OcA+d78CeBzw3rB7SGTFqFZKNJQjEEmUJkdwPjBjZscC7wB+Dlyca6lEMhblCIIxCiISlyYQNMMRPmcC57r73zI32UxkRYh2pmu2FQhEuqVJFu8xs/cArwaeG+5WVs23WCLZihYbbLTaWnhQpEua/yNeQbDG0Ovd/QGCheH+KtdSiWSsEwi0XaXIAmlGDT0AfDj2/B6UI5AVploJAoHmEogs1G/10T0kTxwzgonBB+RWKpGMRTkCzSUQWajfonNKCMuqEc8RiMh8qXdpCdcXWhM9D7uIRFYEBQKR3tJMKHuJmd0O3AV8B7gb+GrO5RLJVBQI6koWiyyQZtTQnxEsL/Fv7n4UcCrwg1xLJZKxWkU5ApFe0gSCRrhxfcnMSu7+LeC4nMslkil1DYn0liZH8IiZrQe+C1xiZg8C2vxVVpRO15ACgcgCPVsEsT0IzgRmgLcDVwF3AL+df9FEsjPXIlCOQKRbvxbBl4AT3H3azC5195cCFy1RuUQyVevMLFaLQKRbvxyBxR4/Ke+CiOSpqmSxSE/9AoH3eCyy4ihHINJbv66hY83sMYKWwdrwMWiJCVmBasoRiPTUb4mJ8lIWRCRPGj4q0psWZpdCqGrROZGeFAikEDrLUGvUkMgCuQYCMzvNzH5mZjvN7N0J759iZo+a2fXhz/vzLI8Ul3IEIr2lXgy/F3MAAAkZSURBVH10UOGWlh8D/jOwC7jWzC5391u6Pvo9d39xXuUQAeUIRPrJs0VwIrDT3e909zrweYJZyiJLrlwySqZAIJIkz0BwGHBv7Pmu8LVuJ5vZDWb2VTN7etKJzGybmW03s+27d+/Oo6xSANVySfMIRBLkGQgs4bXuDtrrgCPd/VjgowTLWiw8yP1Cd9/q7lunpqYyLqYURa1c0ub1IgnyDAS7gCNizw8H7ot/wN0fc/e94eMrgaqZbcqxTFJg1UqJZlstApFueQaCa4GjzewoM6sBZwGXxz9gZoeYmYWPTwzL83COZZICq5ZNOQKRBLmNGnL3ppm9GfgaUAY+5e43m9nZ4fsXAC8D3mhmTWAfcJa7q+0uuaiWS9qqUiRBboEAOt09V3a9dkHs8XnAeXmWQSRSK5fUIhBJoJnFUhhVBQKRRAoEUhjVinIEIkkUCKQwgnkEyhGIdFMgkMKolkraqlIkgQKBFIa6hkSSKRBIYShZLJJMgUAKQzkCkWQKBFIYmkcgkkyBQApDS0yIJFMgkMKoljVqSCSJAoEURrWiHIFIEgUCKQzlCESSKRBIYShHIJJMgUAKQ/MIRJIpEEhhBIHA0ZYXIvMpEEhh1CrBf+4NJYxF5lEgkMKolg1A3UMiXRQIpDCq5ahFoEAgEqdAIIURBYK6AoHIPAoEUhi1snIEIkkUCKQwqpUwR6BlJkTmUSCQwlCOQCSZAoEUhnIEIskUCKQwlCMQSZZrIDCz08zsZ2a208zenfC+mdm54fs3mtkJeZZHik1dQyLJcgsEZlYGPgacDjwNeKWZPa3rY6cDR4c/24Dz8yqPSGdCmZLFIvNUcjz3icBOd78TwMw+D5wJ3BL7zJnAxR4s/vIjM9toZoe6+/05lksKqhouMfGOL97IZK085tKIDO4Vv3EE/+M/Pinz8+YZCA4D7o093wU8K8VnDgPmBQIz20bQYmDz5s2ZF1SK4amHHMArth7BntnGuIsiMpRN6ydyOW+egcASXuvO0qX5DO5+IXAhwNatW5Xpk6GsrZX50MueMe5iiCw7eSaLdwFHxJ4fDtw3xGdERCRHeQaCa4GjzewoM6sBZwGXd33mcuC14eihk4BHlR8QEVlauXUNuXvTzN4MfA0oA59y95vN7Ozw/QuAK4EzgJ3ADPC6vMojIiLJ8swR4O5XElT28dcuiD124E15lkFERPrTzGIRkYJTIBARKTgFAhGRglMgEBEpOAvytSuHme0Gfj7k4ZuAhzIszkpRxOsu4jVDMa+7iNcMg1/3ke4+lfTGigsEozCz7e6+ddzlWGpFvO4iXjMU87qLeM2Q7XWra0hEpOAUCERECq5ogeDCcRdgTIp43UW8ZijmdRfxmiHD6y5UjkBERBYqWotARES6KBCIiBRcYQKBmZ1mZj8zs51m9u5xlycPZnaEmX3LzG41s5vN7Jzw9YPM7Gozuz38feC4y5o1Myub2U/M7IrweRGueaOZfdHMbgv/zU8uyHW/Pfzv+yYz+5yZrVlt121mnzKzB83spthrPa/RzN4T1m0/M7MXDvr3ChEIzKwMfAw4HXga8Eoze9p4S5WLJvCH7v5U4CTgTeF1vhv4hrsfDXwjfL7anAPcGntehGv+W+Aqdz8GOJbg+lf1dZvZYcBbga3u/usES9yfxeq77k8Dp3W9lniN4f/jZwFPD4/5eFjnpVaIQACcCOx09zvdvQ58HjhzzGXKnLvf7+7XhY/3EFQMhxFc60Xhxy4Cfmc8JcyHmR0OvAj4u9jLq/2aDwCeC3wSwN3r7v4Iq/y6QxVgrZlVgEmCXQ1X1XW7+3eBX3a93OsazwQ+7+6z7n4Xwf4uJw7y94oSCA4D7o093xW+tmqZ2RbgeOAa4AnRzm/h74PHV7JcfAR4J9COvbbar/lJwG7g78Musb8zs3Ws8ut2938H/hq4B7ifYFfDr7PKrzvU6xpHrt+KEggs4bVVO27WzNYDlwJvc/fHxl2ePJnZi4EH3X3HuMuyxCrACcD57n48MM3K7w5ZVNgvfiZwFPBEYJ2ZvXq8pRq7keu3ogSCXcARseeHEzQnVx0zqxIEgUvc/bLw5V+Y2aHh+4cCD46rfDl4DvASM7uboMvv+Wb2WVb3NUPw3/Qud78mfP5FgsCw2q/7BcBd7r7b3RvAZcCzWf3XDb2vceT6rSiB4FrgaDM7ysxqBImVy8dcpsyZmRH0Gd/q7h+OvXU58Hvh498DvrzUZcuLu7/H3Q939y0E/67fdPdXs4qvGcDdHwDuNbOnhC+dCtzCKr9ugi6hk8xsMvzv/VSCXNhqv27ofY2XA2eZ2YSZHQUcDfx4oDO7eyF+gDOAfwPuAN437vLkdI2/SdAkvBG4Pvw5A3g8wSiD28PfB427rDld/ynAFeHjVX/NwHHA9vDf+0vAgQW57g8AtwE3AZ8BJlbbdQOfI8iBNAju+F/f7xqB94V128+A0wf9e1piQkSk4IrSNSQiIj0oEIiIFJwCgYhIwSkQiIgUnAKBiEjBKRCIAGbWMrPrYz+ZzdI1sy3xVSRFlpvKuAsgskzsc/fjxl0IkXFQi0CkDzO728w+ZGY/Dn+eHL5+pJl9w8xuDH9vDl9/gpn9k5ndEP48OzxV2cw+Ea6j/3UzWxt+/tfM7Coz22Fm3zOzY8Z0qVJgCgQigbVdXUOviL33mLufCJxHsNIp4eOL3f0ZwCXAueHr5wLfcfdjCdb+uTl8/WjgY+7+dOAR4KXh6xcCb3H3ZwJ/BHw8p+sT6Ukzi0UAM9vr7usTXr8beL673xku6PeAuz/ezB4CDnX3Rvj6/e6+ycx2A4e7+2zsHFuAqz3YUAQzexdQJQgquwmWBYhMeLCxkMiSUY5AZHHe43GvzySZjT1uAWsJWuSPKDch46auIZHFvSL2+4fh438lWO0U4FXA98PH3wDeCJ19lA/odVIP9oq4y8xeHn7ezOzYjMsusigFApFAd47gL2PvTZjZNQT7Ir89fO2twOvM7EbgNeF7hL+fZ2Y/BXYQ7CPbz6uA15vZDQT5hFW3haosf8oRiPQR5gi2uvtD4y6LSF7UIhARKTi1CERECk4tAhGRglMgEBEpOAUCEZGCUyAQESk4BQIRkYL7/6XeD4y7FlJEAAAAAElFTkSuQmCC\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "perzeptron.falsche_klassifikationen()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Das ist kein Hund...\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_neu = [24.5, 13.8, 1]\n",
+    "if perzeptron.perzeptron(x_neu):\n",
+    "    print(\"Das ist ein Hund!\")\n",
+    "else:\n",
+    "    print(\"Das ist kein Hund...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ]
+}
\ No newline at end of file
diff --git a/wie_nutzt_man_while_schleifen.ipynb b/wie_nutzt_man_while_schleifen.ipynb
index da33240..0866d38 100644
--- a/wie_nutzt_man_while_schleifen.ipynb
+++ b/wie_nutzt_man_while_schleifen.ipynb
@@ -502,44 +502,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# parameter\n",
     "sigma = 0.7\n",
-    "num_dogs = 26\n",
-    "num_cats = 53\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create random data\n",
-    "X1 = sigma * np.random.randn(num_dogs,2) + (3.3, 4.42)\n",
-    "X2 = sigma * np.random.randn(num_cats,2) + (1.4, 2.82)\n",
+    "hunde = 26\n",
+    "sonst = 53\n",
+    "\n",
+    "X1 = sigma * np.random.randn(hunde, 2) + (3.3, 4.42)\n",
+    "X2 = sigma * np.random.randn(sonst, 2) + (1.4, 2.82)\n",
     "feature = np.concatenate((X1, X2))\n",
-    "labels = np.concatenate((np.ones(num_dogs), np.zeros(num_cats)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "plt.scatter(feature[:,0], feature[:,1], c=labels)\n",
+    "label = np.concatenate((np.ones(hunde), np.zeros(sonst)))\n",
+    "\n",
+    "plt.scatter(feature[:,0], feature[:,1], c=label)\n",
     "plt.xlabel('Grösse [cm]')\n",
     "plt.ylabel('Breite [cm]')\n",
     "plt.title('Trainingsdaten')\n",