commit c6c758207b4c38278aaaf3d824378514143a98d6
Author: Paul <poggel.paul@gmail.com>
Date:   Sat Nov 14 23:47:31 2020 +0100

    inital commit

diff --git a/ML_U3_2.ipynb b/ML_U3_2.ipynb
new file mode 100644
index 0000000..2b10c73
--- /dev/null
+++ b/ML_U3_2.ipynb
@@ -0,0 +1,195 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Maschinelles Lernen (ML) - Übung 3\n",
+    "# Perzeptronen und mehrschichtige Perzeptronen\n",
+    "# 3.2 Loss Function, Backpropagation und Gradient Descent\n",
+    "\n",
+    "In dieser Aufgabe wird die Aktualisierungsfunktion (Lernalgorithmus) aus der Vorlesung durch das Verfahren der Fehlerrückführung (engl. backpropagation) und des Gradientenverfahrens (engl. gradient descent) ersetzt.\n",
+    "\n",
+    "In dieser Übung sollen Sie das bereits ausgearbeitete Perzeptron anpassen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# matplotlib: Modul zum Plotten von Daten\n",
+    "from matplotlib import pyplot as plt \n",
+    "\n",
+    "# numpy: Mathematikbibliothek\n",
+    "import numpy as np \n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Aufgabe 1:** Ersetzen Sie die bisherige Aktivierung in der Methode *predict* durch die Sigmoidfunktion.\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Aufgabe 2:** Implementieren Sie das beschriebene Gradientenlernverfahren mit Backpropagation in die Methode *fit*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Aufgabe 3:** Schauen Sie sich die Ausgabe des Perzeptrons (perceptron.predict(.)) auf einem der bisher verwendeten Datensätze (z.B. *AND*, Iris) an. Was fällt gegenüber einem Perzeptron mit der Signum-Funktion als Aktivierung auf? Was bedeutet das für den Einsatz als binärer Klassifikator?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Perceptron(object):\n",
+    "    def __init__(self, number_of_inputs, epochs, eta):\n",
+    "        \"\"\"\n",
+    "        Beispielaufruf des Konstruktors:\n",
+    "        >>> Perceptron(2, 100, 0.1)\n",
+    "        \"\"\"\n",
+    "        ### Dein Code kommt hierhin:\n",
+    "        \n",
+    "        ##########################\n",
+    "        pass\n",
+    "           \n",
+    "    def predict(self, inputs):\n",
+    "        \"\"\"\n",
+    "        Beispiel des Funktionsaufrufes:\n",
+    "        >>> inputs = np.array([0, 1])\n",
+    "        >>> h = perceptron.predict(inputs) \n",
+    "        \"\"\"\n",
+    "        \n",
+    "        # Dein Code kommt hierhin:    \n",
+    "        \n",
+    "        \n",
+    "        ##########################\n",
+    "        \n",
+    "        \n",
+    "        pass\n",
+    "\n",
+    "    def fit(self, training_inputs, labels):\n",
+    "        \"\"\"\n",
+    "        Beispiel des Funktionsaufrufs:\n",
+    "        >>> perceptron.fit(train_input, labels)\n",
+    "        \"\"\"\n",
+    "        \n",
+    "        # Dein Code kommt hierhin:\n",
+    "        \n",
+    "\n",
+    "        ##########################\n",
+    "        pass\n",
+    "            \n",
+    "    def status(self):\n",
+    "        \"\"\"\n",
+    "        Die Methode status(...) gibt die aktuellen Gewichte aus.\n",
+    "\n",
+    "        Beispiel des Funktionsaufrufes und der Ausgabe:\n",
+    "        >>> perceptron.status()\n",
+    "        Perceptron weights:  [0. 1. 1.]\n",
+    "        \"\"\"\n",
+    "        print(\"Perceptron weights: \", self.weights)\n",
+    "        \n",
+    "    def getWeights(self):\n",
+    "        return self.weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# AND-Datensatz\n",
+    "train_input = np.array([\n",
+    "                        [0, 0],\n",
+    "                        [0, 1],\n",
+    "                        [1, 0],\n",
+    "                        [1, 1]\n",
+    "                        ])\n",
+    "\n",
+    "labels_AND = np.array([0, 0, 0, 1])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Beispiel mit OR\n",
+    "labels_OR = np.array([0, 1, 1, 1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hier wird der Iris-Datensatz geladen und vorbereitet (siehe Übung 2)\n",
+    "\n",
+    "# Datensatz laden\n",
+    "names = [\"sepal-length\", \"sepal-width\", \"petal-length\", \"petal-width\", \"class\"]\n",
+    "iris_data = pd.read_csv(\"iris.csv\", names = names)\n",
+    "\n",
+    "# Klassen auswählen (Bei Bedarf ändern)\n",
+    "iris_data = iris_data.loc[lambda x: x['class'] != 'Iris-setosa']\n",
+    "\n",
+    "# Merkmale auswählen (Bei Bedarf ändern)\n",
+    "iris_features = ['petal-length', 'petal-width']\n",
+    "X = iris_data[iris_features]\n",
+    "# Pandas-Datenformat in reine Liste umwandeln\n",
+    "X = X.values\n",
+    "\n",
+    "# Label vorbereiten\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "lb_make = LabelEncoder()\n",
+    "iris_data[\"class_code\"] = lb_make.fit_transform(iris_data[\"class\"])\n",
+    "y = iris_data.class_code\n",
+    "y = y.values\n",
+    "        \n",
+    "# Trainings- und Testdatensplit\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = (\n",
+    "    train_test_split(X, y, test_size=.2, random_state=np.random.seed(42)))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Readme.txt b/Readme.txt
new file mode 100644
index 0000000..ba17bfa
--- /dev/null
+++ b/Readme.txt
@@ -0,0 +1,2 @@
+Name: Paul Lödige
+Matrikel: 15405036
diff --git a/iris.csv b/iris.csv
new file mode 100644
index 0000000..5c4316c
--- /dev/null
+++ b/iris.csv
@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+