P4
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227
P4/AI_41.ipynb
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227
P4/AI_41.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fd9c3cd6",
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"metadata": {},
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"source": [
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"# Perceptron"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e484400f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"class Perceptron():\n",
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" def __init__(self):\n",
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" \"\"\"\n",
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" Initialisiert das Perceptron mit zufälligen Gewichten\n",
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" \n",
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" Die Gewichte werden zwischen -1 und 1 für 3 Eingangssignale initialisiert.\n",
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" \"\"\"\n",
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" # Initialisiere Gewichte zufällig zwischen -1 und 1 für 3 Eingänge\n",
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" self.synaptic_weights = 2 * np.random.random((3, 1)) - 1\n",
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"\n",
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" def sigmoid(self, x):\n",
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" \"\"\"\n",
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" Sigmoid-Aktivierungsfunktion.\n",
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" \n",
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" Args:\n",
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" x: Eingabewert oder Array von Eingabewerten\n",
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" \n",
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" Returns:\n",
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" Sigmoid-Transformation: S(x) = 1/(1 + e^(-x))\n",
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" \"\"\"\n",
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" return 1 / (1 + np.exp(-x))\n",
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"\n",
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" def sigmoid_derivative(self, x):\n",
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" \"\"\"\n",
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" Ableitung der Sigmoid-Funktion\n",
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" \n",
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" Args:\n",
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" x: Sigmoid-Ausgabewert (bereits durch Sigmoid-Funktion transformiert)\n",
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" \n",
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" Returns:\n",
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" Ableitung: Ṡ(x) = S(x) * (1 - S(x))\n",
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" \"\"\"\n",
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" return x * (1 - x)\n",
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"\n",
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" def think(self, inputs):\n",
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" \"\"\"\n",
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" Ein \"Denkschritt\" des Perceptrons für gegebene Eingaben.\n",
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" \n",
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" Args:\n",
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" inputs: Array der Eingangssignale [I1, I2, I3]\n",
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" \n",
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" Returns:\n",
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" Ausgabe des Perceptrons nach O = S(I × W)\n",
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" \"\"\"\n",
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" # Berechne gewichtete Summe und wende Sigmoid-Funktion an\n",
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" return self.sigmoid(np.dot(inputs, self.synaptic_weights))\n",
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"\n",
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" def train(self, inputs, targets, iterations):\n",
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" \"\"\"\n",
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" Trainingsschleife mit Backpropagation und Gewichtsanpassungen.\n",
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" \n",
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" Args:\n",
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" inputs: Trainings-Eingangsdaten (4x3 Matrix)\n",
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" targets: Ziel-Ausgangsdaten (4x1 Matrix)\n",
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" iterations: Anzahl der Trainingsiterationen\n",
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" \"\"\"\n",
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" for i in range(iterations):\n",
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" # Vorwärtspropagation: Berechne Ausgabe\n",
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" output = self.think(inputs)\n",
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" \n",
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" # Berechne Fehler: E_out = T - O\n",
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" error = targets - output\n",
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" \n",
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" # Backpropagation: Berechne Gewichtsanpassungen\n",
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" # ΔW = (Fehler * Sigmoid_Ableitung(Ausgabe)) × Eingaben^T\n",
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" adjustments = np.dot(inputs.T, error * self.sigmoid_derivative(output))\n",
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" \n",
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" # Aktualisiere Gewichte: W = W + ΔW\n",
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" self.synaptic_weights += adjustments"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "d3e6debc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Zufällige synaptische Gewichte vor dem Training:\n",
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"[[-0.24139241 -0.39997757 -0.17940957]]\n",
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"\n",
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"Trainiere das Perceptron...\n",
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"\n",
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"Synaptische Gewichte nach dem Training:\n",
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"[[ 9.15628869 0.34019788 -4.91951613]]\n",
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"\n",
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"Test mit Trainingsdaten:\n",
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"Eingabe: [0 0 1] → Ausgabe: 0.0072 (Ziel: 0)\n",
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"Eingabe: [1 1 1] → Ausgabe: 0.9898 (Ziel: 1)\n",
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"Eingabe: [1 0 0] → Ausgabe: 0.9999 (Ziel: 1)\n",
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"Eingabe: [0 1 1] → Ausgabe: 0.0102 (Ziel: 0)\n",
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"\n",
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"Test mit neuer Eingabe [1, 1, 0]:\n",
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"Eingabe: [1 1 0] → Vorhersage: 0.9999\n",
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"\n",
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"==================================================\n",
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"Interaktive Eingabe:\n",
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"\n",
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"Geben Sie Werte für I1, I2, I3 ein (getrennt durch Kommas):\n",
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" Perceptrons Überzeugung für [1. 0. 1.]: 0.9858\n",
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"\n",
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"Geben Sie Werte für I1, I2, I3 ein (getrennt durch Kommas):\n",
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" Perceptrons Überzeugung für [1. 1. 0.]: 0.9999\n",
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"\n",
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"Geben Sie Werte für I1, I2, I3 ein (getrennt durch Kommas):\n",
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"Programm beendet.\n"
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]
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}
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],
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"source": [
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"# Erstelle Perceptron-Instanz\n",
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"p = Perceptron()\n",
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"\n",
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"# Trainingsdaten\n",
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"training_inputs = np.array([[0, 0, 1],\n",
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" [1, 1, 1], \n",
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" [1, 0, 0],\n",
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" [0, 1, 1]])\n",
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"\n",
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"training_targets = np.array([[0, 1, 1, 0]]).T\n",
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"\n",
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"print(\"Zufällige synaptische Gewichte vor dem Training:\")\n",
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"print(p.synaptic_weights.T)\n",
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"\n",
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"# Trainiere das Perceptron\n",
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"print(\"\\nTrainiere das Perceptron...\")\n",
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"p.train(training_inputs, training_targets, 10000)\n",
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"\n",
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"print(\"\\nSynaptische Gewichte nach dem Training:\")\n",
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"print(p.synaptic_weights.T)\n",
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"\n",
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"# Teste das Perceptron mit Trainingsdaten\n",
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"print(\"\\nTest mit Trainingsdaten:\")\n",
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"for i, inp in enumerate(training_inputs):\n",
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" output = p.think(inp.reshape(1, -1))\n",
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" print(f\"Eingabe: {inp} → Ausgabe: {output[0][0]:.4f} (Ziel: {training_targets[i][0]})\")\n",
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"\n",
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"# Teste mit neuen Daten [1, 1, 0]\n",
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"print(\"\\nTest mit neuer Eingabe [1, 1, 0]:\")\n",
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"new_input = np.array([1, 1, 0])\n",
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"prediction = p.think(new_input.reshape(1, -1))\n",
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"print(f\"Eingabe: {new_input} → Vorhersage: {prediction[0][0]:.4f}\")\n",
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"\n",
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"# Interaktiver Teil für Jupyter Notebook\n",
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"print(\"\\n\" + \"=\"*50)\n",
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"print(\"Interaktive Eingabe:\")\n",
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"\n",
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"while True:\n",
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" try:\n",
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" print(\"\\nGeben Sie Werte für I1, I2, I3 ein (getrennt durch Kommas):\")\n",
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" user_input = input(\"I1, I2, I3: \")\n",
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" \n",
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" # Prüfe auf Beenden-Kommandos\n",
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" if user_input.lower() in ['quit', 'exit', 'q', 'beenden']:\n",
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" print(\"Programm beendet.\")\n",
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" break\n",
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" \n",
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" # Parse Benutzereingabe\n",
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" values = [float(x.strip()) for x in user_input.split(',')]\n",
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" if len(values) != 3:\n",
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" print(\" Fehler: Bitte geben Sie genau 3 Werte getrennt durch Kommas ein.\")\n",
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" print(\" Beispiel: 1,0,1 oder 0.5,1,0\")\n",
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" continue\n",
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" \n",
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" # Teste das Perceptron\n",
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" user_array = np.array(values)\n",
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" result = p.think(user_array.reshape(1, -1))\n",
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" \n",
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" # Formatierte Ausgabe\n",
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" print(f\" Perceptrons Überzeugung für {user_array}: {result[0][0]:.4f}\")\n",
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" \n",
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" except (ValueError, IndexError):\n",
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" print(\" Ungültige Eingabe! Verwenden Sie das Format: 1,0,1\")\n",
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" print(\" Oder geben Sie 'quit' zum Beenden ein.\")\n",
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" except KeyboardInterrupt:\n",
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" print(\"\\n\\n Programm durch Keyboard-Interrupt beendet (Strg+C)\")\n",
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" break\n",
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" except EOFError:\n",
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" print(\"\\n\\n Eingabe beendet (Strg+D)\")\n",
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" break"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".env",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.19"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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