feat: In der Übersicht den Grenzwert ergänzt

aufgaben/p5/apply_pla.py

-import math
 import random
 
 import numpy
 
 from algorithm.pla.perceptron import Perceptron
 from algorithm.pla.perceptron_learning_algorithm import train
-from algorithm.pla.transfer_functions import normalized_tanh
 from aufgaben.p4.binary_classification import binary_classification_feature
 from aufgaben.p6.error_rate import ErrorRate
 
@@ -32,23 +30,20 @@ def apply_pla():
     threshold = 0.5
     perceptron = Perceptron(weights, threshold, numpy.sign)
 
+    # Trainiere das Perzeptron
     train(perceptron, training_data, 300, 0.1)
 
-    result_joggen = []
-    result_kniebeuge = []
+    # Vergleiche alle Ergebnisse mit der erwarteten Klasse
     fehlerrate = ErrorRate()
     for features, correct_class in training_data:
         result = perceptron.classify(features)
         fehlerrate.evaluate(correct_class, result)
+    print("Fehlerrate: " + fehlerrate.error_rate())
 
-        if result == CLASS_KNIEBEUGE:
-            result_kniebeuge.append(features[0])
-        else:
-            result_joggen.append(features[0])
+    # Berechne den Grenzwert, ab wann ein Feature anders eingeordnet wird
+    grenzwert = - perceptron.weights[0] / perceptron.weights[1]
 
-    print(fehlerrate.error_rate())
-
-    return result_joggen, result_kniebeuge
+    return joggen_feature, kniebeuge_feature, grenzwert
 
 
 if __name__ == '__main__':

aufgaben/p5/draw_results.ipynb

@@ -2,15 +2,22 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "start_time": "2023-05-08T12:14:51.676509Z",
-     "end_time": "2023-05-08T12:15:27.987647Z"
+     "start_time": "2023-05-08T12:39:32.036232Z",
+     "end_time": "2023-05-08T12:39:34.563637Z"
     }
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n"
+     ]
+    },
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
@@ -22,7 +29,7 @@
     {
      "data": {
       "text/plain": "<IPython.core.display.HTML object>",
-      "text/html": "<div id='01f9591f-0f28-4bca-9e3b-96d356224e68'></div>"
+      "text/html": "<div id='34b44e73-7218-4dd4-84df-b6a1ee280f7a'></div>"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -45,14 +52,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "outputs": [],
    "source": [],
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "start_time": "2023-05-08T12:15:27.987647Z",
-     "end_time": "2023-05-08T12:15:28.003270Z"
+     "start_time": "2023-05-08T12:39:34.563637Z",
+     "end_time": "2023-05-08T12:39:34.579637Z"
     }
    }
   }