Refactoring fixed

aufgaben/p10/evaluation.py

@@ -53,7 +53,7 @@ def evaluate():
     weights = [random.random() for _ in features]
     threshold = 0.5
     perceptron = Perceptron(weights, threshold, numpy.tanh)
-    train(perceptron, training_data, 10000, 0.1)
+    train(perceptron, training_data, 100, 0.1)
     fehlerrate = MulticlassErrorRate()
     for features, correct_class in test_data:
         result = perceptron.classify(features)

aufgaben/p10/evaluation_testdata.py

@@ -37,7 +37,9 @@ def get_evaluation_data(window_size, feature_list: list):
     for bewegungs_name, bewegungs_klasse in bewegungs_list:
         feature_vectors = get_feature_vector(bewegungs_name, feature_list, window_size)
         training, test = label_testdata(feature_vectors, bewegungs_klasse, DATA_LIMIT)
-        training_data += training
-        test_data += test
+        for t in training:
+            training_data.append((list(t[0][0]), t[1]))
+        for t in test:
+            test_data.append((list(t[0][0]), t[1]))
 
     return test_data, training_data