diff --git a/algorithm/decision_tree/tree.py b/algorithm/decision_tree/tree.py
index 75bad482485e2ba99ea479c3b41f35b27eb83ea3..c04dc1f0c75e27faf72f6ec1b8ed57ae84371c56 100644
--- a/algorithm/decision_tree/tree.py
+++ b/algorithm/decision_tree/tree.py
@@ -55,6 +55,6 @@ class Tree:
def str_content(self):
if self.children:
- return f'θ: {self.threshold}'
+ return f'Attr: {self.best_attribute}, θ: {self.threshold}'
else:
return f'Classification: {self.threshold}'
diff --git a/aufgaben/p10/evaluation.py b/aufgaben/p10/evaluation.py
index 9748118267d4281188711ec2ca6ab532a2e32c52..731165aeb45ad95de6b17a20aa01aa95572e5be6 100644
--- a/aufgaben/p10/evaluation.py
+++ b/aufgaben/p10/evaluation.py
@@ -9,7 +9,10 @@ from algorithm.k_nearest_neighbors.k_nearest_neighbors_algorithm import KNearest
from aufgaben.p4.testdata import get_evaluation_data
from aufgaben.p6.error_rate import ErrorRate
from aufgaben.p6.multiclass_error_rate import Multiclass_ErrorRate
-
+from features.standard_deviation import standard_deviation
+from features.arithmetic_mean import arithmetic_mean
+from features.median import median
+from features.extremwerte import maximum
def evaluate_algorithm(training_data, test_data, algorithm, evaluator, args=None):
if args is None:
@@ -30,7 +33,7 @@ def evaluate_algorithm(training_data, test_data, algorithm, evaluator, args=None
def evaluate():
- test_data, training_data = get_evaluation_data(200, True)
+ test_data, training_data = get_evaluation_data(200, [standard_deviation, arithmetic_mean, median, maximum])
classes = list(set(test[1] for test in test_data))
# DecisionTree
@@ -44,7 +47,7 @@ def evaluate():
# PLA
print("\nPLA")
- weights = [random.random(), random.random()]
+ weights = [random.random(), random.random(), random.random(), random.random()]
threshold = 0.5
perceptron = Perceptron(weights, threshold, numpy.tanh)
train(perceptron, training_data, 10000, 0.1)
diff --git a/aufgaben/p4/testdata.py b/aufgaben/p4/testdata.py
index 543d7a02a9fba275c74065039d04a7f9006b4630..cb4ef8eef41d639361c8c1e059d4b1027dd6988c 100644
--- a/aufgaben/p4/testdata.py
+++ b/aufgaben/p4/testdata.py
@@ -79,45 +79,42 @@ def get_labeled_testdata():
return test_data, training_data
-def get_evaluation_data(window_size, second_feature=False):
- # Hole die aus den Sensordaten berechneten Merkmale
- joggen_feature, kniebeuge_feature, jj_feature = classification_evaluation(window_size, standard_deviation)
-
- # Wir nehmen nur DATA_LIMIT an Daten (sonst ist K-Nearest-Neighbors zu langsam)
- joggen_feature = joggen_feature[: min(DATA_LIMIT_PER_TYPE, len(joggen_feature))]
- kniebeuge_feature = kniebeuge_feature[: min(DATA_LIMIT_PER_TYPE, len(kniebeuge_feature))]
- jj_feature = jj_feature[: min(DATA_LIMIT_PER_TYPE, len(jj_feature))]
-
- # Wandel Liste an Merkmalen in einzelne Merkmalsvektoren um
- if second_feature:
- joggen_feature2, kniebeuge_feature2, jj_feature2 = classification_evaluation(window_size, arithmetic_mean)
- joggen_feature2 = joggen_feature2[: min(DATA_LIMIT_PER_TYPE, len(joggen_feature2))]
- kniebeuge_feature2 = kniebeuge_feature2[: min(DATA_LIMIT_PER_TYPE, len(kniebeuge_feature2))]
- jj_feature2 = jj_feature2[: min(DATA_LIMIT_PER_TYPE, len(jj_feature2))]
- joggen_vector = []
+def get_evaluation_data(window_size, feature_list: []):
+ joggen_vector = None
+ kniebeugen_vector = None
+ jj_vector = None
+
+ for feature in feature_list:
+ # Hole die aus den Sensordaten berechneten Merkmale
+ joggen_feature, kniebeuge_feature, jj_feature = classification_evaluation(window_size, feature)
+
+ if not joggen_vector:
+ joggen_vector = [[] for _ in joggen_feature][: min(DATA_LIMIT_PER_TYPE, len(joggen_feature))]
+ kniebeugen_vector = [[] for _ in kniebeuge_feature][: min(DATA_LIMIT_PER_TYPE, len(kniebeuge_feature))]
+ jj_vector = [[] for _ in jj_feature][: min(DATA_LIMIT_PER_TYPE, len(jj_feature))]
+
+ # Wir nehmen nur DATA_LIMIT an Daten (sonst ist K-Nearest-Neighbors zu langsam)
+ joggen_feature = joggen_feature[: min(DATA_LIMIT_PER_TYPE, len(joggen_feature))]
+ kniebeuge_feature = kniebeuge_feature[: min(DATA_LIMIT_PER_TYPE, len(kniebeuge_feature))]
+ jj_feature = jj_feature[: min(DATA_LIMIT_PER_TYPE, len(jj_feature))]
for i in range(len(joggen_feature)):
- joggen_vector.append([joggen_feature[i], joggen_feature2[i]])
- kniebeugen_vector = []
+ joggen_vector[i].append(joggen_feature[i])
for i in range(len(kniebeuge_feature)):
- kniebeugen_vector.append([kniebeuge_feature[i], kniebeuge_feature2[i]])
- jj_vector = []
+ kniebeugen_vector[i].append(kniebeuge_feature[i])
for i in range(len(jj_feature)):
- jj_vector.append([jj_feature[i], jj_feature2[i]])
- else:
- joggen_vector = ([element] for element in joggen_feature)
- kniebeugen_vector = ([element] for element in kniebeuge_feature)
- jj_vector = ([element] for element in jj_feature)
+ jj_vector[i].append(jj_feature[i])
+
# Weise den Trainingsdaten eine Klasse zu
# 0 = Kniebeuge, 1 = Joggen
- training_data_joggen = list(zip(joggen_vector, [CLASS_JOGGEN] * len(joggen_feature)))
- training_data_kniebeuge = list(zip(kniebeugen_vector, [CLASS_KNIEBEUGE] * len(kniebeuge_feature)))
- training_data_jj = list(zip(jj_vector, [CLASS_JUMPINGJACK] * len(jj_feature)))
+ training_data_joggen = list(zip(joggen_vector, [CLASS_JOGGEN] * len(joggen_vector)))
+ training_data_kniebeuge = list(zip(kniebeugen_vector, [CLASS_KNIEBEUGE] * len(kniebeugen_vector)))
+ training_data_jj = list(zip(jj_vector, [CLASS_JUMPINGJACK] * len(jj_vector)))
# Wir nehmen 90 % der Testdaten zum Trainieren und 10 % zum Testen
- delimiter_joggen = floor(len(joggen_feature) * TRAINING_DATA_PERCENTAGE)
- delimiter_kniebeuge = floor(len(kniebeuge_feature) * TRAINING_DATA_PERCENTAGE)
- delimiter_jj = floor(len(jj_feature) * TRAINING_DATA_PERCENTAGE)
+ delimiter_joggen = floor(len(joggen_vector) * TRAINING_DATA_PERCENTAGE)
+ delimiter_kniebeuge = floor(len(kniebeugen_vector) * TRAINING_DATA_PERCENTAGE)
+ delimiter_jj = floor(len(jj_vector) * TRAINING_DATA_PERCENTAGE)
training_data = training_data_joggen[:delimiter_joggen] + training_data_kniebeuge[:delimiter_kniebeuge] + training_data_jj[:delimiter_jj]
test_data = training_data_joggen[delimiter_joggen:] + training_data_kniebeuge[delimiter_kniebeuge:] + training_data_jj[delimiter_jj:]