feature(a2): add kmeans exercises

ML/aufgaben/a2/kmeans_iris.py

@@ -0,0 +1,28 @@
+from sklearn import datasets
+from sklearn.cluster import KMeans
+from sklearn import metrics
+
+iris = datasets.load_iris()
+
+# print 150 samples
+print(iris.target)
+
+# clusters=3, centroiden zufällig wählen, n_init=50
+kmeans = KMeans(n_clusters=3, init='random', n_init=50)
+# fit auf daten
+kmeans.fit(iris.data)
+
+# print alle daten
+#print(list(zip(iris.target, kmeans.labels_)))
+
+# gegenüberstellung
+print("gold standard vs. prediction")
+for target_label, predicted_label in zip(iris.target, kmeans.labels_):
+    print(f'{target_label} vs. {predicted_label}')
+
+print(metrics.homogeneity_score(iris.target, kmeans.labels_))
+print(metrics.completeness_score(iris.target, kmeans.labels_))
+print(metrics.adjusted_rand_score(iris.target, kmeans.labels_))
+print(metrics.silhouette_score(iris.data, kmeans.labels_))
+
+# erkenntnis, der Algo ist nicht perfekt für diese Art von Daten!!