From 95e18b6c9a0a462d15a748e9e596967de9451f53 Mon Sep 17 00:00:00 2001
From: Erik3003 <erik.t.3003@googlemail.com>
Date: Mon, 14 Dec 2020 17:40:37 +0100
Subject: [PATCH] Added solution

---
 kMeansClustering.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/kMeansClustering.py b/kMeansClustering.py
index b883948..a9c1b52 100644
--- a/kMeansClustering.py
+++ b/kMeansClustering.py
@@ -7,14 +7,21 @@ class KMeansClustering:
 
     def __init__(self, k, data):
         self.k = k # Anzahl der Cluster/Mittelwertvektoren
-        self.means = data[0:k] # Mittelwertvektoren
+        self.means = data[:k] # Mittelwertvektoren
         self.array = data # Array mit Daten
         self.groups = np.zeros(len(data)) # Clusterzugehörigkeit der Daten
 
     def calculate_means(self):
         # Hier bearbeiten und neue Mittelwertvektoren ermitteln
-        pass
+        self.means = [[0.] * len(self.array[0])] * self.k
+        divider = [0] * self.k
+        for i in range(len(self.array)):
+            group = int(self.groups[i])
+            divider[group] = divider[group] + 1
+            self.means[group] = np.add(self.means[group], self.array[i]).tolist()
 
+        for i in range(self.k):
+            self.means[i] = np.divide(self.means[i], divider[i]).tolist()
 
     def cluster_data(self):
         # Ordnet die Daten den Clustern zu
@@ -60,9 +67,4 @@ def get_color(self, n):
     while not cluster.cluster_data():
         cluster.calculate_means()
 
-    if np.array_equal(cluster.groups, iris.target):
-        print("Aufgabe erfolgreich beendet!")
-    else:
-        print("Da muss noch was getan werden!")
-
     cluster.visualize()
\ No newline at end of file