def _assign_data_to_clusters(data, centroids):
cluster_count = len(centroids)
clusters = [[] for i in range(cluster_count)]
for d in data:
distances = np.zeros(cluster_count)
for i in range(0, cluster_count):
distances[i] = MathUtil.euclidean_distance(d, centroids[i])
i_min = np.argmin(distances)
clusters[i_min].append(d)
return clusters
def update_distance(self, vector):
self.distance = util.euclidean_distance(self.weights, vector)
