Created
June 7, 2016 18:56
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| def algorithm(points, k, cutoff): | |
| initial = random.sample(points, k) | |
| clusters = [Cluster([p]) for p in initial] | |
| loop_counter = 0 | |
| while True: | |
| lists = [[] for c in clusters] | |
| cluster_count = len(clusters) | |
| loop_counter += 1 | |
| for p in points: | |
| smallest_distance = get_distance(p, clusters[0].centroid) | |
| cluster_index = 0 | |
| for i in range(cluster_count - 1): | |
| distance = get_distance(p, clusters[i + 1].centroid) | |
| if distance < smallest_distance: | |
| smallest_distance = distance | |
| cluster_index = i + 1 | |
| lists[cluster_index].append(p) | |
| biggest_shift = 0.0 | |
| for i in range(cluster_count): | |
| shift = clusters[i].update(lists[i]) | |
| biggest_shift = max(biggest_shift, shift) | |
| if biggest_shift < cutoff: | |
| print "Converged after %s iterations" % loop_counter | |
| break | |
| return clusters |
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