5 def cluster(k, d, data, i_centers=None):
6 """Find *k* clusters on *d* dimensional *data*."""
8 old_centers = i_centers
10 borders = [(min(p[0][i] for p in data), max(p[0][i] for p in data))
12 old_centers = [[(h - l) * random.random() + l for (l, h) in borders]
14 clusters, centers = next_step(old_centers, data)
15 while delta(old_centers, centers) > 0:
17 clusters, centers = next_step(old_centers, data)
21 def next_step(centers, data):
22 """Compute new clusters and centers."""
23 clusters = [[] for _ in centers]
25 clusters[nearest(centers, point)].append(point)
26 centers = [centroid(c) for c in clusters]
27 return clusters, centers
29 def nearest(centers, point):
30 """Find the nearest cluster *center* for *point*."""
31 d, i = min(((sum((p - c) ** 2 for (p, c) in zip(point[0], center)) ** 0.5 ,
32 index) if center else (float('inf'), len(centers)))
33 for (index, center) in enumerate(centers))
36 def centroid(cluster):
37 """Find the centroid of the *cluster*."""
38 # TODO is this just a mean of coordinates?
39 # TODO should we try different definitions?
40 l = float(len(cluster))
42 d = len(cluster[0][0]) #TODO empty cluster error
45 return [sum(c[0][i] for c in cluster) / l for i in range(d)]
48 """Find the absolute distance between two lists of points."""
49 # TODO rewrite this to a sane form
50 return sum((sum(abs(cc1 - cc2) for (cc1, cc2) in zip (ccc1, ccc2)) if ccc2
51 else 0.) for (ccc1, ccc2) in zip(c1, c2))