59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
from utility import *
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class PointCluster:
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def __init__(self):
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self.points = []
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self.center = (0, 0)
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self.max_distance = None
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def update(self):
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if len(self.points) == 0: return
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self.center = (sum([p[0] for p in self.points]) / len(self.points),
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sum([p[1] for p in self.points]) / len(self.points))
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self.max_distance = sqrt(max(
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[squared_distance(self.center, p) for p in self.points]))
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def add(self, pt):
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self.points.append(pt)
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self.update()
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def pop(self):
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p = self.points.pop(-1)
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self.update()
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return p
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def __repr__(self):
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c = f"({self.center[0]:.1f},{self.center[1]:.1f})"
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return f"<PointCluster center={c}, {len(self.points)} points>"
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def cluster_set(points, maxradius):
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"""returns a list of PointCluster objects. Points are fit within circles of maxradius"""
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clusters = []
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for pt in points:
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if len(clusters) == 0:
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#print("first cluster")
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clusters.append(PointCluster())
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clusters[-1].add(pt)
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continue
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# add point to its nearest cluster
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scored_clusters = [(c, squared_distance(pt, c.center)) for c in clusters]
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scored_clusters.sort(key=lambda i: i[1])
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winner = scored_clusters[0][0]
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winner.add(pt)
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# if maxradius constraint was violated, pop the newest point & add new cluster
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if winner.max_distance > maxradius:
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#print(f"{winner.max_distance} > {maxradius}; new cluster")
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winner.pop()
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clusters.append(PointCluster())
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clusters[-1].add(pt)
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# refine step - accept centers as fixed, put points in closest center
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new_clusters = {c.center: PointCluster() for c in clusters}
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closest = lambda pt: sorted(new_clusters.keys(), key= lambda i: squared_distance(pt, i))[0]
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for point in points:
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new_clusters[closest(point)].add(point)
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#print(clusters)
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#print(new_clusters.values())
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return new_clusters.values()
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