kmeans.py 文件源码

def calculateCentroid(self):
        if len(self.points) > 0 :
            # Finds a virtual center point for a group of n-dimensional points
            numPoints = len(self.points)
            # Get a list of all coordinates in this cluster
            coords = [p.coords for p in self.points]
            print('cluster has: '+ str(numPoints) + ' point')
            # Reformat that so all x's are together, all y'z etc.
            unzipped = zip(*coords)
            # Calculate the mean for each dimension
            centroid_coords = [math.fsum(dList)/numPoints for dList in unzipped]
            return Point(centroid_coords,'Centroid')
        else:
            return self.centroid