clustering.py 文件源码

def thrEstimation(self):
        x = 0.00
        dx = 0.05
        countsList = []
        x_list = []
        while x < 1:

            FlatC = hierarchy.fcluster(self.Tree, x, criterion='distance')
            counter=collections.Counter(FlatC)
            Best = max(counter.iteritems(), key=operator.itemgetter(1))[0]
            countsList.append(counter[Best])        
            x+= dx
            x_list.append(x)
        dy = np.diff(countsList)

        for a, b in zip (x_list, dy):
            if b == max(dy):
                return a