1,0.697,0.46,2,0.774,0.376,3,0.634,0.264,4,0.608,0.318,5,0.556,0.215,
6,0.403,0.237,7,0.481,0.149,8,0.437,0.211,9,0.666,0.091,10,0.243,0.267,
11,0.245,0.057,12,0.343,0.099,13,0.639,0.161,14,0.657,0.198,15,0.36,0.37,
16,0.593,0.042,17,0.719,0.103,18,0.359,0.188,19,0.339,0.241,20,0.282,0.257,
21,0.748,0.232,22,0.714,0.346,23,0.483,0.312,24,0.478,0.437,25,0.525,0.369,
26,0.751,0.489,27,0.532,0.472,28,0.473,0.376,29,0.725,0.445,30,0.446,0.459"""#数据处理 dataset是30个样本（密度，含糖量）的列表a = data.split(',')
dataset = [(float(a[i]), float(a[i+1])) for i in range(1, len(a)-1, 3)]#计算欧几里得距离,a,b分别为两个元组def dist(a, b):
return math.sqrt(math.pow(a[0]-b[0], 2)+math.pow(a[1]-b[1], 2))#dist_mindef dist_min(Ci, Cj):
return min(dist(i, j) for i in Ci for j in Cj)#dist_maxdef dist_max(Ci, Cj):
return max(dist(i, j) for i in Ci for j in Cj)#dist_avgdef dist_avg(Ci, Cj):
return sum(dist(i, j) for i in Ci for j in Cj)/(len(Ci)*len(Cj))#找到距离最小的下标def find_Min(M):
min = 1000
x = 0 y = 0
for i in range(len(M)): for j in range(len(M[i])): if i != j and M[i][j] < min:
min = M[i][j]x = i y = j return (x, y, min)#算法模型：def AGNES(dataset, dist, k):
#初始化C和M
C = []M = [] for i in dataset:
Ci = []
Ci.append(i)
C.append(Ci) for i in C:
Mi = [] for j in C:
Mi.append(dist(i, j))
M.append(Mi)
q = len(dataset) #合并更新
while q > k:
x, y, min = find_Min(M)
C[x].extend(C[y])
C.remove(C[y])
M = [] for i in C:
Mi = [] for j in C:
Mi.append(dist(i, j))
M.append(Mi)
q -= 1
return C#画图def draw(C):
colValue = ['r', 'y', 'g', 'b', 'c', 'k', 'm'] for i in range(len(C)):
coo_X = [] #x坐标列表
coo_Y = [] #y坐标列表
for j in range(len(C[i])):
coo_X.append(C[i][j][0])
coo_Y.append(C[i][j][1])
pl.scatter(coo_X, coo_Y, marker='x', color=colValue[i%len(colValue)], label=i)
pl.legend(loc='upper right')
pl.show()
C = AGNES(dataset, dist_avg, 3)
draw(C)
class node: left=None right=None def __init__(self, parent=None): self.parent=parent 赋值的时候对应就好了。如root=node()，a=node(root)，root.left=a，就有点像C语言里的指针了。
