def kdtree_contribute_Matrix(S, K):
N = len(S)
A = np.zeros((N, N))
leaf_size = 4
root = kdtree.kdtree_construction(S, leaf_size=leaf_size)
for i in range(N):
query = S[i]
result_set = KNNResultSet(capacity=K)
kdtree.kdtree_knn_search(root, S, result_set, query)
index = result_set.knn_output_index()
for j in index:
A[i][j] = 1 #
A[j][i] = A[i][j]
if i == j:
A[i][j] = 0
return A
def fit(self, data):
#TODO
#step1 随机选取 K个数据点 作为聚类的中心
self.centers_ = data[random.sample(range(data.shape[0]),
self.k_)] #random.sample(list,num)
old_centers = np.copy(self.centers_) #存储old_centers
#step2 E-Step(expectation)：N个点、K个中心，求N个点到K个中心的nearest-neighbor
#kd-tree config
leaf_size = 1
k = 1 # 结果每个点选取属于自己的类中心
for _ in range(self.max_iter_):
labels = [[] for i in range(self.k_)] #用于分类所有数据点
root = kdtree.kdtree_construction(
self.centers_, leaf_size=leaf_size) #对中心点进行构建kd-tree
for i in range(data.shape[0]): #对每一个点在4个中心点中进行 1-NN的搜索
result_set = KNNResultSet(capacity=k)
query = data[i]
kdtree.kdtree_knn_search(root, self.centers_, result_set,
query) #返回对应中心点的索引
# labels[result_set.output_index].append(data[i])
#print(result_set)
output_index = result_set.knn_output_index()[0] #获取最邻近点的索引
labels[output_index].append(data[i]) #将点放入类中
#step3 M-Step(maximization)：更新中心点的位置，把属于同一个类的数据点求一个均值，作为这个类的中心值
for i in range(self.k_): #求K类里，每个类的的中心点
points = np.array(labels[i])
self.centers_[i] = points.mean(axis=0) #取点的均值，作为新的聚类中心
# print(points)
# print(self.centers_[i])
if np.sum(
np.abs(self.centers_ - old_centers)
) < self.tolerance_ * self.k_: # 如果前后聚类中心的距离相差小于self.tolerance_ * self.k_ 输出
break
old_centers = np.copy(self.centers_) #保存旧中心点
self.fitted = True
Point_Show(self.centers_)