用鸢尾花数据做基于网格CLIQUE算法聚类算法代码

以下是基于网格CLIQUE算法的鸢尾花数据聚类算法代码：

import numpy as np
from sklearn.datasets import load_iris

# 加载鸢尾花数据集
iris = load_iris()
data = iris.data

# 定义距离函数
def distance(x, y):
    return np.sqrt(np.sum((x - y) ** 2))

# 定义网格大小
grid_size = 0.2

# 计算网格数量
n_grid = int(np.ceil((np.max(data, axis=0) - np.min(data, axis=0)) / grid_size))

# 初始化网格
grid = [[] for i in range(n_grid[0] * n_grid[1])]

# 将数据点划分到网格中
for i, x in enumerate(data):
    grid_index = int((x[0] - np.min(data, axis=0)[0]) // grid_size) * n_grid[1] + int((x[1] - np.min(data, axis=0)[1]) // grid_size)
    grid[grid_index].append(i)

# 定义CLIQUE算法
def clique(cluster, min_pts):
    # 定义核心对象集合
    core = set()
    for i in cluster:
        count = 0
        for j in cluster:
            if i != j and distance(data[i], data[j]) <= eps:
                count += 1
        if count >= min_pts:
            core.add(i)
    
    # 定义聚类结果
    clusters = []
    visited = set()
    
    # 遍历核心对象
    for i in core:
        if i not in visited:
            visited.add(i)
            cluster = set()
            cluster.add(i)
            neighbors = set(j for j in core if distance(data[i], data[j]) <= eps)
            while neighbors:
                j = neighbors.pop()
                if j not in visited:
                    visited.add(j)
                    cluster.add(j)
                    new_neighbors = set(j for j in core if distance(data[i], data[j]) <= eps)
                    neighbors.update(new_neighbors - visited)
            clusters.append(cluster)
    
    return clusters

# 定义聚类结果
clusters = []
eps = 0.6
min_pts = 5

# 对每个网格中的数据点进行聚类
for i in range(n_grid[0] * n_grid[1]):
    if grid[i]:
        cluster = clique(grid[i], min_pts)
        for c in cluster:
            clusters.append([grid[i][j] for j in c])

# 输出聚类结果
for i, c in enumerate(clusters):
    print("Cluster %d:" % (i + 1))
    print([iris.target_names[iris.target[j]] for j in c])

该代码实现了基于网格CLIQUE算法的鸢尾花数据聚类，将数据点划分到网格中，并对每个网格中的数据点进行聚类。输出了聚类结果，每个聚类中包含的鸢尾花类型。