代码实现：读入 PCA 降维后的二维鸢尾花数据集，按 DBSCAN 算法描述的过程完成数据集的聚类处理（设 eps=0.5,min_samples=5）（注意：不得直接调用 sklearn 或其他库中的 Kmeans 或类似函数），并输出聚类结果（簇总数，各样本的所属簇编号），调用 matplotlib 的 scatter 函数将聚类后各样本可视化输出（不同簇内的样本用不同的颜色表示，调用 sklearn 库中函数，计算得到外部指标（RI、FMI）和内部指标

好的，以下是按照 DBSCAN 算法描述进行实现的代码：

import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

# 加载数据集
iris = pd.read_csv('iris.csv')
X = iris.iloc[:, :-1]
y = iris.iloc[:, -1]

# PCA降维
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)

# DBSCAN聚类
def dbscan(X, eps=0.5, min_samples=5):
    m, n = X.shape
    visited = np.zeros(m, dtype=bool)
    labels = np.zeros(m, dtype=int)
    cluster_id = 1
    for i in range(m):
        if not visited[i]:
            visited[i] = True
            neighbors = get_neighbors(X, i, eps)
            if len(neighbors) < min_samples:
                labels[i] = -1
            else:
                expand_cluster(X, i, neighbors, visited, labels, cluster_id, eps, min_samples)
                cluster_id += 1
    return labels

def get_neighbors(X, i, eps):
    dists = np.sum((X - X[i]) ** 2, axis=1)
    neighbors = np.where(dists < eps ** 2)[0]
    return neighbors

def expand_cluster(X, i, neighbors, visited, labels, cluster_id, eps, min_samples):
    labels[i] = cluster_id
    for j in neighbors:
        if not visited[j]:
            visited[j] = True
            new_neighbors = get_neighbors(X, j, eps)
            if len(new_neighbors) >= min_samples:
                neighbors = np.union1d(neighbors, new_neighbors)
        if labels[j] == 0:
            labels[j] = cluster_id

labels = dbscan(X_pca, eps=0.5, min_samples=5)

# 簇的总数
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
print("簇的总数：", n_clusters)

# 各样本所归属簇的编号
print("各样本所归属簇的编号：", labels)

# 外部指标
from sklearn.metrics import adjusted_rand_score, fowlkes_mallows_score
ri = adjusted_rand_score(y, labels)
fmi = fowlkes_mallows_score(y, labels)
print("RI：", ri)
print("FMI：", fmi)

# 内部指标
from sklearn.metrics import davies_bouldin_score
dbi = davies_bouldin_score(X_pca, labels)
print("DBI：", dbi)

# 可视化输出
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=labels)
plt.show()

希望能够帮助到您！