Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive)

我在编写DBSCAN聚类的程序

import matplotlib.pyplot as plt  # 导入matplotlib的库
import numpy as np  # 导入numpy的包
import pandas as pd
from sklearn.cluster import DBSCAN  # 引入DBSCAN模块
from sklearn import metrics  # 调用评价指标


data = pd.read_csv('Wine_data.csv', header=None, encoding='GB2312')  # 必须要中文解码,设置encoding = ‘GB2312’
wine = data.drop([0], axis=0)
X = np.array(wine)
X = X[:, :11]  # 表示我们取特征空间中的11个维度
print(X)
print(X.shape)  # 打印出X的尺寸大小

print("===DBSCAN聚类===")
dbscan = DBSCAN(eps=0.4, min_samples=9).fit(X)  # 导入DBSCAN模块进行训练
label_pred = dbscan.labels_  # labels为每个数据的簇标签
plt.subplot(2, 2, 3)  # 创建单个子图，单个子图中包含4个区域，相应的区域在左下角。
x0 = X[label_pred == 0]  # 获取聚类标签等于0的话，则赋值给x0
x1 = X[label_pred == 1]  # 获取聚类标签等于1的话，则赋值给x1
x2 = X[label_pred == 2]  # 获取聚类标签等于2的话，则赋值给x2
plt.scatter(x0[:, 0], x0[:, 1], c="red", marker='o', label='label0')  # 画label0的散点图
plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label1')  # 画label1的散点图
plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label2')  # 画label2的散点图
plt.xlabel('DBSCAN')  # 设置X轴的标签为DBSCAN
plt.legend(loc=2)  # 设置图标在左上角bel_pred)
print(label_pred)
score = metrics.silhouette_score(X, label_pred, metric="euclidean")  # 使用轮廓系数
print(score)  # 打印出轮廓系数