# 计算每个聚类的中心点defget_centermost_point(cluster):# 计算整个点集合的质心点centroid=(MultiPoint(cluster).centroid.x,MultiPoint(cluster).centroid.y)# 取点集合中离质心点最近的点为中心点centermost_point=min(cluster,key=lambdapoint:great_circle(point,centroid).m)# 返回中心点returntuple(centermo...
# 计算每个聚类的中心点defget_centermost_point(cluster):# 计算整个点集合的质心点centroid=(MultiPoint(cluster).centroid.x,MultiPoint(cluster).centroid.y)# 取点集合中离质心点最近的点为中心点centermost_point=min(cluster,key=lambdapoint:great_circle(point,centroid).m)# 返回中心点returntuple(centermo...
eps:DBSCAN算法参数,即我们的ϵ-邻域的距离阈值,和样本距离超过ϵ的样本点不在ϵ-邻域内 min_samples:DBSCAN算法参数,即样本点要成为核心对象所需要的ϵ-邻域的样本数阈值'''X,y=getClusterData(flag=flag,ns=3000,nf=5,centers=[[-1,-1],[1,1],[2,2]],cluster_std=[0.4,0.5,0.2])X_train,...
QuoteYes, the function I provided is just to calculate the cluster_labels based on the DBSCAN algorithm. To find the cluster_centers and outliers, you can modify the code to calculate them after assigning the cluster labels. Here's how you can do it: Cluster Centers: After assigning cluster...
print clf.cluster_centers_ 每个样本所属的簇 print clf.labels_ 用来评估簇的个数是否合适,距离越小说明簇分的越好,选取临界点的簇个数 print clf.inertia_ Sum of distances of samples to their closest cluster center. 两个小例子(很久以前弄的,写得比较简略比较乱,有空再改,数据是movielen中的电影标签...
self.cb_points.append(other)defgetNoisePoints(self): self.noise_points= []#噪音点forpointinself.data:ifnotpointinself.core_pointsandnotpointinself.border_points: self.noise_points.append(point)returnself.noise_pointsdefcluster(self):"""开始聚类"""self.cluster_label=0forpointinself.core_points...
centers:聚类中心点个数,即label数 random_state:随机种子,可以固定生成的数据 cluster_std:设置每个类别的方差 4.2 模拟数据 In 8: 代码语言:python 代码运行次数:0 复制 Cloud Studio代码运行 centers=[[1,1],[-1,-1],[1,-1]]# 设置中心X,labels_true=make_blobs(n_samples=2000,# 样本数centers=cent...
from sklearn.cluster import DBSCAN #matplotlib inline X1, y1=datasets.make_circles(n_samples=5000, factor=.6, noise=.05) X2, y2 = datasets.make_blobs(n_samples=1000, n_features=2, centers=[[1.2,1.2]], cluster_std=[[.1]],
kmeans.fit(X)#返回簇标签labels =kmeans.labels_#返回簇中心centers =kmeans.cluster_centers_#计算各簇样本的离差平方和,并保存到列表中forlabelinset(labels): SSE.append(np.sum((X.loc[labels== label,]-centers[label,:])**2))#计算总的簇内离差平方和TSSE.append(np.sum(SSE))#中文和负号的正常...
() # 中心点 #centers = clf.cluster_centers_ # 用来评估簇的个数是否合适,距离约小说明簇分得越好,选取临界点的簇的个数 #score = clf.inertia_ # 每个样本所属的簇 result = {} for text_idx, label_idx in enumerate(y): if label_idx not in result: result[label_idx] = [text_idx] else...