为什么只使用距离的第一列？
我有一个聚类（应用了kmeans并获得集群）。每个集群的半径正在中心和观测值之间计算。
我在这里不明白这一点[：，0]我知道我们正在从第一列中进行所有观察，但是为什么不参加第二列呢？ [：，0]表示什么？
X_distances = euclidean_distances(X, [center])[:, 0]
radius = np.max(X_distances)
I have a clustering (Kmeans was applied and clusters were obtained). The radius for each cluster is being calculated between the center and the observations.
I don't understand this here [:, 0] I know we're taking all observations from the first column but why not take the second column as well? What does [:, 0] represent?
X_distances = euclidean_distances(X, [center])[:, 0]
radius = np.max(X_distances)
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每当您遇到这样的东西时，请稍微解开代码，看看每个作品给您的东西。
在这种情况下， for code> sklealeln的文档。 Metrics.pairwise.euclidean_distances - 我认为您正在使用（请在您的问题中包含此类信息！） - 另外，请告诉您您需要知道的内容：
因此功能
euclidean_distances（x，y）返回所有距离的2D数组在x中的点与y中的点之间。您的X是您的所有数据，您的y只是一个点：群集的质心。因为y只是一个点，所以您所得的距离矩阵只有一个列。像这样：这给出了：
因此，索引
[：，0]正在获取此列。实际上，您可以跳过索引，因为np.max（）不在乎：它只是为您提供整个数组的最大值。因此，您可以将代码减少到：Whenever you come across things like this, unpack the code a bit and look at what each piece is giving you.
In this case, the docs for
sklearn.metrics.pairwise.euclidean_distances— which I assume you are using (please include this sort of information in your questions!) — also tell you what you need to know:So the function
euclidean_distances(X, Y)returns a 2D array of all the distances between the points inXand the points inY. YourXis all your data, and yourYis just one point: the centroid of the cluster. BecauseYis only one point, your resulting distance matrix has only one column. Like this:This gives:
So the index
[:, 0]is getting this column. In fact, you could skip the indexing becausenp.max()doesn't care: it's just going to give you the max of the entire array. So you could reduce your code to: