K-means算法是一种硬聚类算法，根据数据到聚类中心的某种距离来作为判别该数据所属类别。K-means算法以距离作为相似度测度。(kmeans uses the k-means++ algorithm for centroid initialization and squared Euclidean distance by default. It is good practice to search for lower, local minima by setting the 'Replicates' name-value pair argument.

idx is a vector of predicted cluster indices corrresponding to the observations in X. C is a 3-by-2 matrix containing the final centroid locations.

Use kmeans to compute the distance from each centroid to points on a grid. To do this, pass the centroids (C) and points on a grid to kmeans, and implement one iteration of the algorithm.)