matlab \u7ef4\u8bfa\u56fe\u8d28\u5fc3\u7b97\u6cd5\u3002<\/p>\n
% Compute the Voronoi diagram. % Initialize the centroids and the clusters. % Loop over all edges in the Voronoi diagram. % If this is an infinite edge, ignore it. % Compute the centroid of the region defined by this edge. % Update the closest cluster index for each point in this region. % Update the centroid for this cluster. matlab \u7ef4\u8bfa\u56fe\u8d28\u5fc3\u7b97\u6cd5\u3002 function [centroids, clusters] = voronoi_centroid(points) %VORONOI_CENTROID Vornoi d… <\/p>\n","protected":false},"author":1,"featured_media":809,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,25],"tags":[],"class_list":["post-804","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uav","category-25"],"_links":{"self":[{"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=\/wp\/v2\/posts\/804","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=804"}],"version-history":[{"count":5,"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=\/wp\/v2\/posts\/804\/revisions"}],"predecessor-version":[{"id":998,"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=\/wp\/v2\/posts\/804\/revisions\/998"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=\/wp\/v2\/media\/809"}],"wp:attachment":[{"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=804"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=804"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.autouav.net.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=804"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}function [centroids, clusters] = voronoi_centroid(points)
\n%VORONOI_CENTROID Vornoi diagram centroid-based clustering algorithm
\n%
\n% [CENTROIDS, CLUSTERS] = VORONOI_CENTROID(POINTS) computes the centroids
\n% and cluster assignments of a set of n-dimensional points using the
\n% Voronoi diagram and the centroids of its regions.
\n%
\n% Arguments:
\n% POINTS: A matrix of n-dimensional points, one point per row.
\n%
\n% Returns:
\n% CENTROIDS: A matrix with one centroid per row, corresponding to the
\n% points in the order they appear in CLUSTERS.
\n% CLUSTERS: A vector with one entry per point, indicating the index
\n% into the CENTROIDS matrix of the centroid to which that point
\n% belongs.<\/code><\/p>\n
\n[~, E] = voronoin(points);<\/p>\n
\ncentroids = zeros(size(points));
\nclusters = zeros(length(points), 1);<\/p>\n
\nfor i = 1:length(E)
\n% Indices of the points defining this edge.
\nidx = E{i};<\/p>\n
\nif any(idx == 1), continue; end<\/p>\n
\nx = mean(points(idx, :));<\/p>\n
\nfor j = idx’
\nd = norm(points(j, \ud83d\ude42 – x);
\nif d < norm(points(j, :) - centroids(clusters(j), :))
\nclusters(j) = i;
\nend
\nend<\/code><\/p>\n
\ncentroids(i, \ud83d\ude42 = x;
\nend
\n% Remove any unused centroids.
\ncentroids = centroids(any(clusters == (1:length(E))’, 1), :);
\nend<\/p>\n","protected":false},"excerpt":{"rendered":"