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Mean shift clustering with kd-tree
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Algorithms
distance = (a, b) => Math.hypot(a[0] - b[0], a[1] - b[1])
kernel = d => Math.exp(-0.5 * (d / sigma) ** 2)
function* meanshift({ points, tree, sigma, kn, delta }) {
const pos = Array.from(points, p => Object.create(p));
delta = delta || 1e-3;
kn = kn || 100;
do {
pos.delta = 0;
for (let i = 0; i < pos.length; i++) {
const p = pos[i],
q = p.map(_ => 0);
let weight = 0;
const nn = tree.nearest(p, kn);
for (let j = 0; j < nn.length; j++) {
const ip = nn[j][0],
d = nn[j][1],
w = kernel(d) * (nn[j][0].weight || 1);
weight += w;
for (let k = 0; k < q.length; k++) q[k] += w * ip[k];
}
for (let k = 0; k < q.length; k++) q[k] /= weight;
pos.delta = Math.max(pos.delta, distance(pos[i], q));
pos[i] = q;
}
yield pos;
} while (pos.delta > delta);
}
points = Array.from({ length: N, replay }).map((_, i) => [
i % 2
? Math.random()
: (Math.random() - 0.5) / 4.5 + (0.5 + Math.floor(3 * Math.random())) / 3,
i % 2
? Math.random()
: (Math.random() - 0.5) / 4.5 + (0.5 + Math.floor(3 * Math.random())) / 3
])
kd = require("kd-tree-javascript@1")
palette = d3.schemePastel2.concat(d3.schemePastel1)
d3 = require("d3@7")
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