K-means clustering

We use Complex numbers to represent points in the plane. We feed the algorithm with three artificially made clouds of points so we can easily see if the output makes sense.

sub postfix:«-means++»(Int $K) {
    return sub (@data) {
        my @means = @data.pick;
        until @means == $K {
            my @cumulD2 = [\+] @data.map: -> $x {
                min @means.map: { abs($x - $_)**2 }
            }
            my $rand = rand * @cumulD2[*-1];
            @means.push: @data[
                (^@data).first: { @cumulD2[$_] > $rand }
            ];
        }
        sub cluster { @data.classify: -> $x { @means.min: { abs($_ - $x) } } }
        loop (
            my %cluster;
            $*TOLERANCE < [+] (@means Z- keys (%cluster = cluster))».abs X** 2;
            @means = %cluster.values.map( { .elems R/ [+] @$_ } )
        ) { ; }
        return @means;
    }
}
 
my @centers = 0, 5, 3 + 2i;
my @data = flat @centers.map: { ($_ + .5 - rand + (.5 - rand) * i) xx 100 }
@data.=pick(*);
.say for 3-means++(@data);

Output:

5.04622376429502+0.0145269848483031i
0.0185674577571743+0.0298199687431731i
2.954898072093+2.14922298688815i