A ludicrous spaghetti implementation of n-dimensional weighted k-means

kmeans-spaghetti.py

"""A ludicrous spaghetti implementation of n-dimensional weighted k-means that can do no prediction. This line is long since, after all, it's spaghetti!"""

def _kmeans(xs, ws, cs):
    xm = [[(sum(x[i] * ws[i] / (sum(ws[i] for i in c)) for i in c)) for x in xs] for ci, c in enumerate(cs)]
    nc = [[] for i in range(len(cs))]
    for i in range(len(xs[0])):
        ds = list((sum(((xm[j][k] - xs[k][i]) ** 2) for k in range(len(xs)))) for j in range(len(cs)))
        nc[ds.index(min(ds))].append(i)
    return cs, nc
    
def kmeans(m, xs, ws=None):
    ws = ws if ws else [1] * len(xs[0])
    oc = [None] * m
    nc = list(list(range(len(xs[0]))[i::m]) for i in range(m))
    while any(nc[i] != oc[i] for i in range(m)): oc, nc = _kmeans(xs, ws, nc)
    return nc
    
if __name__ == '__main__':
    data = [
            [10, 50, 20, 60, 90, 50, 60, 70, 30, 40],
            [40, 100, 80, 2, 70, 10, 40, 70, 10, 90]
        ]
    w = [0.25, 0.6, 0.75, 0.6, 1.2, 0.5, 0.4, 0.5, 0.35, 0.6]
    print(kmeans(3, data, w))