def weighted_sampler(seq, weights):
    "Return a random-sample function that picks from seq weighted by weights."
    totals = []
    for w in weights:
        totals.append(w + totals[-1] if totals else w)
    return lambda: seq[bisect.bisect(totals, random.uniform(0, totals[-1]))]
def weighted_sampler(seq, weights):
    "Return a random-sample function that picks from seq weighted by weights."
    totals = []
    for w in weights:
        totals.append(w + totals[-1] if totals else w)
    return lambda: seq[bisect.bisect(totals, random.uniform(0, totals[-1]))]
def weighted_sampler ( seq , weights ) : totals = [ ] for w in weights : totals . append ( w + totals [ - 1 ] if totals else w ) return lambda : seq [ bisect . bisect ( totals , random . uniform ( 0 , totals [ - 1 ] ) ) ]
Return a random-sample function that picks from seq weighted by weights.
Return a random - sample function that picks from seq weighted by weights .