aima/learning.py
def weighted_replicate(seq, weights, n):
    """Return n selections from seq, with the count of each element of
    seq proportional to the corresponding weight (filling in fractions
    randomly).
    >>> weighted_replicate('ABC', [1,2,1], 4)
    ['A', 'B', 'B', 'C']"""
    assert len(seq) == len(weights)
    weights = normalize(weights)
    wholes = [int(w*n) for w in weights]
    fractions = [(w*n) % 1 for w in weights]
    return (flatten([x] * nx for x, nx in zip(seq, wholes))
            + weighted_sample_with_replacement(seq, fractions, n - sum(wholes)))
def weighted_replicate(seq, weights, n):
    """Return n selections from seq, with the count of each element of
    seq proportional to the corresponding weight (filling in fractions
    randomly).
    >>> weighted_replicate('ABC', [1,2,1], 4)
    ['A', 'B', 'B', 'C']"""
    assert len(seq) == len(weights)
    weights = normalize(weights)
    wholes = [int(w*n) for w in weights]
    fractions = [(w*n) % 1 for w in weights]
    return (flatten([x] * nx for x, nx in zip(seq, wholes))
            + weighted_sample_with_replacement(seq, fractions, n - sum(wholes)))
def weighted_replicate ( seq , weights , n ) : assert len ( seq ) == len ( weights ) weights = normalize ( weights ) wholes = [ int ( w * n ) for w in weights ] fractions = [ ( w * n ) % 1 for w in weights ] return ( flatten ( [ x ] * nx for x , nx in zip ( seq , wholes ) ) + weighted_sample_with_replacement ( seq , fractions , n - sum ( wholes ) ) )
Return n selections from seq, with the count of each element of
    seq proportional to the corresponding weight (filling in fractions
    randomly).
    >>> weighted_replicate('ABC', [1,2,1], 4)
    ['A', 'B', 'B', 'C']
Return n selections from seq with the count of each element of seq proportional to the corresponding weight ( filling in fractions randomly ) . >>> weighted_replicate ( ABC [ 1 2 1 ] 4 ) [ A B B C ]