def n_feature_hash(feature, dims, seeds):
    """N-hot-encoded feature hashing.

    Args:
        feature (str): Target feature represented as string.
        dims (list of int): Number of dimensions for each hash value.
        seeds (list of float): Seed of each hash function (mmh3).

    Returns:
        numpy 1d array: n-hot-encoded feature vector for `s`.

    """
    vec = np.zeros(sum(dims))
    offset = 0

    for seed, dim in zip(seeds, dims):
        vec[offset:(offset + dim)] = feature_hash(feature, dim, seed)
        offset += dim

    return vec
def n_feature_hash ( feature , dims , seeds ) : vec = np . zeros ( sum ( dims ) ) offset = 0 for seed , dim in zip ( seeds , dims ) : vec [ offset : ( offset + dim ) ] = feature_hash ( feature , dim , seed ) offset += dim return vec
N-hot-encoded feature hashing.

    Args:
        feature (str): Target feature represented as string.
        dims (list of int): Number of dimensions for each hash value.
        seeds (list of float): Seed of each hash function (mmh3).

    Returns:
        numpy 1d array: n-hot-encoded feature vector for `s`.