8115a167b0f280a62aa0d5560709ab9790d96e15,deslib/util/aggregation.py,,weighted_majority_voting_rule,#Any#Any#Any#,125

Before Change

    for idx in range(n_samples):

        for label in labels_set:
            w_votes[idx, np.where(labels_set == label)] = sum(
                weights[idx, votes[idx] == label])

    predicted_label = labels_set[np.argmax(w_votes, axis=1)]

After Change

    n_samples = votes.shape[0]
    w_votes = np.zeros((len(labels_set), n_samples))
    ma_weights = weights.view(np.ma.MaskedArray)
    for ind, label in enumerate(labels_set):
        ma_weights.mask = votes != label
        w_votes[ind, :] = ma_weights.sum(axis=1)

    predicted_label = labels_set[np.argmax(w_votes, axis=0)]
    return predicted_label

Instances