0e17f47e9a4920e276bb61b60d6de16264bc6bcf,pythonds/util/aggregation.py,,majority_voting,#Any#Any#,9

Before Change

    
    votes = [clf.predict(query)[0] for clf in ensemble]
    counter = collections.Counter(votes)
    predicted_label = counter.most_common()[0][0]

    return predicted_label

After Change

    predicted_label : The label of the query sample, predicted by the majority voting rule
    
    // Check if a single sample was passed down to the function. In this case the sample must be converted to a 2D array.
    if query.ndim == 1:
        query = np.atleast_2d(query)

    n_samples = query.shape[0]
    votes = np.zeros((n_samples, len(classifier_ensemble)))
    for clf_index, clf in enumerate(classifier_ensemble):
        votes[:, clf_index] = clf.predict(query)

Instances