gistfile1.txt

def knn(tweet_vector, train_set, k) -> list:
    """
    Find k nearest neighbors of a given tweet

    :param tweet_vector: vector of tweet whose neighbors we seek
    :param train_set: training set
    :param k: desired number of nearest neighbors

    :return: list of indices in main tweet list of k nearest neighbors, and distances of those
            neighbors to given tweet
    """
    knn_indices_and_distances = []
    distance = 0

    for x in range(len(train_set)):
        distance = get_distance(tweet_vector[0], train_set[x])
        knn_indices_and_distances.append([train_set[x][-2], distance])

    #  Sort by distance (smallest to largest)
    knn_indices_and_distances.sort(key=lambda x: x[1])

    return knn_indices_and_distances[:k]