def classify_vectorspace(self, vector):
        # finds the closest cluster centroid
        # returns that cluster's index
        best_distance = best_index = None
        for index in range(len(self._means)):
            mean = self._means[index]
            dist = self._distance(vector, mean)
            if best_distance is None or best_distance < dist:
                best_index, best_distance = index, dist
        return best_index
dataset/ETHPy150Open nltk/nltk/nltk/cluster/kmeans.py/KMeansClusterer.classify_vectorspace/swapped operands of `<`