k-nearest-neighbors classification

Implement k-nearest-neighbors classification. Given a list of training points (each a list of floats), a parallel list of integer labels, a query point, and an integer k, return the predicted label: the most common label among the k training points closest to the query by Euclidean distance. There are at least k points. Break distance ties by preferring the earlier point (lower index). Break label-vote ties by choosing the smallest label value.