In class we've briefly discussed the problem of randomly distributing points in the xy-plane. One can use the Random.float function to compute a random x-coordinate and a random y-coordinate. But the result will often have too many points that are clumped together with other areas being completely free of points.

The best candidate algorithm is an iterative algorithm that maintains a list of random points [p₁, ..., p_k]. In each cycle through the algorithm, a set of candidate points [c₁, ..., c_j] are generated, exactly one of these candidates will be selected join the list of random points. In particular, for each candidate c_i, the nearest neighbor in [p₁, ..., p_k] is found. The candidate with the largest distance to it's nearest neighbor is the best, it joins the list of random points and the other candidates are discarded.

Implement the best candidate algorithm as described above. Your implementation should render the set of generated as small circles on the xy-plane provided by the stddraw library.