Weighted randomness in Java

If you want runtime selection efficiency then taking a little more time on the setup would probably be best. Here is one possible solution. It has more code but guarantees log(n) selection.

WeightedItemSelector Implements selection of a random object from a collection of weighted objects. Selection is guaranteed to run in log(n) time.

public class WeightedItemSelector<T> {
    private final Random rnd = new Random();
    private final TreeMap<Object, Range<T>> ranges = new TreeMap<Object, Range<T>>();
    private int rangeSize; // Lowest integer higher than the top of the highest range.

    public WeightedItemSelector(List<WeightedItem<T>> weightedItems) {
        int bottom = 0; // Increments by size of non zero range added as ranges grows.

        for(WeightedItem<T> wi : weightedItems) {
            int weight = wi.getWeight();
            if(weight > 0) {
                int top = bottom + weight - 1;
                Range<T> r = new Range<T>(bottom, top, wi);
                if(ranges.containsKey(r)) {
                    Range<T> other = ranges.get(r);
                    throw new IllegalArgumentException(String.format("Range %s conflicts with range %s", r, other));
                }
                ranges.put(r, r);
                bottom = top + 1;
            }
        }
        rangeSize = bottom; 
    }

    public WeightedItem<T> select() {
        Integer key = rnd.nextInt(rangeSize);
        Range<T> r = ranges.get(key);
        if(r == null)
            return null;
        return r.weightedItem;
    }
}

Range Implements range selection to leverage TreeMap selection.

class  Range<T> implements Comparable<Object>{
    final int bottom;
    final int top;
    final WeightedItem<T> weightedItem;
    public Range(int bottom, int top, WeightedItem<T> wi) {
        this.bottom = bottom;