source: branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/HeuristicLab.Algorithms.Bandits/ActionInfos/ExtremeHunterActionInfo.cs @ 13234

using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using HeuristicLab.Common;

namespace HeuristicLab.Algorithms.Bandits.BanditPolicies {
  // stores information that is relevant for most of the policies
  public class ExtremeHunterActionInfo : IBanditPolicyActionInfo {

    // hills estimator using a fraction r of the largest sample values
    private class OnlineHillEstimator {
      private MinHeap<double> minHeap;
      private MaxHeap<double> maxHeap;

      public double R { get; set; }
      private long n;
      public double Alpha {
        get {
          if (minHeap.Count <= 1) return double.PositiveInfinity;
          double xk = minHeap.GetMin();
          if (xk.IsAlmost(0.0)) return double.PositiveInfinity;
          var alpha = 1.0 / (minHeap.Count - 1) * minHeap.Skip(1).Sum(x => Math.Log(x) - Math.Log(xk));
          Debug.Assert(alpha > 0);
          return alpha;
        }
      }
      public OnlineHillEstimator(double r = 0.1) {
        this.R = r;
        this.minHeap = new MinHeap<double>();
        this.maxHeap = new MaxHeap<double>();
        n = 0;
      }

      private double maxNotInHeap = double.NegativeInfinity;
      public void Update(double reward) {
        // if we found a new larger reward then the best n*r so far then insert and drop the smallest
        if (minHeap.Count > 0 && reward > minHeap.GetMin()) {
          maxHeap.Add(minHeap.ExtractDominating());
          minHeap.Add(reward);
        } else {
          maxHeap.Add(reward);
        }

        // increase number of observed rewards
        n++;

        // now it could be possible that we need to increase the size of the heap by 1 
        // => add the largest observed reward so far which is not in the heap
        if (((int)Math.Floor(n * R)) > minHeap.Count) {
          minHeap.Add(maxHeap.ExtractDominating());
        }

        Debug.Assert(minHeap.Count == ((int)Math.Floor(n * R)));
        Debug.Assert(maxHeap.Count == 0 || minHeap.Count == 0 || maxHeap.GetMin() <= minHeap.GetMin());
      }
    }


    public int Tries { get; private set; }
    public IEnumerable<double> Rewards { get { return rewards; } }
    private OnlineHillEstimator hillEstimator;
    private List<double> rewards;
    public double MaxReward { get; private set; }
    public double Value {
      get {
        return hillEstimator.Alpha;
      }
    }
    public ExtremeHunterActionInfo() {
      Reset();
    }

    public void UpdateReward(double reward) {
      if (reward < 0.0) throw new ArgumentException("reward");
      MaxReward = Math.Max(MaxReward, reward);
      Tries++;
      reward = (1 / (1 - reward)); // transformation from [0..1]
      rewards.Add(reward);
      hillEstimator.Update(reward);
    }

    public void Reset() {
      MaxReward = double.NegativeInfinity;

      this.hillEstimator = new OnlineHillEstimator();
      this.rewards = new List<double>();
      this.Tries = 0;
    }

    public override string ToString() {
      return string.Format("{0:F3} {1}", Value, Tries);
    }
  }
}