source: branches/HeuristicLab.Problems.GrammaticalOptimization/HeuristicLab.Algorithms.Bandits/Models/GaussianMixtureModel.cs @ 11742

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.Models {
  public class GaussianMixtureModel : IModel {
    private readonly int numActions;
    private readonly double[][] meanMean; // mean of mean for each arm and component 
    private readonly double[][] meanVariance; // variance of mean for each arm and component 
    private readonly double[][] componentProb;

    // parameters of beta prior distribution
    private int numComponents;
    private double priorMean;

    public GaussianMixtureModel(int numActions, double priorMean = 0.5, int nComponents = 5) {
      this.numActions = numActions;
      this.numComponents = nComponents;
      this.priorMean = priorMean;
      this.meanMean = new double[numActions][];
      this.meanVariance = new double[numActions][];
      this.componentProb = new double[numActions][];
      for (int a = 0; a < numActions; a++) {
        // TODO: probably need to initizalize this randomly to allow learning
        meanMean[a] = Enumerable.Repeat(priorMean, nComponents).ToArray();
        meanVariance[a] = Enumerable.Repeat(1.0, nComponents).ToArray(); // prior variance of mean variance = 1
        componentProb[a] = Enumerable.Repeat(1.0 / nComponents, nComponents).ToArray(); // uniform prior for component probabilities
      }
    }


    public double[] SampleExpectedRewards(Random random) {
      // sample mean foreach action and component from the prior
      var exp = new double[numActions];
      for (int a = 0; a < numActions; a++) {
        var sumReward = 0.0;
        var numSamples = 10000;
        var sampledComponents = Enumerable.Range(0, numComponents).SampleProportional(random, componentProb[a]).Take(numSamples);
        foreach (var k in sampledComponents) {
          sumReward += Rand.RandNormal(random) * Math.Sqrt(meanVariance[a][k]) + meanMean[a][k];
        }
        exp[a] = sumReward / (double)numSamples;
      }

      return exp;
    }

    public void Update(int action, double reward) {
      // see http://www.cs.toronto.edu/~mackay/itprnn/ps/302.320.pdf Algorithm 22.2 soft k-means
      throw new NotImplementedException();
    }

    public void Disable(int action) {
      Array.Clear(meanMean[action], 0, meanMean[action].Length);
      Array.Clear(meanVariance[action], 0, meanVariance[action].Length);
    }

    public void Reset() {
      Array.Clear(meanMean, 0, meanMean.Length);
      Array.Clear(meanVariance, 0, meanVariance.Length);
    }

    public void PrintStats() {
      throw new NotImplementedException();
    }
  }
}