source: branches/2893_BNLR/HeuristicLab.Random/3.3/HamiltonianMonteCarlo.cs @ 16123

#region License Information

/* HeuristicLab
 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */

#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Core;

namespace HeuristicLab.Random {
  /// <summary>
  /// See Radford Neal, MCMC using Hamiltonian dynamics, 2011, https://arxiv.org/pdf/1206.1901.pdf
  /// Algorithm from the paper:
  /// 
  /// HMC = function (U, grad_U, epsilon, L, current_q)
  /// {
  ///   q = current_q
  ///   p = rnorm(length(q), 0, 1) # independent standard normal variates
  ///   current_p = p
  ///   # Make a half step for momentum at the beginning
  ///   p = p - epsilon * grad_U(q) / 2
  ///   # Alternate full steps for position and momentum
  ///   for (i in 1:L)
  ///   {
  ///     # Make a full step for the position
  ///     q = q + epsilon* p
  ///     # Make a full step for the momentum, except at end of trajectory
  ///     if (i!=L) p = p - epsilon * grad_U(q)
  ///   }
  ///   # Make a half step for momentum at the end.
  ///   p = p - epsilon* grad_U(q) / 2
  ///   # Negate momentum at end of trajectory to make the proposal symmetric
  ///   p = -p
  ///   # Evaluate potential and kinetic energies at start and end of trajectory
  ///     current_U = U(current_q)
  ///     current_K = sum(current_p^2) / 2
  ///     proposed_U = U(q)
  ///     proposed_K = sum(p^2) / 2
  ///   # Accept or reject the state at end of trajectory, returning either
  ///   # the position at the end of the trajectory or the initial position
  ///   if (runif(1) < exp(current_U-proposed_U+current_K-proposed_K)) {
  ///     return (q) # accept
  ///   }
  ///   else {
  ///     return (current_q) # reject
  ///   }
  /// }

  /// </summary>
  public static class HamiltonianMonteCarlo {

    public static IEnumerable<double[]> SampleChain(double[] startingPosition,
      Func<double[], Tuple<double, double[]>> potentialEnergyFunction,
      IRandom uniformRandom, double stepSize, int steps = 10) {
      var x = (double[])startingPosition.Clone();
      while(true) {
        x = Sample(x, potentialEnergyFunction, uniformRandom, stepSize, steps);
        yield return x;
      }
    }

    public static double[] Sample(
      double[] startingPosition,
      Func<double[], Tuple<double, double[]>> potentialEnergyFunction, // U
      IRandom uniformRandom, double stepSize, int steps) {
      // step 1
      // new values for the momentum variables are randomly drawn from their Gaussian 
      // distribution, independently if the current values of the position variables

      // step 2
      // Metropolis update is performed, using Hamiltonian dynamics to propose a new state.
      // Starting with the current state Hamiltonian dynamics is simulated for L steps
      // using the Leapfrog method with a step size of eps. 
      // L and eps are parameters of the algorithm, which need to be tuned to obtain 
      // good performance.
      // The proposed state is accepted as the next state of the Markov chain with
      // probability [...].
      // If the proposed state is not accepted the next state is the same as the current state
      // (and is counted again when estimating the expectation of some function of
      // state by its average over states of the Markov chain).


      int nVars = startingPosition.Length;
      var u_initial = potentialEnergyFunction(startingPosition).Item1;

      // rename variables to match Neal's algorithm
      var current_q = startingPosition;
      var epsilon = stepSize;
      var L = steps;

      var q = current_q;
      // p = rnorm(length(q), 0, 1) # independent standard normal variates
      var p = q
        .Select(_ => NormalDistributedRandom.NextDouble(uniformRandom, 0, 1))
        .ToArray();
      var current_p = p;
      // Make a half step for momentum at the beginning
      // p = p - epsilon * grad_U(q) / 2;
      p = VectorSum(p, ScaleVector(-0.5 * epsilon, potentialEnergyFunction(q).Item2));
      // Alternate full steps for position and momentum

      for (int i = 1; i <= L; i++) {
        // Make a full step for the position
        // q = q + epsilon * p;
        q = VectorSum(q, ScaleVector(epsilon, p));
        // Make a full step for the momentum, except at end of trajectory
        if (i != L)
          p = VectorSum(p, ScaleVector(-epsilon, potentialEnergyFunction(q).Item2));
      }
      // Make a half step for momentum at the end.
      ///   p = p - epsilon* grad_U(q) / 2
      p = VectorSum(p, ScaleVector(-0.5 * epsilon, potentialEnergyFunction(q).Item2));
      // Negate momentum at end of trajectory to make the proposal symmetric
      ///   p = -p
      p = ScaleVector(-1.0, p);
      // Evaluate potential and kinetic energies at start and end of trajectory
      double current_U = potentialEnergyFunction(current_q).Item1;
      // sum(current_p ^ 2) / 2
      double current_K = 0.5 * current_p.Sum(pi => pi * pi);
      double proposed_U = potentialEnergyFunction(q).Item1;
      // sum(p^2) / 2
      double proposed_K = 0.5 * p.Sum(pi => pi * pi);
      ///   # Accept or reject the state at end of trajectory, returning either
      ///   # the position at the end of the trajectory or the initial position
      if (uniformRandom.NextDouble() < Math.Exp(current_U - proposed_U + current_K - proposed_K)) {
        return q; // accept
      } else {
        return current_q; // # reject
      }
    }

    #region to be improved or removed
    private static double VectorProd(double[] x, double[] y) {
      double s = 0.0;
      for (int i = 0; i < x.Length; i++)
        s += x[i] * y[i];
      return s;
    }

    private static double[] VectorSum(double[] x, double[] y) {
      double[] s = new double[x.Length];
      for (int i = 0; i < x.Length; i++)
        s[i] += x[i] + y[i];
      return s;
    }

    private static double[] ScaleVector(double a, double[] x) {
      double[] s = new double[x.Length];
      for (int i = 0; i < x.Length; i++)
        s[i] = a * x[i];
      return s;
    }
    #endregion
  }
}