HopfieldNetwork.cc

/**************************************************************************
 * basf2 (Belle II Analysis Software Framework)                           *
 * Author: The Belle II Collaboration                                     *
 *                                                                        *
 * See git log for contributors and copyright holders.                    *
 * This file is licensed under LGPL-3.0, see LICENSE.md.                  *
 **************************************************************************/
 
#include <tracking/trackFindingVXD/trackSetEvaluator/HopfieldNetwork.h>
 
#include <framework/logging/Logger.h>
#include <framework/utilities/TRandomWrapper.h>
 
#include <Eigen/Dense>
 
#include <numeric>
 
using namespace Belle2;
 
unsigned short HopfieldNetwork::doHopfield(
  std::vector<OverlapResolverNodeInfo>& overlapResolverNodeInfos, unsigned short nIterations)
{
  //Start value for neurons if they are compatible.
  //Each compatible connection activates a node with this value.
  //As the sum of all the activations shall be less than one, we divide the
  //activation by the total number of Nodes.
  //Incompatible Nodes get later minus one, which counteracts all activation,
  //if the incompatible Node is active.
  if (overlapResolverNodeInfos.size() < 2) {
    B2DEBUG(20, "No reason to doHopfield with less than 2 nodes!");
    return 0;
  }
 
  const float compatibilityValue = (1.0 - m_omega) / static_cast<float>(overlapResolverNodeInfos.size() - 1);
 
  const size_t overlapSize = overlapResolverNodeInfos.size();
 
  //Weight matrix; knows compatibility between each possible pair of Nodes
  Eigen::MatrixXd W(overlapSize, overlapSize);
  //A): Set all elements to compatible:
  W.fill(compatibilityValue);
 
  //B): Inform weight matrix elements of incompatible neurons:
  for (const auto& aTC : overlapResolverNodeInfos) {
    for (unsigned int overlapIndex : aTC.overlaps) {
      W(aTC.trackIndex, overlapIndex) = -1.0;
    }
  }
 
 
  // Neuron values
  Eigen::VectorXd x(overlapSize);
  // randomize neuron values for first iteration:
  for (unsigned int i = 0; i < overlapSize; i++) {
    x(i) = gRandom->Uniform(1.0); // WARNING: original does Un(0;0.1) not Un(0;1)!
  }
 
  //Store for results from last round:
  Eigen::VectorXd xOld(overlapSize);
 
  //Order of execution for neuron values:
  std::vector<unsigned short> sequenceVector(overlapSize);
  //iota fills the vector with 0, 1, 2, ... , (size-1)
  std::iota(sequenceVector.begin(), sequenceVector.end(), 0);
 
  //The following block will be evaluated to empty, if LOG_NO_B2DEBUG is defined:
  B2DEBUG(29, "sequenceVector with length " << sequenceVector.size());
  B2DEBUG(29, "Entries are from begin to end:");
  for (auto&& entry : sequenceVector) {
    B2DEBUG(29, std::to_string(entry) + ", ");
  }
 
  //Store all values of c for protocolling:
  std::vector<float> cValues(nIterations);
  //Store for maximum change of weights between iterations.
  float c = 1.0;
 
  //Iterate until change in weights is small:
  unsigned iIterations = 0;
 
  float T = m_T;
 
 
  while (c > m_cmax) {
    std::shuffle(sequenceVector.begin(), sequenceVector.end(), TRandomWrapper());
 
    xOld = x;
 
    for (unsigned int i : sequenceVector) {
      float aTempVal = W.row(i).dot(x);
      float act = aTempVal + m_omega * overlapResolverNodeInfos[i].qualityIndicator;
      x(i) = 0.5 * (1. + tanh(act / T));
    }
 
    T = 0.5 * (T + m_Tmin);
 
    //Determine maximum change in weights:
    c = (x - xOld).cwiseAbs().maxCoeff();
    B2DEBUG(21, "c value is " << c << " at iteration " << iIterations);
    cValues.at(iIterations) = c;
 
    if (iIterations + 1 == nIterations) {
      B2INFO("Hopfield reached maximum number of iterations without convergence. cValues are:");
      for (auto&& entry : cValues) {
        B2INFO(std::to_string(entry));
      }
      break;
    }
    iIterations++;
  }
 
  //Copy Node values into the activity state of the OverlapResolverNodeInfo objects:
  for (unsigned int i = 0; i < overlapSize; i++) {
    overlapResolverNodeInfos[i].activityState = x(i);
  }
 
  return iIterations;
}