ProcMLP.cc

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// -*- C++ -*-
//
// Package:     MVAComputer
// Class  :     ProcMLP
//

// Implementation:
//     An evaluator for a feed-forward neural net (multi-layer perceptron).
//     Each layer has (n + 1) x m weights for n input neurons, 1 bias
//     and m neurons. Also each layer can select between linear and logistic
//     activation function. The output from the last layer is returned.
//
// Author:      Christophe Saout
// Created:     Sat Apr 24 15:18 CEST 2007
//

#include <cstdlib>
#include <algorithm>
#include <iterator>
#include <vector>
#include <cmath>

#include "FWCore/Utilities/interface/Exception.h"

#include "PhysicsTools/MVAComputer/interface/VarProcessor.h"
#include "PhysicsTools/MVAComputer/interface/Calibration.h"

using namespace PhysicsTools;

namespace {  // anonymous

  class ProcMLP : public VarProcessor {
  public:
    typedef VarProcessor::Registry::Registry<ProcMLP, Calibration::ProcMLP> Registry;

    ProcMLP(const char *name, const Calibration::ProcMLP *calib, const MVAComputer *computer);
    ~ProcMLP() override {}

    void configure(ConfIterator iter, unsigned int n) override;
    void eval(ValueIterator iter, unsigned int n) const override;
    std::vector<double> deriv(ValueIterator iter, unsigned int n) const override;

  private:
    struct Layer {
      Layer(const Calibration::ProcMLP::Layer &calib);
      Layer(const Layer &orig)
          : inputs(orig.inputs), neurons(orig.neurons), coeffs(orig.coeffs), sigmoid(orig.sigmoid) {}

      unsigned int inputs;
      unsigned int neurons;
      std::vector<double> coeffs;
      bool sigmoid;
    };

    std::vector<Layer> layers;
    unsigned int maxTmp;
  };

  ProcMLP::Registry registry("ProcMLP");

  ProcMLP::Layer::Layer(const Calibration::ProcMLP::Layer &calib)
      : inputs(calib.first.front().second.size()), neurons(calib.first.size()), sigmoid(calib.second) {
    typedef Calibration::ProcMLP::Neuron Neuron;

    coeffs.resize(neurons * (inputs + 1));
    std::vector<double>::iterator inserter = coeffs.begin();

    for (std::vector<Neuron>::const_iterator iter = calib.first.begin(); iter != calib.first.end(); iter++) {
      *inserter++ = iter->first;

      if (iter->second.size() != inputs)
        throw cms::Exception("ProcMLPInput") << "ProcMLP neuron layer inconsistent." << std::endl;

      inserter = std::copy(iter->second.begin(), iter->second.end(), inserter);
    }
  }

  ProcMLP::ProcMLP(const char *name, const Calibration::ProcMLP *calib, const MVAComputer *computer)
      : VarProcessor(name, calib, computer), maxTmp(0) {
    std::copy(calib->layers.begin(), calib->layers.end(), std::back_inserter(layers));

    for (unsigned int i = 0; i < layers.size(); i++) {
      maxTmp = std::max<unsigned int>(maxTmp, layers[i].neurons);
      if (i > 0 && layers[i - 1].neurons != layers[i].inputs)
        throw cms::Exception("ProcMLPInput") << "ProcMLP neuron layers do not connect "
                                                "properly."
                                             << std::endl;
    }
  }

  void ProcMLP::configure(ConfIterator iter, unsigned int n) {
    if (n != layers.front().inputs)
      return;

    for (unsigned int i = 0; i < n; i++)
      iter++(Variable::FLAG_NONE);

    for (unsigned int i = 0; i < layers.back().neurons; i++)
      iter << Variable::FLAG_NONE;
  }

  void ProcMLP::eval(ValueIterator iter, unsigned int n) const {
    double *tmp = (double *)alloca(2 * maxTmp * sizeof(double));
    bool flip = false;

    for (double *pos = tmp; iter; iter++, pos++)
      *pos = *iter;

    double *output = nullptr;
    for (std::vector<Layer>::const_iterator layer = layers.begin(); layer != layers.end(); layer++, flip = !flip) {
      const double *input = &tmp[flip ? maxTmp : 0];
      output = &tmp[flip ? 0 : maxTmp];
      std::vector<double>::const_iterator coeff = layer->coeffs.begin();
      for (unsigned int i = 0; i < layer->neurons; i++) {
        double sum = *coeff++;
        for (unsigned int j = 0; j < layer->inputs; j++)
          sum += input[j] * *coeff++;
        if (layer->sigmoid)
          sum = 1.0 / (std::exp(-sum) + 1.0);
        *output++ = sum;
      }
    }

    for (const double *pos = &tmp[flip ? maxTmp : 0]; pos < output; pos++)
      iter(*pos);
  }

  std::vector<double> ProcMLP::deriv(ValueIterator iter, unsigned int n) const {
    std::vector<double> prevValues, nextValues;
    std::vector<double> prevMatrix, nextMatrix;

    while (iter)
      nextValues.push_back(*iter++);

    unsigned int size = nextValues.size();
    nextMatrix.resize(size * size);
    for (unsigned int i = 0; i < size; i++)
      nextMatrix[i * size + i] = 1.;

    for (std::vector<Layer>::const_iterator layer = layers.begin(); layer != layers.end(); layer++) {
      prevValues.clear();
      std::swap(prevValues, nextValues);
      prevMatrix.clear();
      std::swap(prevMatrix, nextMatrix);

      std::vector<double>::const_iterator coeff = layer->coeffs.begin();
      for (unsigned int i = 0; i < layer->neurons; i++) {
        double sum = *coeff++;
        for (unsigned int j = 0; j < layer->inputs; j++)
          sum += prevValues[j] * *coeff++;

        double deriv;
        if (layer->sigmoid) {
          double e = std::exp(-sum);
          sum = 1.0 / (e + 1.0);
          deriv = 1.0 / (e + 1.0 / e + 2.0);
        } else
          deriv = 1.0;

        nextValues.push_back(sum);

        for (unsigned int k = 0; k < size; k++) {
          sum = 0.0;
          coeff -= layer->inputs;
          for (unsigned int j = 0; j < layer->inputs; j++)
            sum += prevMatrix[j * size + k] * *coeff++;
          nextMatrix.push_back(sum * deriv);
        }
      }
    }

    return nextMatrix;
  }

}  // anonymous namespace