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
// $Id: ProcMLP.cc,v 1.4 2009/06/03 09:50:14 saout Exp $
//

#include <stdlib.h>
#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);
    virtual ~ProcMLP() {}

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

    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;
};

static 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 = 0;
    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