ProcNormalize.cc

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

// Implementation:
//     Normalizes the input variables. n values in each input variable
//     is normalized to n values for each input variables. The normalization
//     consists of a range normalization step (min...max) and mapping step
//     that equalizes using the probability distribution (via PDF).
//
// Author:      Christophe Saout
// Created:     Sat Apr 24 15:18 CEST 2007
// $Id: ProcNormalize.cc,v 1.9 2009/06/03 09:50:14 saout Exp $
//

#include <vector>

#include "CondFormats/PhysicsToolsObjects/interface/Histogram.h"
#include "PhysicsTools/MVAComputer/interface/VarProcessor.h"
#include "PhysicsTools/MVAComputer/interface/Calibration.h"
#include "PhysicsTools/MVAComputer/interface/Spline.h"

using namespace PhysicsTools;

namespace { // anonymous

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

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

    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 Map {
        Map(const Calibration::HistogramF &pdf) :
            min(pdf.range().min), width(pdf.range().width())
        {
            std::vector<double> values(
                    pdf.values().begin() + 1,
                    pdf.values().end() - 1);
            spline.set(values.size(), &values.front());
        }

        double      min, width;
        Spline      spline;
    };

    void findMap(ValueIterator iter, unsigned int n,
                 std::vector<Map>::const_iterator &begin,
                 std::vector<Map>::const_iterator &end) const;

    std::vector<Map>    maps;
    int         categoryIdx;
    unsigned int        nCategories;
};

static ProcNormalize::Registry registry("ProcNormalize");

ProcNormalize::ProcNormalize(const char *name,
                             const Calibration::ProcNormalize *calib,
                             const MVAComputer *computer) :
    VarProcessor(name, calib, computer),
    maps(calib->distr.begin(), calib->distr.end()),
    categoryIdx(calib->categoryIdx),
    nCategories(1)
{
}

void ProcNormalize::configure(ConfIterator iter, unsigned int n)
{
    if (categoryIdx >= 0) {
        if ((int)n < categoryIdx + 1)
            return;
        nCategories = maps.size() / (n - 1);
        if (nCategories * (n - 1) != maps.size())
            return;
    } else if (n != maps.size())
        return;

    int i = 0;
    while(iter) {
        if (categoryIdx == i++)
            iter++(Variable::FLAG_NONE);
        else
            iter << iter++(Variable::FLAG_ALL);
    }
}

void ProcNormalize::findMap(ValueIterator iter, unsigned int n,
                            std::vector<Map>::const_iterator &begin,
                            std::vector<Map>::const_iterator &end) const
{
    if (categoryIdx >= 0) {
        ValueIterator iter2 = iter;
        for(int i = 0; i < categoryIdx; i++)
            ++iter2;

        int cat = (int)*iter2;
        if (cat < 0 || (unsigned int)cat >= nCategories) {
            for(; iter; ++iter)
                iter();
            return;
        }

        begin = maps.begin() + cat * (n - 1);
        end = begin + (n - 1);
    } else {
        begin = maps.begin();
        end = maps.end();
    }

}

void ProcNormalize::eval(ValueIterator iter, unsigned int n) const
{
    std::vector<Map>::const_iterator map, last;
    findMap(iter, n, map, last);

    for(int i = 0; map != last; ++iter, i++) {
        if (i == categoryIdx)
            continue;
        for(double *value = iter.begin();
            value < iter.end(); value++) {
            double val = *value;
            val = (val - map->min) / map->width;
            val = map->spline.integral(val);
            iter << val;
        }
        iter();
        ++map;
    }
}

std::vector<double> ProcNormalize::deriv(ValueIterator iter,
                                         unsigned int n) const
{
    std::vector<Map>::const_iterator map, last;
    findMap(iter, n, map, last);

    unsigned int size = 0;
    for(ValueIterator iter2 = iter; iter2; ++iter2)
        size += iter2.size();

    std::vector<double> result(size * size);

    unsigned int j = 0;
    for(int i = 0; map != last; ++iter, i++) {
        if (i == categoryIdx) {
            j += iter.size();
            continue;
        }

        for(double *value = iter.begin();
            value < iter.end(); value++, j++) {
            double val = *value;
            val = (val - map->min) / map->width;
            val = map->spline.eval(val) *
                  (map->spline.numberOfEntries() - 1) /
                  (map->width * map->spline.getArea());
            result[j * size + j] = val;
        }
        ++map;
    }

    return result;
}

} // anonymous namespace