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
//
 
#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);
    ~ProcNormalize() 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 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;
  };
 
  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