sampler.h

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard
// All rights reserved.
//
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// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
//   this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
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#ifndef G2O_GAUSSIAN_SAMPLER_
#define G2O_GAUSSIAN_SAMPLER_
 
#include <Eigen/Core>
#include <cassert>
#include <cmath>
#include <cstdlib>
#include <ctime>
#include <memory>
#include <random>
 
#include "g2o_stuff_api.h"
 
namespace g2o {
 
double G2O_STUFF_API sampleUniform(double min = 0, double max = 1,
                                   std::mt19937* generator = 0);
double G2O_STUFF_API sampleGaussian(std::mt19937* generator = 0);
 
template <class SampleType, class CovarianceType>
class GaussianSampler {
 public:
  GaussianSampler(GaussianSampler const&) = delete;
  GaussianSampler& operator=(const GaussianSampler&) = delete;
  GaussianSampler(bool hasGenerator = true)
      : _generator(hasGenerator ? new std::mt19937 : nullptr) {}
  void setDistribution(const CovarianceType& cov) {
    Eigen::LLT<CovarianceType> cholDecomp;
    cholDecomp.compute(cov);
    if (cholDecomp.info() == Eigen::NumericalIssue) {
      assert(false && "Cholesky decomposition on the covariance matrix failed");
      return;
    }
    _cholesky = cholDecomp.matrixL();
  }

  SampleType generateSample() {
    SampleType s;
    for (int i = 0; i < s.size(); i++) {
      s(i) = (_generator) ? sampleGaussian(_generator.get()) : sampleGaussian();
    }
    return _cholesky * s;
  }

  bool seed(int s) {
    if (!_generator) return false;
    _generator->seed(s);
    return true;
  }
 
 protected:
  CovarianceType _cholesky;
  std::unique_ptr<std::mt19937> _generator;
};
 
class G2O_STUFF_API Sampler {
 public:
  static double gaussRand(double mean, double sigma) {
    double y, r2;
    do {
      double x = -1.0 + 2.0 * uniformRand(0.0, 1.0);
      y = -1.0 + 2.0 * uniformRand(0.0, 1.0);
      r2 = x * x + y * y;
    } while (r2 > 1.0 || r2 == 0.0);
    return mean + sigma * y * std::sqrt(-2.0 * log(r2) / r2);
  }

  static double uniformRand(double lowerBndr, double upperBndr) {
    return lowerBndr +
           ((double)std::rand() / (RAND_MAX + 1.0)) * (upperBndr - lowerBndr);
  }

  static void seedRand() {
    seedRand(static_cast<unsigned int>(std::time(NULL)));
  }

  static void seedRand(unsigned int seed) { std::srand(seed); }
};
 
}  // namespace g2o
 
#endif