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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// 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 notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#ifndef CERES_INTERNAL_NUMERIC_DIFF_TEST_UTILS_H_
#define CERES_INTERNAL_NUMERIC_DIFF_TEST_UTILS_H_
#include "ceres/cost_function.h"
#include "ceres/internal/export.h"
#include "ceres/sized_cost_function.h"
#include "ceres/types.h"
namespace ceres {
namespace internal {
// Noise factor for randomized cost function.
static constexpr double kNoiseFactor = 0.01;
// Default random seed for randomized cost function.
static constexpr unsigned int kRandomSeed = 1234;
// y1 = x1'x2 -> dy1/dx1 = x2, dy1/dx2 = x1
// y2 = (x1'x2)^2 -> dy2/dx1 = 2 * x2 * (x1'x2), dy2/dx2 = 2 * x1 * (x1'x2)
// y3 = x2'x2 -> dy3/dx1 = 0, dy3/dx2 = 2 * x2
class CERES_NO_EXPORT EasyFunctor {
public:
bool operator()(const double* x1, const double* x2, double* residuals) const;
void ExpectCostFunctionEvaluationIsNearlyCorrect(
const CostFunction& cost_function, NumericDiffMethodType method) const;
};
class EasyCostFunction : public SizedCostFunction<3, 5, 5> {
public:
bool Evaluate(double const* const* parameters,
double* residuals,
double** /* not used */) const final {
return functor_(parameters[0], parameters[1], residuals);
}
private:
EasyFunctor functor_;
};
// y1 = sin(x1'x2)
// y2 = exp(-x1'x2 / 10)
//
// dy1/dx1 = x2 * cos(x1'x2), dy1/dx2 = x1 * cos(x1'x2)
// dy2/dx1 = -x2 * exp(-x1'x2 / 10) / 10, dy2/dx2 = -x2 * exp(-x1'x2 / 10) / 10
class CERES_NO_EXPORT TranscendentalFunctor {
public:
bool operator()(const double* x1, const double* x2, double* residuals) const;
void ExpectCostFunctionEvaluationIsNearlyCorrect(
const CostFunction& cost_function, NumericDiffMethodType method) const;
};
class CERES_NO_EXPORT TranscendentalCostFunction
: public SizedCostFunction<2, 5, 5> {
public:
bool Evaluate(double const* const* parameters,
double* residuals,
double** /* not used */) const final {
return functor_(parameters[0], parameters[1], residuals);
}
private:
TranscendentalFunctor functor_;
};
// y = exp(x), dy/dx = exp(x)
class CERES_NO_EXPORT ExponentialFunctor {
public:
bool operator()(const double* x1, double* residuals) const;
void ExpectCostFunctionEvaluationIsNearlyCorrect(
const CostFunction& cost_function) const;
};
class ExponentialCostFunction : public SizedCostFunction<1, 1> {
public:
bool Evaluate(double const* const* parameters,
double* residuals,
double** /* not used */) const final {
return functor_(parameters[0], residuals);
}
private:
ExponentialFunctor functor_;
};
// Test adaptive numeric differentiation by synthetically adding random noise
// to a functor.
// y = x^2 + [random noise], dy/dx ~ 2x
class CERES_NO_EXPORT RandomizedFunctor {
public:
RandomizedFunctor(double noise_factor, unsigned int random_seed)
: noise_factor_(noise_factor), random_seed_(random_seed) {}
bool operator()(const double* x1, double* residuals) const;
void ExpectCostFunctionEvaluationIsNearlyCorrect(
const CostFunction& cost_function) const;
private:
double noise_factor_;
unsigned int random_seed_;
};
class CERES_NO_EXPORT RandomizedCostFunction : public SizedCostFunction<1, 1> {
public:
RandomizedCostFunction(double noise_factor, unsigned int random_seed)
: functor_(noise_factor, random_seed) {}
bool Evaluate(double const* const* parameters,
double* residuals,
double** /* not used */) const final {
return functor_(parameters[0], residuals);
}
private:
RandomizedFunctor functor_;
};
} // namespace internal
} // namespace ceres
#endif // CERES_INTERNAL_NUMERIC_DIFF_TEST_UTILS_H_
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