| // 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: |
| // |
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| // 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" |
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| // 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/sized_cost_function.h" |
| #include "ceres/types.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| // Noise factor for randomized cost function. |
| static const double kNoiseFactor = 0.01; |
| |
| // Default random seed for randomized cost function. |
| static const 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 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: |
| virtual bool Evaluate(double const* const* parameters, |
| double* residuals, |
| double** /* not used */) const { |
| 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 TranscendentalFunctor { |
| public: |
| bool operator()(const double* x1, const double* x2, double* residuals) const; |
| void ExpectCostFunctionEvaluationIsNearlyCorrect( |
| const CostFunction& cost_function, |
| NumericDiffMethodType method) const; |
| }; |
| |
| class TranscendentalCostFunction : public SizedCostFunction<2, 5, 5> { |
| public: |
| virtual bool Evaluate(double const* const* parameters, |
| double* residuals, |
| double** /* not used */) const { |
| return functor_(parameters[0], parameters[1], residuals); |
| } |
| private: |
| TranscendentalFunctor functor_; |
| }; |
| |
| // y = exp(x), dy/dx = exp(x) |
| class ExponentialFunctor { |
| public: |
| bool operator()(const double* x1, double* residuals) const; |
| void ExpectCostFunctionEvaluationIsNearlyCorrect( |
| const CostFunction& cost_function) const; |
| }; |
| |
| class ExponentialCostFunction : public SizedCostFunction<1, 1> { |
| public: |
| virtual bool Evaluate(double const* const* parameters, |
| double* residuals, |
| double** /* not used */) const { |
| 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 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 RandomizedCostFunction : public SizedCostFunction<1, 1> { |
| public: |
| RandomizedCostFunction(double noise_factor, unsigned int random_seed) |
| : functor_(noise_factor, random_seed) { |
| } |
| |
| virtual bool Evaluate(double const* const* parameters, |
| double* residuals, |
| double** /* not used */) const { |
| return functor_(parameters[0], residuals); |
| } |
| |
| private: |
| RandomizedFunctor functor_; |
| }; |
| |
| |
| } // namespace internal |
| } // namespace ceres |
| |
| #endif // CERES_INTERNAL_NUMERIC_DIFF_TEST_UTILS_H_ |