| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2023 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) |
| |
| #include "ceres/numeric_diff_first_order_function.h" |
| |
| #include <memory> |
| |
| #include "ceres/array_utils.h" |
| #include "ceres/first_order_function.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres::internal { |
| |
| class QuadraticCostFunctor { |
| public: |
| explicit QuadraticCostFunctor(double a) : a_(a) {} |
| bool operator()(const double* const x, double* cost) const { |
| cost[0] = x[0] * x[1] + x[2] * x[3] - a_; |
| return true; |
| } |
| |
| private: |
| double a_; |
| }; |
| |
| TEST(NumericDiffFirstOrderFunction, BilinearDifferentiationTestStatic) { |
| auto function = std::make_unique< |
| NumericDiffFirstOrderFunction<QuadraticCostFunctor, CENTRAL, 4>>( |
| new QuadraticCostFunctor(1.0)); |
| |
| double parameters[4] = {1.0, 2.0, 3.0, 4.0}; |
| double gradient[4]; |
| double cost; |
| |
| function->Evaluate(parameters, &cost, nullptr); |
| EXPECT_EQ(cost, 13.0); |
| |
| cost = -1.0; |
| function->Evaluate(parameters, &cost, gradient); |
| |
| EXPECT_EQ(cost, 13.0); |
| |
| const double kTolerance = 1e-9; |
| EXPECT_NEAR(gradient[0], parameters[1], kTolerance); |
| EXPECT_NEAR(gradient[1], parameters[0], kTolerance); |
| EXPECT_NEAR(gradient[2], parameters[3], kTolerance); |
| EXPECT_NEAR(gradient[3], parameters[2], kTolerance); |
| } |
| |
| TEST(NumericDiffFirstOrderFunction, BilinearDifferentiationTestDynamic) { |
| auto function = std::make_unique< |
| NumericDiffFirstOrderFunction<QuadraticCostFunctor, CENTRAL>>( |
| new QuadraticCostFunctor(1.0), 4); |
| |
| double parameters[4] = {1.0, 2.0, 3.0, 4.0}; |
| double gradient[4]; |
| double cost; |
| |
| function->Evaluate(parameters, &cost, nullptr); |
| EXPECT_EQ(cost, 13.0); |
| |
| cost = -1.0; |
| function->Evaluate(parameters, &cost, gradient); |
| |
| EXPECT_EQ(cost, 13.0); |
| |
| const double kTolerance = 1e-9; |
| EXPECT_NEAR(gradient[0], parameters[1], kTolerance); |
| EXPECT_NEAR(gradient[1], parameters[0], kTolerance); |
| EXPECT_NEAR(gradient[2], parameters[3], kTolerance); |
| EXPECT_NEAR(gradient[3], parameters[2], kTolerance); |
| } |
| |
| } // namespace ceres::internal |