| // Ceres Solver - A fast non-linear least squares minimizer | 
 | // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. | 
 | // http://code.google.com/p/ceres-solver/ | 
 | // | 
 | // 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 | 
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 | // 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/autodiff_cost_function.h" | 
 |  | 
 | #include <cstddef> | 
 |  | 
 | #include "gtest/gtest.h" | 
 | #include "ceres/cost_function.h" | 
 |  | 
 | namespace ceres { | 
 | namespace internal { | 
 |  | 
 | class BinaryScalarCost { | 
 |  public: | 
 |   explicit BinaryScalarCost(double a): a_(a) {} | 
 |   template <typename T> | 
 |   bool operator()(const T* const x, const T* const y, | 
 |                   T* cost) const { | 
 |     cost[0] = x[0] * y[0] + x[1] * y[1]  - T(a_); | 
 |     return true; | 
 |   } | 
 |  private: | 
 |   double a_; | 
 | }; | 
 |  | 
 | TEST(AutoDiffResidualAndJacobian, BilinearDifferentiationTest) { | 
 |   CostFunction* cost_function  = | 
 |     new AutoDiffCostFunction<BinaryScalarCost, 1, 2, 2>( | 
 |         new BinaryScalarCost(1.0)); | 
 |  | 
 |   double** parameters = new double*[2]; | 
 |   parameters[0] = new double[2]; | 
 |   parameters[1] = new double[2]; | 
 |  | 
 |   parameters[0][0] = 1; | 
 |   parameters[0][1] = 2; | 
 |  | 
 |   parameters[1][0] = 3; | 
 |   parameters[1][1] = 4; | 
 |  | 
 |   double** jacobians = new double*[2]; | 
 |   jacobians[0] = new double[2]; | 
 |   jacobians[1] = new double[2]; | 
 |  | 
 |   double residuals = 0.0; | 
 |  | 
 |   cost_function->Evaluate(parameters, &residuals, NULL); | 
 |   EXPECT_EQ(residuals, 10); | 
 |   cost_function->Evaluate(parameters, &residuals, jacobians); | 
 |  | 
 |   EXPECT_EQ(jacobians[0][0], 3); | 
 |   EXPECT_EQ(jacobians[0][1], 4); | 
 |   EXPECT_EQ(jacobians[1][0], 1); | 
 |   EXPECT_EQ(jacobians[1][1], 2); | 
 |  | 
 |   delete []jacobians[0]; | 
 |   delete []jacobians[1]; | 
 |   delete []parameters[0]; | 
 |   delete []parameters[1]; | 
 |   delete []jacobians; | 
 |   delete []parameters; | 
 |   delete cost_function; | 
 | } | 
 |  | 
 | }  // namespace internal | 
 | }  // namespace ceres |