// 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/
//
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//
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// 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