// 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)

#include "ceres/internal/eigen.h"

#include "ceres/internal/scoped_ptr.h"

#include "ceres/levenberg_marquardt_strategy.h"

#include "ceres/linear_solver.h"

#include "ceres/trust_region_strategy.h"

#include "glog/logging.h"

#include "gmock/gmock.h"

#include "gmock/mock-log.h"

#include "gtest/gtest.h"

using testing::AllOf;

using testing::AnyNumber;

using testing::HasSubstr;

using testing::ScopedMockLog;

using testing::_;

namespace ceres {

namespace internal {

const double kTolerance = 1e-16;

// Linear solver that takes as input a vector and checks that the

// caller passes the same vector as LinearSolver::PerSolveOptions.D.

class RegularizationCheckingLinearSolver : public DenseSparseMatrixSolver {

 public:

  RegularizationCheckingLinearSolver(const int num_cols, const double* diagonal)

      : num_cols_(num_cols),

        diagonal_(diagonal) {

  }

  virtual ~RegularizationCheckingLinearSolver() {}

 private:

  virtual LinearSolver::Summary SolveImpl(

      DenseSparseMatrix* A,

      const double* b,

      const LinearSolver::PerSolveOptions& per_solve_options,

      double* x) {

    CHECK_NOTNULL(per_solve_options.D);

    for (int i = 0; i < num_cols_; ++i) {

      EXPECT_NEAR(per_solve_options.D[i], diagonal_[i], kTolerance)

          << i << " " << per_solve_options.D[i] << " " << diagonal_[i];

    }

    return LinearSolver::Summary();

  }

  const int num_cols_;

  const double* diagonal_;

};

TEST(LevenbergMarquardtStrategy, AcceptRejectStepRadiusScaling) {

  TrustRegionStrategy::Options options;

  options.initial_radius = 2.0;

  options.max_radius = 20.0;

  options.min_lm_diagonal = 1e-8;

  options.max_lm_diagonal = 1e8;

  // We need a non-null pointer here, so anything should do.

  scoped_ptr<LinearSolver> linear_solver(

      new RegularizationCheckingLinearSolver(0, NULL));

  options.linear_solver = linear_solver.get();

  LevenbergMarquardtStrategy lms(options);

  EXPECT_EQ(lms.Radius(), options.initial_radius);

  lms.StepRejected(0.0);

  EXPECT_EQ(lms.Radius(), 1.0);

  lms.StepRejected(-1.0);

  EXPECT_EQ(lms.Radius(), 0.25);

  lms.StepAccepted(1.0);

  EXPECT_EQ(lms.Radius(), 0.25 * 3.0);

  lms.StepAccepted(1.0);

  EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0);

  lms.StepAccepted(0.25);

  EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125);

  lms.StepAccepted(1.0);

  EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125 * 3.0);

  lms.StepAccepted(1.0);

  EXPECT_EQ(lms.Radius(), 0.25 * 3.0 * 3.0 / 1.125 * 3.0 * 3.0);

  lms.StepAccepted(1.0);

  EXPECT_EQ(lms.Radius(), options.max_radius);

}

TEST(LevenbergMarquardtStrategy, CorrectDiagonalToLinearSolver) {

  Matrix jacobian(2, 3);

  jacobian.setZero();

  jacobian(0, 0) = 0.0;

  jacobian(0, 1) = 1.0;

  jacobian(1, 1) = 1.0;

  jacobian(0, 2) = 100.0;

  double residual = 1.0;

  double x[3];

  DenseSparseMatrix dsm(jacobian);

  TrustRegionStrategy::Options options;

  options.initial_radius = 2.0;

  options.max_radius = 20.0;

  options.min_lm_diagonal = 1e-2;

  options.max_lm_diagonal = 1e2;

  double diagonal[3];

  diagonal[0] = options.min_lm_diagonal;

  diagonal[1] = 2.0;

  diagonal[2] = options.max_lm_diagonal;

  for (int i = 0; i < 3; ++i) {

    diagonal[i] = sqrt(diagonal[i] / options.initial_radius);

  }

  RegularizationCheckingLinearSolver linear_solver(3, diagonal);

  options.linear_solver = &linear_solver;

  LevenbergMarquardtStrategy lms(options);

  TrustRegionStrategy::PerSolveOptions pso;

  {

    ScopedMockLog log;

    EXPECT_CALL(log, Log(_, _, _)).Times(AnyNumber());

    // This using directive is needed get around the fact that there

    // are versions of glog which are not in the google namespace.

    using namespace google;

#if defined(_MSC_VER)

    // Use GLOG_WARNING to support MSVC if GLOG_NO_ABBREVIATED_SEVERITIES

    // is defined.

    EXPECT_CALL(log, Log(GLOG_WARNING, _,

                         HasSubstr("Failed to compute a step")));

#else

    EXPECT_CALL(log, Log(google::WARNING, _,

                         HasSubstr("Failed to compute a step")));

#endif

    TrustRegionStrategy::Summary summary =

        lms.ComputeStep(pso, &dsm, &residual, x);

    EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_FAILURE);

  }

}

}  // namespace internal

}  // namespace ceres