// Ceres Solver - A fast non-linear least squares minimizer

// Copyright 2015 Google Inc. All rights reserved.

// http://ceres-solver.org/

//

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// Author: sameeragarwal@google.com (Sameer Agarwal)

//         mierle@gmail.com (Keir Mierle)

#include <cstddef>

#include "ceres/dynamic_numeric_diff_cost_function.h"

#include "ceres/internal/scoped_ptr.h"

#include "gtest/gtest.h"

namespace ceres {

namespace internal {

using std::vector;

const double kTolerance = 1e-6;

// Takes 2 parameter blocks:

//     parameters[0] is size 10.

//     parameters[1] is size 5.

// Emits 21 residuals:

//     A: i - parameters[0][i], for i in [0,10)  -- this is 10 residuals

//     B: parameters[0][i] - i, for i in [0,10)  -- this is another 10.

//     C: sum(parameters[0][i]^2 - 8*parameters[0][i]) + sum(parameters[1][i])

class MyCostFunctor {

 public:

  bool operator()(double const* const* parameters, double* residuals) const {

    const double* params0 = parameters[0];

    int r = 0;

    for (int i = 0; i < 10; ++i) {

      residuals[r++] = i - params0[i];

      residuals[r++] = params0[i] - i;

    }

    double c_residual = 0.0;

    for (int i = 0; i < 10; ++i) {

      c_residual += pow(params0[i], 2) - 8.0 * params0[i];

    }

    const double* params1 = parameters[1];

    for (int i = 0; i < 5; ++i) {

      c_residual += params1[i];

    }

    residuals[r++] = c_residual;

    return true;

  }

};

TEST(DynamicNumericdiffCostFunctionTest, TestResiduals) {

  vector<double> param_block_0(10, 0.0);

  vector<double> param_block_1(5, 0.0);

  DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(

      new MyCostFunctor());

  cost_function.AddParameterBlock(param_block_0.size());

  cost_function.AddParameterBlock(param_block_1.size());

  cost_function.SetNumResiduals(21);

  // Test residual computation.

  vector<double> residuals(21, -100000);

  vector<double*> parameter_blocks(2);

  parameter_blocks[0] = &param_block_0[0];

  parameter_blocks[1] = &param_block_1[0];

  EXPECT_TRUE(cost_function.Evaluate(&parameter_blocks[0],

                                     residuals.data(),

                                     NULL));

  for (int r = 0; r < 10; ++r) {

    EXPECT_EQ(1.0 * r, residuals.at(r * 2));

    EXPECT_EQ(-1.0 * r, residuals.at(r * 2 + 1));

  }

  EXPECT_EQ(0, residuals.at(20));

}

TEST(DynamicNumericdiffCostFunctionTest, TestJacobian) {

  // Test the residual counting.

  vector<double> param_block_0(10, 0.0);

  for (int i = 0; i < 10; ++i) {

    param_block_0[i] = 2 * i;

  }

  vector<double> param_block_1(5, 0.0);

  DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(

      new MyCostFunctor());

  cost_function.AddParameterBlock(param_block_0.size());

  cost_function.AddParameterBlock(param_block_1.size());

  cost_function.SetNumResiduals(21);

  // Prepare the residuals.

  vector<double> residuals(21, -100000);

  // Prepare the parameters.

  vector<double*> parameter_blocks(2);

  parameter_blocks[0] = &param_block_0[0];

  parameter_blocks[1] = &param_block_1[0];

  // Prepare the jacobian.

  vector<vector<double> > jacobian_vect(2);

  jacobian_vect[0].resize(21 * 10, -100000);

  jacobian_vect[1].resize(21 * 5, -100000);

  vector<double*> jacobian;

  jacobian.push_back(jacobian_vect[0].data());

  jacobian.push_back(jacobian_vect[1].data());

  // Test jacobian computation.

  EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(),

                                     residuals.data(),

                                     jacobian.data()));

  for (int r = 0; r < 10; ++r) {

    EXPECT_EQ(-1.0 * r, residuals.at(r * 2));

    EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));

  }

  EXPECT_EQ(420, residuals.at(20));

  for (int p = 0; p < 10; ++p) {

    // Check "A" Jacobian.

    EXPECT_NEAR(-1.0, jacobian_vect[0][2*p * 10 + p], kTolerance);

    // Check "B" Jacobian.

    EXPECT_NEAR(+1.0, jacobian_vect[0][(2*p+1) * 10 + p], kTolerance);

    jacobian_vect[0][2*p * 10 + p] = 0.0;

    jacobian_vect[0][(2*p+1) * 10 + p] = 0.0;

  }

  // Check "C" Jacobian for first parameter block.

  for (int p = 0; p < 10; ++p) {

    EXPECT_NEAR(4 * p - 8, jacobian_vect[0][20 * 10 + p], kTolerance);

    jacobian_vect[0][20 * 10 + p] = 0.0;

  }

  for (int i = 0; i < jacobian_vect[0].size(); ++i) {

    EXPECT_NEAR(0.0, jacobian_vect[0][i], kTolerance);

  }

  // Check "C" Jacobian for second parameter block.

  for (int p = 0; p < 5; ++p) {

    EXPECT_NEAR(1.0, jacobian_vect[1][20 * 5 + p], kTolerance);

    jacobian_vect[1][20 * 5 + p] = 0.0;

  }

  for (int i = 0; i < jacobian_vect[1].size(); ++i) {

    EXPECT_NEAR(0.0, jacobian_vect[1][i], kTolerance);

  }

}

TEST(DynamicNumericdiffCostFunctionTest, JacobianWithFirstParameterBlockConstant) {  // NOLINT

  // Test the residual counting.

  vector<double> param_block_0(10, 0.0);

  for (int i = 0; i < 10; ++i) {

    param_block_0[i] = 2 * i;

  }

  vector<double> param_block_1(5, 0.0);

  DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(

      new MyCostFunctor());

  cost_function.AddParameterBlock(param_block_0.size());

  cost_function.AddParameterBlock(param_block_1.size());

  cost_function.SetNumResiduals(21);

  // Prepare the residuals.

  vector<double> residuals(21, -100000);

  // Prepare the parameters.

  vector<double*> parameter_blocks(2);

  parameter_blocks[0] = &param_block_0[0];

  parameter_blocks[1] = &param_block_1[0];

  // Prepare the jacobian.

  vector<vector<double> > jacobian_vect(2);

  jacobian_vect[0].resize(21 * 10, -100000);

  jacobian_vect[1].resize(21 * 5, -100000);

  vector<double*> jacobian;

  jacobian.push_back(NULL);

  jacobian.push_back(jacobian_vect[1].data());

  // Test jacobian computation.

  EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(),

                                     residuals.data(),

                                     jacobian.data()));

  for (int r = 0; r < 10; ++r) {

    EXPECT_EQ(-1.0 * r, residuals.at(r * 2));

    EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));

  }

  EXPECT_EQ(420, residuals.at(20));

  // Check "C" Jacobian for second parameter block.

  for (int p = 0; p < 5; ++p) {

    EXPECT_NEAR(1.0, jacobian_vect[1][20 * 5 + p], kTolerance);

    jacobian_vect[1][20 * 5 + p] = 0.0;

  }

  for (int i = 0; i < jacobian_vect[1].size(); ++i) {

    EXPECT_EQ(0.0, jacobian_vect[1][i]);

  }

}

TEST(DynamicNumericdiffCostFunctionTest, JacobianWithSecondParameterBlockConstant) {  // NOLINT

  // Test the residual counting.

  vector<double> param_block_0(10, 0.0);

  for (int i = 0; i < 10; ++i) {

    param_block_0[i] = 2 * i;

  }

  vector<double> param_block_1(5, 0.0);

  DynamicNumericDiffCostFunction<MyCostFunctor> cost_function(

      new MyCostFunctor());

  cost_function.AddParameterBlock(param_block_0.size());

  cost_function.AddParameterBlock(param_block_1.size());

  cost_function.SetNumResiduals(21);

  // Prepare the residuals.

  vector<double> residuals(21, -100000);

  // Prepare the parameters.

  vector<double*> parameter_blocks(2);

  parameter_blocks[0] = &param_block_0[0];

  parameter_blocks[1] = &param_block_1[0];

  // Prepare the jacobian.

  vector<vector<double> > jacobian_vect(2);

  jacobian_vect[0].resize(21 * 10, -100000);

  jacobian_vect[1].resize(21 * 5, -100000);

  vector<double*> jacobian;

  jacobian.push_back(jacobian_vect[0].data());

  jacobian.push_back(NULL);

  // Test jacobian computation.

  EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(),

                                     residuals.data(),

                                     jacobian.data()));

  for (int r = 0; r < 10; ++r) {

    EXPECT_EQ(-1.0 * r, residuals.at(r * 2));

    EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1));

  }

  EXPECT_EQ(420, residuals.at(20));

  for (int p = 0; p < 10; ++p) {

    // Check "A" Jacobian.

    EXPECT_NEAR(-1.0, jacobian_vect[0][2*p * 10 + p], kTolerance);

    // Check "B" Jacobian.

    EXPECT_NEAR(+1.0, jacobian_vect[0][(2*p+1) * 10 + p], kTolerance);

    jacobian_vect[0][2*p * 10 + p] = 0.0;

    jacobian_vect[0][(2*p+1) * 10 + p] = 0.0;

  }

  // Check "C" Jacobian for first parameter block.

  for (int p = 0; p < 10; ++p) {

    EXPECT_NEAR(4 * p - 8, jacobian_vect[0][20 * 10 + p], kTolerance);

    jacobian_vect[0][20 * 10 + p] = 0.0;

  }

  for (int i = 0; i < jacobian_vect[0].size(); ++i) {

    EXPECT_EQ(0.0, jacobian_vect[0][i]);

  }

}

// Takes 3 parameter blocks:

//     parameters[0] (x) is size 1.

//     parameters[1] (y) is size 2.

//     parameters[2] (z) is size 3.

// Emits 7 residuals:

//     A: x[0] (= sum_x)

//     B: y[0] + 2.0 * y[1] (= sum_y)

//     C: z[0] + 3.0 * z[1] + 6.0 * z[2] (= sum_z)

//     D: sum_x * sum_y

//     E: sum_y * sum_z

//     F: sum_x * sum_z

//     G: sum_x * sum_y * sum_z

class MyThreeParameterCostFunctor {

 public:

  template <typename T>

  bool operator()(T const* const* parameters, T* residuals) const {

    const T* x = parameters[0];

    const T* y = parameters[1];

    const T* z = parameters[2];

    T sum_x = x[0];

    T sum_y = y[0] + 2.0 * y[1];

    T sum_z = z[0] + 3.0 * z[1] + 6.0 * z[2];

    residuals[0] = sum_x;

    residuals[1] = sum_y;

    residuals[2] = sum_z;

    residuals[3] = sum_x * sum_y;

    residuals[4] = sum_y * sum_z;

    residuals[5] = sum_x * sum_z;

    residuals[6] = sum_x * sum_y * sum_z;

    return true;

  }

};

class ThreeParameterCostFunctorTest : public ::testing::Test {

 protected:

  virtual void SetUp() {

    // Prepare the parameters.

    x_.resize(1);

    x_[0] = 0.0;

    y_.resize(2);

    y_[0] = 1.0;

    y_[1] = 3.0;

    z_.resize(3);

    z_[0] = 2.0;

    z_[1] = 4.0;

    z_[2] = 6.0;

    parameter_blocks_.resize(3);

    parameter_blocks_[0] = &x_[0];

    parameter_blocks_[1] = &y_[0];

    parameter_blocks_[2] = &z_[0];

    // Prepare the cost function.

    typedef DynamicNumericDiffCostFunction<MyThreeParameterCostFunctor>

      DynamicMyThreeParameterCostFunction;

    DynamicMyThreeParameterCostFunction * cost_function =

      new DynamicMyThreeParameterCostFunction(

        new MyThreeParameterCostFunctor());

    cost_function->AddParameterBlock(1);

    cost_function->AddParameterBlock(2);

    cost_function->AddParameterBlock(3);

    cost_function->SetNumResiduals(7);

    cost_function_.reset(cost_function);

    // Setup jacobian data.

    jacobian_vect_.resize(3);

    jacobian_vect_[0].resize(7 * x_.size(), -100000);

    jacobian_vect_[1].resize(7 * y_.size(), -100000);

    jacobian_vect_[2].resize(7 * z_.size(), -100000);

    // Prepare the expected residuals.

    const double sum_x = x_[0];

    const double sum_y = y_[0] + 2.0 * y_[1];

    const double sum_z = z_[0] + 3.0 * z_[1] + 6.0 * z_[2];

    expected_residuals_.resize(7);

    expected_residuals_[0] = sum_x;

    expected_residuals_[1] = sum_y;

    expected_residuals_[2] = sum_z;

    expected_residuals_[3] = sum_x * sum_y;

    expected_residuals_[4] = sum_y * sum_z;

    expected_residuals_[5] = sum_x * sum_z;

    expected_residuals_[6] = sum_x * sum_y * sum_z;

    // Prepare the expected jacobian entries.

    expected_jacobian_x_.resize(7);

    expected_jacobian_x_[0] = 1.0;

    expected_jacobian_x_[1] = 0.0;

    expected_jacobian_x_[2] = 0.0;

    expected_jacobian_x_[3] = sum_y;

    expected_jacobian_x_[4] = 0.0;

    expected_jacobian_x_[5] = sum_z;

    expected_jacobian_x_[6] = sum_y * sum_z;

    expected_jacobian_y_.resize(14);

    expected_jacobian_y_[0] = 0.0;

    expected_jacobian_y_[1] = 0.0;

    expected_jacobian_y_[2] = 1.0;

    expected_jacobian_y_[3] = 2.0;

    expected_jacobian_y_[4] = 0.0;

    expected_jacobian_y_[5] = 0.0;

    expected_jacobian_y_[6] = sum_x;

    expected_jacobian_y_[7] = 2.0 * sum_x;

    expected_jacobian_y_[8] = sum_z;

    expected_jacobian_y_[9] = 2.0 * sum_z;

    expected_jacobian_y_[10] = 0.0;

    expected_jacobian_y_[11] = 0.0;

    expected_jacobian_y_[12] = sum_x * sum_z;

    expected_jacobian_y_[13] = 2.0 * sum_x * sum_z;

    expected_jacobian_z_.resize(21);

    expected_jacobian_z_[0] = 0.0;

    expected_jacobian_z_[1] = 0.0;

    expected_jacobian_z_[2] = 0.0;

    expected_jacobian_z_[3] = 0.0;

    expected_jacobian_z_[4] = 0.0;

    expected_jacobian_z_[5] = 0.0;

    expected_jacobian_z_[6] = 1.0;

    expected_jacobian_z_[7] = 3.0;

    expected_jacobian_z_[8] = 6.0;

    expected_jacobian_z_[9] = 0.0;

    expected_jacobian_z_[10] = 0.0;

    expected_jacobian_z_[11] = 0.0;

    expected_jacobian_z_[12] = sum_y;

    expected_jacobian_z_[13] = 3.0 * sum_y;

    expected_jacobian_z_[14] = 6.0 * sum_y;

    expected_jacobian_z_[15] = sum_x;

    expected_jacobian_z_[16] = 3.0 * sum_x;

    expected_jacobian_z_[17] = 6.0 * sum_x;

    expected_jacobian_z_[18] = sum_x * sum_y;

    expected_jacobian_z_[19] = 3.0 * sum_x * sum_y;

    expected_jacobian_z_[20] = 6.0 * sum_x * sum_y;

  }

 protected:

  vector<double> x_;

  vector<double> y_;

  vector<double> z_;

  vector<double*> parameter_blocks_;

  scoped_ptr<CostFunction> cost_function_;

  vector<vector<double> > jacobian_vect_;

  vector<double> expected_residuals_;

  vector<double> expected_jacobian_x_;

  vector<double> expected_jacobian_y_;

  vector<double> expected_jacobian_z_;

};

TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterResiduals) {

  vector<double> residuals(7, -100000);

  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),

                                       residuals.data(),

                                       NULL));

  for (int i = 0; i < 7; ++i) {

    EXPECT_EQ(expected_residuals_[i], residuals[i]);

  }

}

TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterJacobian) {

  vector<double> residuals(7, -100000);

  vector<double*> jacobian;

  jacobian.push_back(jacobian_vect_[0].data());

  jacobian.push_back(jacobian_vect_[1].data());

  jacobian.push_back(jacobian_vect_[2].data());

  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),

                                       residuals.data(),

                                       jacobian.data()));

  for (int i = 0; i < 7; ++i) {

    EXPECT_EQ(expected_residuals_[i], residuals[i]);

  }

  for (int i = 0; i < 7; ++i) {

    EXPECT_NEAR(expected_jacobian_x_[i], jacobian[0][i], kTolerance);

  }

  for (int i = 0; i < 14; ++i) {

    EXPECT_NEAR(expected_jacobian_y_[i], jacobian[1][i], kTolerance);

  }

  for (int i = 0; i < 21; ++i) {

    EXPECT_NEAR(expected_jacobian_z_[i], jacobian[2][i], kTolerance);

  }

}

TEST_F(ThreeParameterCostFunctorTest,

       ThreeParameterJacobianWithFirstAndLastParameterBlockConstant) {

  vector<double> residuals(7, -100000);

  vector<double*> jacobian;

  jacobian.push_back(NULL);

  jacobian.push_back(jacobian_vect_[1].data());

  jacobian.push_back(NULL);

  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),

                                       residuals.data(),

                                       jacobian.data()));

  for (int i = 0; i < 7; ++i) {

    EXPECT_EQ(expected_residuals_[i], residuals[i]);

  }

  for (int i = 0; i < 14; ++i) {

    EXPECT_NEAR(expected_jacobian_y_[i], jacobian[1][i], kTolerance);

  }

}

TEST_F(ThreeParameterCostFunctorTest,

       ThreeParameterJacobianWithSecondParameterBlockConstant) {

  vector<double> residuals(7, -100000);

  vector<double*> jacobian;

  jacobian.push_back(jacobian_vect_[0].data());

  jacobian.push_back(NULL);

  jacobian.push_back(jacobian_vect_[2].data());

  EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(),

                                       residuals.data(),

                                       jacobian.data()));

  for (int i = 0; i < 7; ++i) {

    EXPECT_EQ(expected_residuals_[i], residuals[i]);

  }

  for (int i = 0; i < 7; ++i) {

    EXPECT_NEAR(expected_jacobian_x_[i], jacobian[0][i], kTolerance);

  }

  for (int i = 0; i < 21; ++i) {

    EXPECT_NEAR(expected_jacobian_z_[i], jacobian[2][i], kTolerance);

  }

}

}  // namespace internal

}  // namespace ceres