// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 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
// 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: thadh@gmail.com (Thad Hughes)
// mierle@gmail.com (Keir Mierle)
// sameeragarwal@google.com (Sameer Agarwal)
#include <cstddef>
#include "ceres/dynamic_autodiff_cost_function.h"
#include "ceres/internal/scoped_ptr.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
// 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:
template <typename T>
bool operator()(T const* const* parameters, T* residuals) const {
const T* params0 = parameters[0];
int r = 0;
for (int i = 0; i < 10; ++i) {
residuals[r++] = T(i) - params0[i];
residuals[r++] = params0[i] - T(i);
}
T c_residual(0.0);
for (int i = 0; i < 10; ++i) {
c_residual += pow(params0[i], 2) - T(8) * params0[i];
}
const T* params1 = parameters[1];
for (int i = 0; i < 5; ++i) {
c_residual += params1[i];
}
residuals[r++] = c_residual;
return true;
}
};
TEST(DynamicAutodiffCostFunctionTest, TestResiduals) {
vector<double> param_block_0(10, 0.0);
vector<double> param_block_1(5, 0.0);
DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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] = ¶m_block_0[0];
parameter_blocks[1] = ¶m_block_1[0];
EXPECT_TRUE(cost_function.Evaluate(¶meter_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(DynamicAutodiffCostFunctionTest, 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);
DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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] = ¶m_block_0[0];
parameter_blocks[1] = ¶m_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_EQ(-1.0, jacobian_vect[0][2*p * 10 + p]);
// Check "B" Jacobian.
EXPECT_EQ(+1.0, jacobian_vect[0][(2*p+1) * 10 + p]);
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_EQ(4 * p - 8, jacobian_vect[0][20 * 10 + p]);
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]);
}
// Check "C" Jacobian for second parameter block.
for (int p = 0; p < 5; ++p) {
EXPECT_EQ(1.0, jacobian_vect[1][20 * 5 + p]);
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(DynamicAutodiffCostFunctionTest, JacobianWithFirstParameterBlockConstant) {
// 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);
DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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] = ¶m_block_0[0];
parameter_blocks[1] = ¶m_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_EQ(1.0, jacobian_vect[1][20 * 5 + p]);
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(DynamicAutodiffCostFunctionTest, JacobianWithSecondParameterBlockConstant) {
// 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);
DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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] = ¶m_block_0[0];
parameter_blocks[1] = ¶m_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_EQ(-1.0, jacobian_vect[0][2*p * 10 + p]);
// Check "B" Jacobian.
EXPECT_EQ(+1.0, jacobian_vect[0][(2*p+1) * 10 + p]);
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_EQ(4 * p - 8, jacobian_vect[0][20 * 10 + p]);
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 DynamicAutoDiffCostFunction<MyThreeParameterCostFunctor, 3>
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_EQ(expected_jacobian_x_[i], jacobian[0][i]);
}
for (int i = 0; i < 14; ++i) {
EXPECT_EQ(expected_jacobian_y_[i], jacobian[1][i]);
}
for (int i = 0; i < 21; ++i) {
EXPECT_EQ(expected_jacobian_z_[i], jacobian[2][i]);
}
}
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_EQ(expected_jacobian_y_[i], jacobian[1][i]);
}
}
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_EQ(expected_jacobian_x_[i], jacobian[0][i]);
}
for (int i = 0; i < 21; ++i) {
EXPECT_EQ(expected_jacobian_z_[i], jacobian[2][i]);
}
}
// Takes 6 parameter blocks all of size 1:
// x0, y0, y1, z0, z1, z2
// Same 7 residuals as MyThreeParameterCostFunctor.
// Naming convention for tests is (V)ariable and (C)onstant.
class MySixParameterCostFunctor {
public:
template <typename T>
bool operator()(T const* const* parameters, T* residuals) const {
const T* x0 = parameters[0];
const T* y0 = parameters[1];
const T* y1 = parameters[2];
const T* z0 = parameters[3];
const T* z1 = parameters[4];
const T* z2 = parameters[5];
T sum_x = x0[0];
T sum_y = y0[0] + 2.0 * y1[0];
T sum_z = z0[0] + 3.0 * z1[0] + 6.0 * z2[0];
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 SixParameterCostFunctorTest : public ::testing::Test {
protected:
virtual void SetUp() {
// Prepare the parameters.
x0_ = 0.0;
y0_ = 1.0;
y1_ = 3.0;
z0_ = 2.0;
z1_ = 4.0;
z2_ = 6.0;
parameter_blocks_.resize(6);
parameter_blocks_[0] = &x0_;
parameter_blocks_[1] = &y0_;
parameter_blocks_[2] = &y1_;
parameter_blocks_[3] = &z0_;
parameter_blocks_[4] = &z1_;
parameter_blocks_[5] = &z2_;
// Prepare the cost function.
typedef DynamicAutoDiffCostFunction<MySixParameterCostFunctor, 3>
DynamicMySixParameterCostFunction;
DynamicMySixParameterCostFunction * cost_function =
new DynamicMySixParameterCostFunction(
new MySixParameterCostFunctor());
for (int i = 0; i < 6; ++i) {
cost_function->AddParameterBlock(1);
}
cost_function->SetNumResiduals(7);
cost_function_.reset(cost_function);
// Setup jacobian data.
jacobian_vect_.resize(6);
for (int i = 0; i < 6; ++i) {
jacobian_vect_[i].resize(7, -100000);
}
// Prepare the expected residuals.
const double sum_x = x0_;
const double sum_y = y0_ + 2.0 * y1_;
const double sum_z = z0_ + 3.0 * z1_ + 6.0 * z2_;
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_jacobians_.resize(6);
expected_jacobians_[0].resize(7);
expected_jacobians_[0][0] = 1.0;
expected_jacobians_[0][1] = 0.0;
expected_jacobians_[0][2] = 0.0;
expected_jacobians_[0][3] = sum_y;
expected_jacobians_[0][4] = 0.0;
expected_jacobians_[0][5] = sum_z;
expected_jacobians_[0][6] = sum_y * sum_z;
expected_jacobians_[1].resize(7);
expected_jacobians_[1][0] = 0.0;
expected_jacobians_[1][1] = 1.0;
expected_jacobians_[1][2] = 0.0;
expected_jacobians_[1][3] = sum_x;
expected_jacobians_[1][4] = sum_z;
expected_jacobians_[1][5] = 0.0;
expected_jacobians_[1][6] = sum_x * sum_z;
expected_jacobians_[2].resize(7);
expected_jacobians_[2][0] = 0.0;
expected_jacobians_[2][1] = 2.0;
expected_jacobians_[2][2] = 0.0;
expected_jacobians_[2][3] = 2.0 * sum_x;
expected_jacobians_[2][4] = 2.0 * sum_z;
expected_jacobians_[2][5] = 0.0;
expected_jacobians_[2][6] = 2.0 * sum_x * sum_z;
expected_jacobians_[3].resize(7);
expected_jacobians_[3][0] = 0.0;
expected_jacobians_[3][1] = 0.0;
expected_jacobians_[3][2] = 1.0;
expected_jacobians_[3][3] = 0.0;
expected_jacobians_[3][4] = sum_y;
expected_jacobians_[3][5] = sum_x;
expected_jacobians_[3][6] = sum_x * sum_y;
expected_jacobians_[4].resize(7);
expected_jacobians_[4][0] = 0.0;
expected_jacobians_[4][1] = 0.0;
expected_jacobians_[4][2] = 3.0;
expected_jacobians_[4][3] = 0.0;
expected_jacobians_[4][4] = 3.0 * sum_y;
expected_jacobians_[4][5] = 3.0 * sum_x;
expected_jacobians_[4][6] = 3.0 * sum_x * sum_y;
expected_jacobians_[5].resize(7);
expected_jacobians_[5][0] = 0.0;
expected_jacobians_[5][1] = 0.0;
expected_jacobians_[5][2] = 6.0;
expected_jacobians_[5][3] = 0.0;
expected_jacobians_[5][4] = 6.0 * sum_y;
expected_jacobians_[5][5] = 6.0 * sum_x;
expected_jacobians_[5][6] = 6.0 * sum_x * sum_y;
}
protected:
double x0_;
double y0_;
double y1_;
double z0_;
double z1_;
double z2_;
vector<double*> parameter_blocks_;
scoped_ptr<CostFunction> cost_function_;
vector<vector<double> > jacobian_vect_;
vector<double> expected_residuals_;
vector<vector<double> > expected_jacobians_;
};
TEST_F(SixParameterCostFunctorTest, TestSixParameterResiduals) {
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(SixParameterCostFunctorTest, TestSixParameterJacobian) {
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());
jacobian.push_back(jacobian_vect_[3].data());
jacobian.push_back(jacobian_vect_[4].data());
jacobian.push_back(jacobian_vect_[5].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 < 6; ++i) {
for (int j = 0; j < 7; ++j) {
EXPECT_EQ(expected_jacobians_[i][j], jacobian[i][j]);
}
}
}
TEST_F(SixParameterCostFunctorTest, TestSixParameterJacobianVVCVVC) {
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(NULL);
jacobian.push_back(jacobian_vect_[3].data());
jacobian.push_back(jacobian_vect_[4].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 < 6; ++i) {
// Skip the constant variables.
if (i == 2 || i == 5) {
continue;
}
for (int j = 0; j < 7; ++j) {
EXPECT_EQ(expected_jacobians_[i][j], jacobian[i][j]);
}
}
}
TEST_F(SixParameterCostFunctorTest, TestSixParameterJacobianVCCVCV) {
vector<double> residuals(7, -100000);
vector<double*> jacobian;
jacobian.push_back(jacobian_vect_[0].data());
jacobian.push_back(NULL);
jacobian.push_back(NULL);
jacobian.push_back(jacobian_vect_[3].data());
jacobian.push_back(NULL);
jacobian.push_back(jacobian_vect_[5].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 < 6; ++i) {
// Skip the constant variables.
if (i == 1 || i == 2 || i == 4) {
continue;
}
for (int j = 0; j < 7; ++j) {
EXPECT_EQ(expected_jacobians_[i][j], jacobian[i][j]);
}
}
}
} // namespace internal
} // namespace ceres