| #include "ceres/numeric_diff_test_utils.h" |
| |
| #include <algorithm> |
| #include <cmath> |
| #include "ceres/cost_function.h" |
| #include "ceres/internal/macros.h" |
| #include "ceres/test_util.h" |
| #include "ceres/types.h" |
| #include "gtest/gtest.h" |
| |
| |
| namespace ceres { |
| namespace internal { |
| |
| bool EasyFunctor::operator()(const double* x1, |
| const double* x2, |
| double* residuals) const { |
| residuals[0] = residuals[1] = residuals[2] = 0; |
| for (int i = 0; i < 5; ++i) { |
| residuals[0] += x1[i] * x2[i]; |
| residuals[2] += x2[i] * x2[i]; |
| } |
| residuals[1] = residuals[0] * residuals[0]; |
| return true; |
| } |
| |
| void EasyFunctor::ExpectCostFunctionEvaluationIsNearlyCorrect( |
| const CostFunction& cost_function, |
| NumericDiffMethod method) const { |
| double x1[] = { 1.0, 2.0, 3.0, 4.0, 5.0 }; |
| double x2[] = { 9.0, 9.0, 5.0, 5.0, 1.0 }; |
| double *parameters[] = { &x1[0], &x2[0] }; |
| |
| double dydx1[15]; // 3 x 5, row major. |
| double dydx2[15]; // 3 x 5, row major. |
| double *jacobians[2] = { &dydx1[0], &dydx2[0] }; |
| |
| double residuals[3] = {-1e-100, -2e-100, -3e-100 }; |
| |
| ASSERT_TRUE(cost_function.Evaluate(¶meters[0], |
| &residuals[0], |
| &jacobians[0])); |
| |
| EXPECT_EQ(residuals[0], 67); |
| EXPECT_EQ(residuals[1], 4489); |
| EXPECT_EQ(residuals[2], 213); |
| |
| const double tolerance = (method == CENTRAL)? 3e-9 : 2e-5; |
| |
| for (int i = 0; i < 5; ++i) { |
| ExpectClose(x2[i], dydx1[5 * 0 + i], tolerance); // y1 |
| ExpectClose(x1[i], dydx2[5 * 0 + i], tolerance); |
| ExpectClose(2 * x2[i] * residuals[0], dydx1[5 * 1 + i], tolerance); // y2 |
| ExpectClose(2 * x1[i] * residuals[0], dydx2[5 * 1 + i], tolerance); |
| ExpectClose(0.0, dydx1[5 * 2 + i], tolerance); // y3 |
| ExpectClose(2 * x2[i], dydx2[5 * 2 + i], tolerance); |
| } |
| } |
| |
| bool TranscendentalFunctor::operator()(const double* x1, |
| const double* x2, |
| double* residuals) const { |
| double x1x2 = 0; |
| for (int i = 0; i < 5; ++i) { |
| x1x2 += x1[i] * x2[i]; |
| } |
| residuals[0] = sin(x1x2); |
| residuals[1] = exp(-x1x2 / 10); |
| return true; |
| } |
| |
| void TranscendentalFunctor::ExpectCostFunctionEvaluationIsNearlyCorrect( |
| const CostFunction& cost_function, |
| NumericDiffMethod method) const { |
| struct { |
| double x1[5]; |
| double x2[5]; |
| } kTests[] = { |
| { { 1.0, 2.0, 3.0, 4.0, 5.0 }, // No zeros. |
| { 9.0, 9.0, 5.0, 5.0, 1.0 }, |
| }, |
| { { 0.0, 2.0, 3.0, 0.0, 5.0 }, // Some zeros x1. |
| { 9.0, 9.0, 5.0, 5.0, 1.0 }, |
| }, |
| { { 1.0, 2.0, 3.0, 1.0, 5.0 }, // Some zeros x2. |
| { 0.0, 9.0, 0.0, 5.0, 0.0 }, |
| }, |
| { { 0.0, 0.0, 0.0, 0.0, 0.0 }, // All zeros x1. |
| { 9.0, 9.0, 5.0, 5.0, 1.0 }, |
| }, |
| { { 1.0, 2.0, 3.0, 4.0, 5.0 }, // All zeros x2. |
| { 0.0, 0.0, 0.0, 0.0, 0.0 }, |
| }, |
| { { 0.0, 0.0, 0.0, 0.0, 0.0 }, // All zeros. |
| { 0.0, 0.0, 0.0, 0.0, 0.0 }, |
| }, |
| }; |
| |
| for (int k = 0; k < CERES_ARRAYSIZE(kTests); ++k) { |
| double *x1 = &(kTests[k].x1[0]); |
| double *x2 = &(kTests[k].x2[0]); |
| double *parameters[] = { x1, x2 }; |
| |
| double dydx1[10]; |
| double dydx2[10]; |
| double *jacobians[2] = { &dydx1[0], &dydx2[0] }; |
| |
| double residuals[2]; |
| |
| ASSERT_TRUE(cost_function.Evaluate(¶meters[0], |
| &residuals[0], |
| &jacobians[0])); |
| double x1x2 = 0; |
| for (int i = 0; i < 5; ++i) { |
| x1x2 += x1[i] * x2[i]; |
| } |
| |
| const double tolerance = (method == CENTRAL)? 3e-9 : 2e-5; |
| |
| for (int i = 0; i < 5; ++i) { |
| ExpectClose( x2[i] * cos(x1x2), dydx1[5 * 0 + i], tolerance); |
| ExpectClose( x1[i] * cos(x1x2), dydx2[5 * 0 + i], tolerance); |
| ExpectClose(-x2[i] * exp(-x1x2 / 10.) / 10., dydx1[5 * 1 + i], tolerance); |
| ExpectClose(-x1[i] * exp(-x1x2 / 10.) / 10., dydx2[5 * 1 + i], tolerance); |
| } |
| } |
| } |
| |
| } // namespace internal |
| } // namespace ceres |