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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2021 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
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// this list of conditions and the following disclaimer in the documentation
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/numeric_diff_first_order_function.h"
#include <memory>
#include "ceres/array_utils.h"
#include "ceres/first_order_function.h"
#include "gtest/gtest.h"
namespace ceres::internal {
class QuadraticCostFunctor {
public:
explicit QuadraticCostFunctor(double a) : a_(a) {}
bool operator()(const double* const x, double* cost) const {
cost[0] = x[0] * x[1] + x[2] * x[3] - a_;
return true;
}
private:
double a_;
};
TEST(NumericDiffFirstOrderFunction, BilinearDifferentiationTestStatic) {
auto function = std::make_unique<
NumericDiffFirstOrderFunction<QuadraticCostFunctor, CENTRAL, 4>>(
new QuadraticCostFunctor(1.0));
double parameters[4] = {1.0, 2.0, 3.0, 4.0};
double gradient[4];
double cost;
function->Evaluate(parameters, &cost, nullptr);
EXPECT_EQ(cost, 13.0);
cost = -1.0;
function->Evaluate(parameters, &cost, gradient);
EXPECT_EQ(cost, 13.0);
const double kTolerance = 1e-9;
EXPECT_NEAR(gradient[0], parameters[1], kTolerance);
EXPECT_NEAR(gradient[1], parameters[0], kTolerance);
EXPECT_NEAR(gradient[2], parameters[3], kTolerance);
EXPECT_NEAR(gradient[3], parameters[2], kTolerance);
}
TEST(NumericDiffFirstOrderFunction, BilinearDifferentiationTestDynamic) {
auto function = std::make_unique<
NumericDiffFirstOrderFunction<QuadraticCostFunctor, CENTRAL>>(
new QuadraticCostFunctor(1.0), 4);
double parameters[4] = {1.0, 2.0, 3.0, 4.0};
double gradient[4];
double cost;
function->Evaluate(parameters, &cost, nullptr);
EXPECT_EQ(cost, 13.0);
cost = -1.0;
function->Evaluate(parameters, &cost, gradient);
EXPECT_EQ(cost, 13.0);
const double kTolerance = 1e-9;
EXPECT_NEAR(gradient[0], parameters[1], kTolerance);
EXPECT_NEAR(gradient[1], parameters[0], kTolerance);
EXPECT_NEAR(gradient[2], parameters[3], kTolerance);
EXPECT_NEAR(gradient[3], parameters[2], kTolerance);
}
} // namespace ceres::internal