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// 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: wjr@google.com (William Rucklidge)
//
// Tests for the conditioned cost function.
#include "ceres/conditioned_cost_function.h"
#include "ceres/internal/eigen.h"
#include "ceres/normal_prior.h"
#include "ceres/types.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
// The size of the cost functions we build.
static constexpr int kTestCostFunctionSize = 3;
// A simple cost function: return ax + b.
class LinearCostFunction : public CostFunction {
public:
LinearCostFunction(double a, double b) : a_(a), b_(b) {
set_num_residuals(1);
mutable_parameter_block_sizes()->push_back(1);
}
bool Evaluate(double const* const* parameters,
double* residuals,
double** jacobians) const final {
*residuals = **parameters * a_ + b_;
if (jacobians && *jacobians) {
**jacobians = a_;
}
return true;
}
private:
const double a_, b_;
};
// Tests that ConditionedCostFunction does what it's supposed to.
TEST(ConditionedCostFunction, NormalOperation) {
double v1[kTestCostFunctionSize], v2[kTestCostFunctionSize],
jac[kTestCostFunctionSize * kTestCostFunctionSize],
result[kTestCostFunctionSize];
for (int i = 0; i < kTestCostFunctionSize; i++) {
v1[i] = i;
v2[i] = i * 10;
// Seed a few garbage values in the Jacobian matrix, to make sure that
// they're overwritten.
jac[i * 2] = i * i;
result[i] = i * i * i;
}
// Make a cost function that computes x - v2
VectorRef v2_vector(v2, kTestCostFunctionSize, 1);
Matrix identity(kTestCostFunctionSize, kTestCostFunctionSize);
identity.setIdentity();
NormalPrior* difference_cost_function = new NormalPrior(identity, v2_vector);
std::vector<CostFunction*> conditioners;
for (int i = 0; i < kTestCostFunctionSize; i++) {
conditioners.push_back(new LinearCostFunction(i + 2, i * 7));
}
ConditionedCostFunction conditioned_cost_function(
difference_cost_function, conditioners, TAKE_OWNERSHIP);
EXPECT_EQ(difference_cost_function->num_residuals(),
conditioned_cost_function.num_residuals());
EXPECT_EQ(difference_cost_function->parameter_block_sizes(),
conditioned_cost_function.parameter_block_sizes());
double* parameters[1];
parameters[0] = v1;
double* jacs[1];
jacs[0] = jac;
conditioned_cost_function.Evaluate(parameters, result, jacs);
for (int i = 0; i < kTestCostFunctionSize; i++) {
EXPECT_DOUBLE_EQ((i + 2) * (v1[i] - v2[i]) + i * 7, result[i]);
}
for (int i = 0; i < kTestCostFunctionSize; i++) {
for (int j = 0; j < kTestCostFunctionSize; j++) {
double actual = jac[i * kTestCostFunctionSize + j];
if (i != j) {
EXPECT_DOUBLE_EQ(0, actual);
} else {
EXPECT_DOUBLE_EQ(i + 2, actual);
}
}
}
}
TEST(ConditionedCostFunction, SharedConditionersDoNotTriggerDoubleFree) {
// Make a cost function that computes x - v2
double v2[kTestCostFunctionSize];
VectorRef v2_vector(v2, kTestCostFunctionSize, 1);
Matrix identity =
Matrix::Identity(kTestCostFunctionSize, kTestCostFunctionSize);
NormalPrior* difference_cost_function = new NormalPrior(identity, v2_vector);
CostFunction* conditioner = new LinearCostFunction(2, 7);
std::vector<CostFunction*> conditioners;
for (int i = 0; i < kTestCostFunctionSize; i++) {
conditioners.push_back(conditioner);
}
ConditionedCostFunction conditioned_cost_function(
difference_cost_function, conditioners, TAKE_OWNERSHIP);
}
} // namespace internal
} // namespace ceres