Lint changes from Jim Roseborough.
Change-Id: Ia92ed4dcd6750f33a178ff39465814805a99bdfa
diff --git a/internal/ceres/evaluator_test.cc b/internal/ceres/evaluator_test.cc
index a0b28ee..a156b89 100644
--- a/internal/ceres/evaluator_test.cc
+++ b/internal/ceres/evaluator_test.cc
@@ -61,7 +61,8 @@
typedef SizedCostFunction<kNumResiduals, Ns...> Base;
public:
- ParameterIgnoringCostFunction(bool succeeds = true) : succeeds_(succeeds) {}
+ explicit ParameterIgnoringCostFunction(bool succeeds = true)
+ : succeeds_(succeeds) {}
virtual bool Evaluate(double const* const* parameters,
double* residuals,
@@ -83,7 +84,7 @@
//
// where the multiplication by kFactor makes it easier to distinguish
// between Jacobians of different residuals for the same parameter.
- if (jacobians[k] != NULL) {
+ if (jacobians[k] != nullptr) {
MatrixRef jacobian(jacobians[k],
Base::num_residuals(),
Base::parameter_block_sizes()[k]);
@@ -173,12 +174,12 @@
ASSERT_TRUE(evaluator->Evaluate(
&state[0],
&cost,
- expected_residuals != NULL ? &residuals[0] : NULL,
- expected_gradient != NULL ? &gradient[0] : NULL,
- expected_jacobian != NULL ? jacobian.get() : NULL));
+ expected_residuals != nullptr ? &residuals[0] : nullptr,
+ expected_gradient != nullptr ? &gradient[0] : nullptr,
+ expected_jacobian != nullptr ? jacobian.get() : nullptr));
Matrix actual_jacobian;
- if (expected_jacobian != NULL) {
+ if (expected_jacobian != nullptr) {
jacobian->ToDenseMatrix(&actual_jacobian);
}
@@ -201,9 +202,9 @@
expected.num_rows,
expected.num_cols,
expected.cost,
- (i & 1) ? expected.residuals : NULL,
- (i & 2) ? expected.gradient : NULL,
- (i & 4) ? expected.jacobian : NULL);
+ (i & 1) ? expected.residuals : nullptr,
+ (i & 2) ? expected.gradient : nullptr,
+ (i & 4) ? expected.jacobian : nullptr);
}
}
@@ -222,7 +223,7 @@
TEST_P(EvaluatorTest, SingleResidualProblem) {
problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 2, 3, 4>,
- NULL,
+ nullptr,
x, y, z);
ExpectedEvaluation expected = {
@@ -260,7 +261,7 @@
// for a long time, since by chance most users added parameters to the problem
// in the same order that they occurred as parameters to a cost function.
problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 4, 3, 2>,
- NULL,
+ nullptr,
z, y, x);
ExpectedEvaluation expected = {
@@ -303,7 +304,7 @@
problem.AddParameterBlock(d, 3);
problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 3, 2, 3, 4>,
- NULL,
+ nullptr,
x, y, z);
ExpectedEvaluation expected = {
@@ -340,17 +341,17 @@
// f(x, y) in R^2
problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>,
- NULL,
+ nullptr,
x, y);
// g(x, z) in R^3
problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>,
- NULL,
+ nullptr,
x, z);
// h(y, z) in R^4
problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>,
- NULL,
+ nullptr,
y, z);
ExpectedEvaluation expected = {
@@ -403,17 +404,17 @@
// f(x, y) in R^2
problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>,
- NULL,
+ nullptr,
x, y);
// g(x, z) in R^3
problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>,
- NULL,
+ nullptr,
x, z);
// h(y, z) in R^4
problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>,
- NULL,
+ nullptr,
y, z);
ExpectedEvaluation expected = {
@@ -463,17 +464,17 @@
// f(x, y) in R^2
problem.AddResidualBlock(new ParameterIgnoringCostFunction<1, 2, 2, 3>,
- NULL,
- x, y);
+ nullptr,
+ x, y);
// g(x, z) in R^3
problem.AddResidualBlock(new ParameterIgnoringCostFunction<2, 3, 2, 4>,
- NULL,
- x, z);
+ nullptr,
+ x, z);
// h(y, z) in R^4
problem.AddResidualBlock(new ParameterIgnoringCostFunction<3, 4, 3, 4>,
- NULL,
+ nullptr,
y, z);
// For this test, "z" is constant.
@@ -531,15 +532,20 @@
TEST_P(EvaluatorTest, EvaluatorAbortsForResidualsThatFailToEvaluate) {
// Switch the return value to failure.
problem.AddResidualBlock(
- new ParameterIgnoringCostFunction<20, 3, 2, 3, 4>(false), NULL, x, y, z);
+ new ParameterIgnoringCostFunction<20, 3, 2, 3, 4>(false),
+ nullptr,
+ x,
+ y,
+ z);
// The values are ignored.
double state[9];
- std::unique_ptr<Evaluator> evaluator(CreateEvaluator(problem.mutable_program()));
+ std::unique_ptr<Evaluator> evaluator(
+ CreateEvaluator(problem.mutable_program()));
std::unique_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian());
double cost;
- EXPECT_FALSE(evaluator->Evaluate(state, &cost, NULL, NULL, NULL));
+ EXPECT_FALSE(evaluator->Evaluate(state, &cost, nullptr, nullptr, nullptr));
}
// In the pairs, the first argument is the linear solver type, and the second
@@ -551,25 +557,24 @@
INSTANTIATE_TEST_CASE_P(
LinearSolvers,
EvaluatorTest,
- ::testing::Values(
- EvaluatorTestOptions(DENSE_QR, 0),
- EvaluatorTestOptions(DENSE_SCHUR, 0),
- EvaluatorTestOptions(DENSE_SCHUR, 1),
- EvaluatorTestOptions(DENSE_SCHUR, 2),
- EvaluatorTestOptions(DENSE_SCHUR, 3),
- EvaluatorTestOptions(DENSE_SCHUR, 4),
- EvaluatorTestOptions(SPARSE_SCHUR, 0),
- EvaluatorTestOptions(SPARSE_SCHUR, 1),
- EvaluatorTestOptions(SPARSE_SCHUR, 2),
- EvaluatorTestOptions(SPARSE_SCHUR, 3),
- EvaluatorTestOptions(SPARSE_SCHUR, 4),
- EvaluatorTestOptions(ITERATIVE_SCHUR, 0),
- EvaluatorTestOptions(ITERATIVE_SCHUR, 1),
- EvaluatorTestOptions(ITERATIVE_SCHUR, 2),
- EvaluatorTestOptions(ITERATIVE_SCHUR, 3),
- EvaluatorTestOptions(ITERATIVE_SCHUR, 4),
- EvaluatorTestOptions(SPARSE_NORMAL_CHOLESKY, 0, false),
- EvaluatorTestOptions(SPARSE_NORMAL_CHOLESKY, 0, true)));
+ ::testing::Values(EvaluatorTestOptions(DENSE_QR, 0),
+ EvaluatorTestOptions(DENSE_SCHUR, 0),
+ EvaluatorTestOptions(DENSE_SCHUR, 1),
+ EvaluatorTestOptions(DENSE_SCHUR, 2),
+ EvaluatorTestOptions(DENSE_SCHUR, 3),
+ EvaluatorTestOptions(DENSE_SCHUR, 4),
+ EvaluatorTestOptions(SPARSE_SCHUR, 0),
+ EvaluatorTestOptions(SPARSE_SCHUR, 1),
+ EvaluatorTestOptions(SPARSE_SCHUR, 2),
+ EvaluatorTestOptions(SPARSE_SCHUR, 3),
+ EvaluatorTestOptions(SPARSE_SCHUR, 4),
+ EvaluatorTestOptions(ITERATIVE_SCHUR, 0),
+ EvaluatorTestOptions(ITERATIVE_SCHUR, 1),
+ EvaluatorTestOptions(ITERATIVE_SCHUR, 2),
+ EvaluatorTestOptions(ITERATIVE_SCHUR, 3),
+ EvaluatorTestOptions(ITERATIVE_SCHUR, 4),
+ EvaluatorTestOptions(SPARSE_NORMAL_CHOLESKY, 0, false),
+ EvaluatorTestOptions(SPARSE_NORMAL_CHOLESKY, 0, true)));
// Simple cost function used to check if the evaluator is sensitive to
// state changes.
@@ -583,9 +588,9 @@
residuals[0] = x1 * x1;
residuals[1] = x2 * x2;
- if (jacobians != NULL) {
+ if (jacobians != nullptr) {
double* jacobian = jacobians[0];
- if (jacobian != NULL) {
+ if (jacobian != nullptr) {
jacobian[0] = 2.0 * x1;
jacobian[1] = 0.0;
jacobian[2] = 0.0;
@@ -603,7 +608,7 @@
x[0] = 1.0;
x[1] = 1.0;
- problem.AddResidualBlock(new ParameterSensitiveCostFunction(), NULL, x);
+ problem.AddResidualBlock(new ParameterSensitiveCostFunction(), nullptr, x);
Program* program = problem.mutable_program();
program->SetParameterOffsetsAndIndex();
@@ -612,7 +617,8 @@
options.num_eliminate_blocks = 0;
options.context = problem.context();
string error;
- std::unique_ptr<Evaluator> evaluator(Evaluator::Create(options, program, &error));
+ std::unique_ptr<Evaluator> evaluator(
+ Evaluator::Create(options, program, &error));
std::unique_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian());
ASSERT_EQ(2, jacobian->num_rows());
@@ -630,15 +636,15 @@
// Cost only; no residuals and no jacobian.
{
double cost = -1;
- ASSERT_TRUE(evaluator->Evaluate(state, &cost, NULL, NULL, NULL));
+ ASSERT_TRUE(evaluator->Evaluate(state, &cost, nullptr, nullptr, nullptr));
EXPECT_EQ(48.5, cost);
}
// Cost and residuals, no jacobian.
{
double cost = -1;
- double residuals[2] = { -2, -2 };
- ASSERT_TRUE(evaluator->Evaluate(state, &cost, residuals, NULL, NULL));
+ double residuals[2] = {-2, -2};
+ ASSERT_TRUE(evaluator->Evaluate(state, &cost, residuals, nullptr, nullptr));
EXPECT_EQ(48.5, cost);
EXPECT_EQ(4, residuals[0]);
EXPECT_EQ(9, residuals[1]);
@@ -647,13 +653,10 @@
// Cost, residuals, and jacobian.
{
double cost = -1;
- double residuals[2] = { -2, -2};
+ double residuals[2] = {-2, -2};
SetSparseMatrixConstant(jacobian.get(), -1);
- ASSERT_TRUE(evaluator->Evaluate(state,
- &cost,
- residuals,
- NULL,
- jacobian.get()));
+ ASSERT_TRUE(
+ evaluator->Evaluate(state, &cost, residuals, nullptr, jacobian.get()));
EXPECT_EQ(48.5, cost);
EXPECT_EQ(4, residuals[0]);
EXPECT_EQ(9, residuals[1]);
@@ -661,13 +664,12 @@
jacobian->ToDenseMatrix(&actual_jacobian);
Matrix expected_jacobian(2, 2);
- expected_jacobian
- << 2 * state[0], 0,
- 0, 2 * state[1];
+ expected_jacobian << 2 * state[0], 0, 0, 2 * state[1];
EXPECT_TRUE((actual_jacobian.array() == expected_jacobian.array()).all())
- << "Actual:\n" << actual_jacobian
- << "\nExpected:\n" << expected_jacobian;
+ << "Actual:\n"
+ << actual_jacobian << "\nExpected:\n"
+ << expected_jacobian;
}
}
diff --git a/internal/ceres/program_test.cc b/internal/ceres/program_test.cc
index 01bf233..6cb316e 100644
--- a/internal/ceres/program_test.cc
+++ b/internal/ceres/program_test.cc
@@ -55,7 +55,7 @@
double* residuals,
double** jacobians) const {
residuals[0] = parameters[0][0];
- if (jacobians != NULL && jacobians[0] != NULL) {
+ if (jacobians != nullptr && jacobians[0] != nullptr) {
jacobians[0][0] = 1.0;
}
return true;
@@ -91,15 +91,14 @@
problem.AddParameterBlock(&x, 1);
problem.AddParameterBlock(&y, 1);
problem.AddParameterBlock(&z, 1);
- problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
- problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
- problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z);
+ problem.AddResidualBlock(new UnaryCostFunction(), nullptr, &x);
+ problem.AddResidualBlock(new BinaryCostFunction(), nullptr, &x, &y);
+ problem.AddResidualBlock(new TernaryCostFunction(), nullptr, &x, &y, &z);
vector<double*> removed_parameter_blocks;
double fixed_cost = 0.0;
string message;
- std::unique_ptr<Program> reduced_program(
- problem.program().CreateReducedProgram(
+ std::unique_ptr<Program> reduced_program(problem.program().CreateReducedProgram(
&removed_parameter_blocks, &fixed_cost, &message));
EXPECT_EQ(reduced_program->NumParameterBlocks(), 3);
@@ -113,7 +112,7 @@
double x = 1.0;
problem.AddParameterBlock(&x, 1);
- problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
+ problem.AddResidualBlock(new UnaryCostFunction(), nullptr, &x);
problem.SetParameterBlockConstant(&x);
vector<double*> removed_parameter_blocks;
@@ -163,8 +162,8 @@
problem.AddParameterBlock(&y, 1);
problem.AddParameterBlock(&z, 1);
- problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
- problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
+ problem.AddResidualBlock(new UnaryCostFunction(), nullptr, &x);
+ problem.AddResidualBlock(new BinaryCostFunction(), nullptr, &x, &y);
problem.SetParameterBlockConstant(&x);
vector<double*> removed_parameter_blocks;
@@ -186,9 +185,9 @@
problem.AddParameterBlock(&x, 1);
problem.AddParameterBlock(&y, 1);
problem.AddParameterBlock(&z, 1);
- problem.AddResidualBlock(new UnaryCostFunction(), NULL, &x);
- problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z);
- problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
+ problem.AddResidualBlock(new UnaryCostFunction(), nullptr, &x);
+ problem.AddResidualBlock(new TernaryCostFunction(), nullptr, &x, &y, &z);
+ problem.AddResidualBlock(new BinaryCostFunction(), nullptr, &x, &y);
problem.SetParameterBlockConstant(&x);
vector<double*> removed_parameter_blocks;
@@ -210,9 +209,9 @@
problem.AddParameterBlock(&x, 1);
problem.AddParameterBlock(&y, 1);
problem.AddParameterBlock(&z, 1);
- problem.AddResidualBlock(new UnaryIdentityCostFunction(), NULL, &x);
- problem.AddResidualBlock(new TernaryCostFunction(), NULL, &x, &y, &z);
- problem.AddResidualBlock(new BinaryCostFunction(), NULL, &x, &y);
+ problem.AddResidualBlock(new UnaryIdentityCostFunction(), nullptr, &x);
+ problem.AddResidualBlock(new TernaryCostFunction(), nullptr, &x, &y, &z);
+ problem.AddResidualBlock(new BinaryCostFunction(), nullptr, &x, &y);
problem.SetParameterBlockConstant(&x);
ResidualBlock *expected_removed_block =
@@ -222,8 +221,8 @@
double expected_fixed_cost;
expected_removed_block->Evaluate(true,
&expected_fixed_cost,
- NULL,
- NULL,
+ nullptr,
+ nullptr,
scratch.get());
@@ -249,14 +248,14 @@
problem.AddParameterBlock(y, 3);
problem.AddParameterBlock(&z, 1);
- problem.AddResidualBlock(new MockCostFunctionBase<2, 2>(), NULL, x);
- problem.AddResidualBlock(new MockCostFunctionBase<3, 1, 2>(), NULL, &z, x);
- problem.AddResidualBlock(new MockCostFunctionBase<4, 1, 3>(), NULL, &z, y);
- problem.AddResidualBlock(new MockCostFunctionBase<5, 1, 3>(), NULL, &z, y);
- problem.AddResidualBlock(new MockCostFunctionBase<1, 2, 1>(), NULL, x, &z);
- problem.AddResidualBlock(new MockCostFunctionBase<2, 1, 3>(), NULL, &z, y);
- problem.AddResidualBlock(new MockCostFunctionBase<2, 2, 1>(), NULL, x, &z);
- problem.AddResidualBlock(new MockCostFunctionBase<1, 3>(), NULL, y);
+ problem.AddResidualBlock(new MockCostFunctionBase<2, 2>(), nullptr, x);
+ problem.AddResidualBlock(new MockCostFunctionBase<3, 1, 2>(), nullptr, &z, x);
+ problem.AddResidualBlock(new MockCostFunctionBase<4, 1, 3>(), nullptr, &z, y);
+ problem.AddResidualBlock(new MockCostFunctionBase<5, 1, 3>(), nullptr, &z, y);
+ problem.AddResidualBlock(new MockCostFunctionBase<1, 2, 1>(), nullptr, x, &z);
+ problem.AddResidualBlock(new MockCostFunctionBase<2, 1, 3>(), nullptr, &z, y);
+ problem.AddResidualBlock(new MockCostFunctionBase<2, 2, 1>(), nullptr, x, &z);
+ problem.AddResidualBlock(new MockCostFunctionBase<1, 3>(), nullptr, y);
TripletSparseMatrix expected_block_sparse_jacobian(3, 8, 14);
{
@@ -352,7 +351,7 @@
}
problem.AddResidualBlock(new NumParameterBlocksCostFunction<1, 20>(),
- NULL,
+ nullptr,
parameter_blocks);
TripletSparseMatrix expected_block_sparse_jacobian(20, 1, 20);
@@ -388,7 +387,7 @@
double x[2];
x[0] = 1.0;
x[1] = std::numeric_limits<double>::quiet_NaN();
- problem.AddResidualBlock(new MockCostFunctionBase<1, 2>(), NULL, x);
+ problem.AddResidualBlock(new MockCostFunctionBase<1, 2>(), nullptr, x);
string error;
EXPECT_FALSE(problem.program().ParameterBlocksAreFinite(&error));
EXPECT_NE(error.find("has at least one invalid value"),
@@ -398,7 +397,7 @@
TEST(Program, InfeasibleParameterBlock) {
ProblemImpl problem;
double x[] = {0.0, 0.0};
- problem.AddResidualBlock(new MockCostFunctionBase<1, 2>(), NULL, x);
+ problem.AddResidualBlock(new MockCostFunctionBase<1, 2>(), nullptr, x);
problem.SetParameterLowerBound(x, 0, 2.0);
problem.SetParameterUpperBound(x, 0, 1.0);
string error;
@@ -409,7 +408,7 @@
TEST(Program, InfeasibleConstantParameterBlock) {
ProblemImpl problem;
double x[] = {0.0, 0.0};
- problem.AddResidualBlock(new MockCostFunctionBase<1, 2>(), NULL, x);
+ problem.AddResidualBlock(new MockCostFunctionBase<1, 2>(), nullptr, x);
problem.SetParameterLowerBound(x, 0, 1.0);
problem.SetParameterUpperBound(x, 0, 2.0);
problem.SetParameterBlockConstant(x);
diff --git a/internal/ceres/tiny_solver_autodiff_function_test.cc b/internal/ceres/tiny_solver_autodiff_function_test.cc
index 0b542a2..90033fc 100644
--- a/internal/ceres/tiny_solver_autodiff_function_test.cc
+++ b/internal/ceres/tiny_solver_autodiff_function_test.cc
@@ -70,7 +70,7 @@
// Check the case with cost-only evaluation.
residuals.setConstant(555); // Arbitrary.
- EXPECT_TRUE(f(&x(0), &residuals(0), NULL));
+ EXPECT_TRUE(f(&x(0), &residuals(0), nullptr));
EXPECT_NEAR(3.0, residuals(0), kTolerance);
EXPECT_NEAR(2.0, residuals(1), kTolerance);
@@ -110,7 +110,7 @@
template<typename T>
bool operator()(const T* parameters, T* residuals) const {
// Jacobian is not evaluated by cost function, but by autodiff.
- T* jacobian = NULL;
+ T* jacobian = nullptr;
return EvaluateResidualsAndJacobians(parameters, residuals, jacobian);
}
};
@@ -119,7 +119,7 @@
void TestHelper(const Function& f, const Vector& x0) {
Vector x = x0;
Eigen::Vector2d residuals;
- f(x.data(), residuals.data(), NULL);
+ f(x.data(), residuals.data(), nullptr);
EXPECT_GT(residuals.squaredNorm() / 2.0, 1e-10);
TinySolver<Function> solver;
@@ -140,7 +140,7 @@
AutoDiffCostFunctor f_autodiff(f);
Eigen::Vector2d residuals;
- f_autodiff(x0.data(), residuals.data(), NULL);
+ f_autodiff(x0.data(), residuals.data(), nullptr);
EXPECT_GT(residuals.squaredNorm() / 2.0, 1e-10);
TinySolver<AutoDiffCostFunctor> solver;
diff --git a/internal/ceres/trust_region_preprocessor_test.cc b/internal/ceres/trust_region_preprocessor_test.cc
index 47cc4fb..40338c1 100644
--- a/internal/ceres/trust_region_preprocessor_test.cc
+++ b/internal/ceres/trust_region_preprocessor_test.cc
@@ -96,7 +96,7 @@
TEST(TrustRegionPreprocessor, RemoveParameterBlocksFailed) {
ProblemImpl problem;
double x = 3.0;
- problem.AddResidualBlock(new FailingCostFunction, NULL, &x);
+ problem.AddResidualBlock(new FailingCostFunction, nullptr, &x);
problem.SetParameterBlockConstant(&x);
Solver::Options options;
TrustRegionPreprocessor preprocessor;
@@ -124,13 +124,13 @@
residuals[i] = kNumResiduals * kNumResiduals + i;
}
- if (jacobians == NULL) {
+ if (jacobians == nullptr) {
return true;
}
std::array<int, sizeof...(Ns)> N{Ns...};
for (size_t i = 0; i < N.size(); ++i) {
- if (jacobians[i] != NULL) {
+ if (jacobians[i] != nullptr) {
MatrixRef j(jacobians[i], kNumResiduals, N[i]);
j.setOnes();
j *= kNumResiduals * N[i];
@@ -147,8 +147,8 @@
x_ = 1.0;
y_ = 1.0;
z_ = 1.0;
- problem_.AddResidualBlock(new DummyCostFunction<1, 1, 1>, NULL, &x_, &y_);
- problem_.AddResidualBlock(new DummyCostFunction<1, 1, 1>, NULL, &y_, &z_);
+ problem_.AddResidualBlock(new DummyCostFunction<1, 1, 1>, nullptr, &x_, &y_);
+ problem_.AddResidualBlock(new DummyCostFunction<1, 1, 1>, nullptr, &y_, &z_);
}
void PreprocessForGivenLinearSolverAndVerify(
@@ -161,8 +161,8 @@
EXPECT_EQ(pp.options.linear_solver_type, linear_solver_type);
EXPECT_EQ(pp.linear_solver_options.type, linear_solver_type);
EXPECT_EQ(pp.evaluator_options.linear_solver_type, linear_solver_type);
- EXPECT_TRUE(pp.linear_solver.get() != NULL);
- EXPECT_TRUE(pp.evaluator.get() != NULL);
+ EXPECT_TRUE(pp.linear_solver.get() != nullptr);
+ EXPECT_TRUE(pp.evaluator.get() != nullptr);
}
protected:
@@ -214,8 +214,8 @@
EXPECT_EQ(pp.linear_solver_options.type, options.linear_solver_type);
EXPECT_EQ(pp.evaluator_options.linear_solver_type,
options.linear_solver_type);
- EXPECT_TRUE(pp.linear_solver.get() != NULL);
- EXPECT_TRUE(pp.evaluator.get() != NULL);
+ EXPECT_TRUE(pp.linear_solver.get() != nullptr);
+ EXPECT_TRUE(pp.evaluator.get() != nullptr);
EXPECT_TRUE(pp.minimizer_options.is_constrained);
}
@@ -246,8 +246,8 @@
EXPECT_EQ(pp.options.linear_solver_type, DENSE_SCHUR);
EXPECT_EQ(pp.linear_solver_options.type, DENSE_SCHUR);
EXPECT_EQ(pp.evaluator_options.linear_solver_type, DENSE_SCHUR);
- EXPECT_TRUE(pp.linear_solver.get() != NULL);
- EXPECT_TRUE(pp.evaluator.get() != NULL);
+ EXPECT_TRUE(pp.linear_solver.get() != nullptr);
+ EXPECT_TRUE(pp.evaluator.get() != nullptr);
}
TEST_F(LinearSolverAndEvaluatorCreationTest,
@@ -268,8 +268,8 @@
EXPECT_EQ(pp.options.linear_solver_type, DENSE_QR);
EXPECT_EQ(pp.linear_solver_options.type, DENSE_QR);
EXPECT_EQ(pp.evaluator_options.linear_solver_type, DENSE_QR);
- EXPECT_TRUE(pp.linear_solver.get() != NULL);
- EXPECT_TRUE(pp.evaluator.get() != NULL);
+ EXPECT_TRUE(pp.linear_solver.get() != nullptr);
+ EXPECT_TRUE(pp.evaluator.get() != nullptr);
}
TEST_F(LinearSolverAndEvaluatorCreationTest,
@@ -289,14 +289,14 @@
EXPECT_EQ(pp.options.linear_solver_type, DENSE_SCHUR);
EXPECT_EQ(pp.linear_solver_options.type, DENSE_SCHUR);
EXPECT_EQ(pp.evaluator_options.linear_solver_type, DENSE_SCHUR);
- EXPECT_TRUE(pp.linear_solver.get() != NULL);
- EXPECT_TRUE(pp.evaluator.get() != NULL);
+ EXPECT_TRUE(pp.linear_solver.get() != nullptr);
+ EXPECT_TRUE(pp.evaluator.get() != nullptr);
}
TEST(TrustRegionPreprocessorTest, InnerIterationsWithOneParameterBlock) {
ProblemImpl problem;
double x = 1.0;
- problem.AddResidualBlock(new DummyCostFunction<1, 1>, NULL, &x);
+ problem.AddResidualBlock(new DummyCostFunction<1, 1>, nullptr, &x);
Solver::Options options;
options.use_inner_iterations = true;
@@ -304,9 +304,9 @@
TrustRegionPreprocessor preprocessor;
PreprocessedProblem pp;
EXPECT_TRUE(preprocessor.Preprocess(options, &problem, &pp));
- EXPECT_TRUE(pp.linear_solver.get() != NULL);
- EXPECT_TRUE(pp.evaluator.get() != NULL);
- EXPECT_TRUE(pp.inner_iteration_minimizer.get() == NULL);
+ EXPECT_TRUE(pp.linear_solver.get() != nullptr);
+ EXPECT_TRUE(pp.evaluator.get() != nullptr);
+ EXPECT_TRUE(pp.inner_iteration_minimizer.get() == nullptr);
}
TEST_F(LinearSolverAndEvaluatorCreationTest,
@@ -317,9 +317,9 @@
TrustRegionPreprocessor preprocessor;
PreprocessedProblem pp;
EXPECT_TRUE(preprocessor.Preprocess(options, &problem_, &pp));
- EXPECT_TRUE(pp.linear_solver.get() != NULL);
- EXPECT_TRUE(pp.evaluator.get() != NULL);
- EXPECT_TRUE(pp.inner_iteration_minimizer.get() != NULL);
+ EXPECT_TRUE(pp.linear_solver.get() != nullptr);
+ EXPECT_TRUE(pp.evaluator.get() != nullptr);
+ EXPECT_TRUE(pp.inner_iteration_minimizer.get() != nullptr);
}
TEST_F(LinearSolverAndEvaluatorCreationTest,
@@ -347,9 +347,9 @@
TrustRegionPreprocessor preprocessor;
PreprocessedProblem pp;
EXPECT_TRUE(preprocessor.Preprocess(options, &problem_, &pp));
- EXPECT_TRUE(pp.linear_solver.get() != NULL);
- EXPECT_TRUE(pp.evaluator.get() != NULL);
- EXPECT_TRUE(pp.inner_iteration_minimizer.get() != NULL);
+ EXPECT_TRUE(pp.linear_solver.get() != nullptr);
+ EXPECT_TRUE(pp.evaluator.get() != nullptr);
+ EXPECT_TRUE(pp.inner_iteration_minimizer.get() != nullptr);
}
} // namespace internal
