Lint cleanup from William Rucklidge.
Change-Id: Ifb36de18f95d521242d8853bb54024b09effb937
diff --git a/examples/more_garbow_hillstrom.cc b/examples/more_garbow_hillstrom.cc
index 5e4d6e1..4158396 100644
--- a/examples/more_garbow_hillstrom.cc
+++ b/examples/more_garbow_hillstrom.cc
@@ -52,7 +52,7 @@
#include <cmath>
-#include <iostream>
+#include <iostream> // NOLINT
#include "ceres/ceres.h"
#include "gflags/gflags.h"
#include "glog/logging.h"
@@ -71,14 +71,14 @@
static const double constrained_optimal_cost; \
static const double unconstrained_optimal_cost; \
static CostFunction* Create() { \
- return new AutoDiffCostFunction<name, \
+ return new AutoDiffCostFunction<name, \ // NOLINT
num_residuals, \
num_parameters>(new name); \
} \
template <typename T> \
bool operator()(const T* const x, T* residual) const {
-#define END_MGH_PROBLEM return true; } };
+#define END_MGH_PROBLEM return true; } }; // NOLINT
// Rosenbrock function.
BEGIN_MGH_PROBLEM(TestProblem1, 2, 2)
@@ -91,7 +91,8 @@
const double TestProblem1::initial_x[] = {-1.2, 1.0};
const double TestProblem1::lower_bounds[] = {-kDoubleMax, -kDoubleMax};
const double TestProblem1::upper_bounds[] = {kDoubleMax, kDoubleMax};
-const double TestProblem1::constrained_optimal_cost = std::numeric_limits<double>::quiet_NaN();
+const double TestProblem1::constrained_optimal_cost =
+ std::numeric_limits<double>::quiet_NaN();
const double TestProblem1::unconstrained_optimal_cost = 0.0;
// Freudenstein and Roth function.
@@ -105,7 +106,8 @@
const double TestProblem2::initial_x[] = {0.5, -2.0};
const double TestProblem2::lower_bounds[] = {-kDoubleMax, -kDoubleMax};
const double TestProblem2::upper_bounds[] = {kDoubleMax, kDoubleMax};
-const double TestProblem2::constrained_optimal_cost = std::numeric_limits<double>::quiet_NaN();
+const double TestProblem2::constrained_optimal_cost =
+ std::numeric_limits<double>::quiet_NaN();
const double TestProblem2::unconstrained_optimal_cost = 0.0;
// Powell badly scaled function.
@@ -157,14 +159,17 @@
const T x1 = x[0];
const T x2 = x[1];
for (int i = 1; i <= 10; ++i) {
- residual[i - 1] = T(2.0) + T(2.0 * i) - exp(T(double(i)) * x1) - exp(T(double(i) * x2));
+ residual[i - 1] = T(2.0) + T(2.0 * i) -
+ exp(T(static_cast<double>(i)) * x1) -
+ exp(T(static_cast<double>(i) * x2));
}
END_MGH_PROBLEM;
const double TestProblem6::initial_x[] = {1.0, 1.0};
const double TestProblem6::lower_bounds[] = {-kDoubleMax, -kDoubleMax};
const double TestProblem6::upper_bounds[] = {kDoubleMax, kDoubleMax};
-const double TestProblem6::constrained_optimal_cost = std::numeric_limits<double>::quiet_NaN();
+const double TestProblem6::constrained_optimal_cost =
+ std::numeric_limits<double>::quiet_NaN();
const double TestProblem6::unconstrained_optimal_cost = 124.362;
// Helical valley function.
@@ -196,17 +201,20 @@
0.73, 0.96, 1.34, 2.10, 4.39};
for (int i = 1; i <=15; ++i) {
- const T u = T(double(i));
- const T v = T(double(16 - i));
- const T w = T(double(std::min(i, 16 - i)));
+ const T u = T(static_cast<double>(i));
+ const T v = T(static_cast<double>(16 - i));
+ const T w = T(static_cast<double>(std::min(i, 16 - i)));
residual[i - 1] = T(y[i - 1]) - x1 + u / (v * x2 + w * x3);
}
END_MGH_PROBLEM;
const double TestProblem8::initial_x[] = {1.0, 1.0, 1.0};
-const double TestProblem8::lower_bounds[] = {-kDoubleMax, -kDoubleMax, -kDoubleMax};
-const double TestProblem8::upper_bounds[] = {kDoubleMax, kDoubleMax, kDoubleMax};
-const double TestProblem8::constrained_optimal_cost = std::numeric_limits<double>::quiet_NaN();
+const double TestProblem8::lower_bounds[] = {
+ -kDoubleMax, -kDoubleMax, -kDoubleMax};
+const double TestProblem8::upper_bounds[] = {
+ kDoubleMax, kDoubleMax, kDoubleMax};
+const double TestProblem8::constrained_optimal_cost =
+ std::numeric_limits<double>::quiet_NaN();
const double TestProblem8::unconstrained_optimal_cost = 8.21487e-3;
// Gaussian function.
@@ -221,12 +229,12 @@
for (int i = 0; i < 15; ++i) {
const T t_i = T((8.0 - i - 1.0) / 2.0);
const T y_i = T(y[i]);
- residual[i] = x1 * exp( -x2 * (t_i - x3) * (t_i - x3) / T(2.0)) - y_i;
+ residual[i] = x1 * exp(-x2 * (t_i - x3) * (t_i - x3) / T(2.0)) - y_i;
}
END_MGH_PROBLEM;
const double TestProblem9::initial_x[] = {0.4, 1.0, 0.0};
-const double TestProblem9::lower_bounds[] = {0.398, 1.0 ,-0.5};
+const double TestProblem9::lower_bounds[] = {0.398, 1.0, -0.5};
const double TestProblem9::upper_bounds[] = {4.2, 2.0, 0.1};
const double TestProblem9::constrained_optimal_cost = 0.11279300e-7;
const double TestProblem9::unconstrained_optimal_cost = 0.112793e-7;
@@ -258,7 +266,8 @@
const double kMinLogRelativeError = 5.0;
const double log_relative_error = -std::log10(
- std::abs(2.0 * summary.final_cost - TestProblem::constrained_optimal_cost) /
+ std::abs(2.0 * summary.final_cost -
+ TestProblem::constrained_optimal_cost) /
(TestProblem::constrained_optimal_cost > 0.0
? TestProblem::constrained_optimal_cost
: 1.0));
@@ -288,7 +297,8 @@
const double kMinLogRelativeError = 5.0;
const double log_relative_error = -std::log10(
- std::abs(2.0 * summary.final_cost - TestProblem::unconstrained_optimal_cost) /
+ std::abs(2.0 * summary.final_cost -
+ TestProblem::unconstrained_optimal_cost) /
(TestProblem::unconstrained_optimal_cost > 0.0
? TestProblem::unconstrained_optimal_cost
: 1.0));
diff --git a/internal/ceres/line_search_minimizer.cc b/internal/ceres/line_search_minimizer.cc
index 977fd6c..7623f8d 100644
--- a/internal/ceres/line_search_minimizer.cc
+++ b/internal/ceres/line_search_minimizer.cc
@@ -95,7 +95,8 @@
}
state->gradient_squared_norm = (x - projected_gradient_step).squaredNorm();
- state->gradient_max_norm = (x - projected_gradient_step).lpNorm<Eigen::Infinity>();
+ state->gradient_max_norm =
+ (x - projected_gradient_step).lpNorm<Eigen::Infinity>();
return true;
}
diff --git a/internal/ceres/parameter_block.h b/internal/ceres/parameter_block.h
index c3fcce3..a978eb5 100644
--- a/internal/ceres/parameter_block.h
+++ b/internal/ceres/parameter_block.h
@@ -352,6 +352,10 @@
// If non-null, contains the residual blocks this parameter block is in.
scoped_ptr<ResidualBlockSet> residual_blocks_;
+ // Upper and lower bounds for the parameter block. These arrays are
+ // initialized to std::numeric_limits<double>::max() and
+ // -std::numeric_limits<double>::max() respectively which correspond
+ // to the parameter block being unconstrained.
scoped_array<double> upper_bounds_;
scoped_array<double> lower_bounds_;
diff --git a/internal/ceres/solver_impl.cc b/internal/ceres/solver_impl.cc
index 937ab78..a0ad1b7 100644
--- a/internal/ceres/solver_impl.cc
+++ b/internal/ceres/solver_impl.cc
@@ -364,7 +364,8 @@
for (int j = 0; j < size; ++j) {
if (array[j] < lower_bounds[j] || array[j] > upper_bounds[j]) {
*message = StringPrintf(
- "ParameterBlock: %p with size %d has at least one infeasible value."
+ "ParameterBlock: %p with size %d has at least one infeasible "
+ "value."
"\nFirst infeasible value is at index: %d."
"\nLower bound: %e, value: %e, upper bound: %e"
"\nParameter block values: ",
@@ -380,7 +381,8 @@
for (int j = 0; j < size; ++j) {
if (lower_bounds[j] >= upper_bounds[j]) {
*message = StringPrintf(
- "ParameterBlock: %p with size %d has at least one infeasible bound."
+ "ParameterBlock: %p with size %d has at least one infeasible "
+ "bound."
"\nFirst infeasible bound is at index: %d."
"\nLower bound: %e, upper bound: %e"
"\nParameter block values: ",
diff --git a/internal/ceres/trust_region_minimizer.cc b/internal/ceres/trust_region_minimizer.cc
index 51eb75f..e2f1234 100644
--- a/internal/ceres/trust_region_minimizer.cc
+++ b/internal/ceres/trust_region_minimizer.cc
@@ -175,7 +175,8 @@
if (options.is_constrained) {
delta.setZero();
if (!evaluator->Plus(x.data(), delta.data(), x_plus_delta.data())) {
- summary->message = "Unable to project initial point onto the feasible set.";
+ summary->message =
+ "Unable to project initial point onto the feasible set.";
summary->termination_type = FAILURE;
LOG_IF(WARNING, is_not_silent) << "Terminating: " << summary->message;
return;
