Various corrections and enhancements to the documentation. Change-Id: I03519bfccf4367b36d36006f1450d5fbcbbf8621
diff --git a/include/ceres/autodiff_local_parameterization.h b/include/ceres/autodiff_local_parameterization.h index 1099061..0aae6c7 100644 --- a/include/ceres/autodiff_local_parameterization.h +++ b/include/ceres/autodiff_local_parameterization.h
@@ -58,7 +58,7 @@ // // For example, Quaternions have a three dimensional local // parameterization. It's plus operation can be implemented as (taken -// from interncal/ceres/auto_diff_local_parameterization_test.cc) +// from internal/ceres/auto_diff_local_parameterization_test.cc) // // struct QuaternionPlus { // template<typename T>
diff --git a/include/ceres/loss_function.h b/include/ceres/loss_function.h index 7b4af15..b99c184 100644 --- a/include/ceres/loss_function.h +++ b/include/ceres/loss_function.h
@@ -347,19 +347,20 @@ // // CostFunction* cost_function = // new AutoDiffCostFunction < UW_Camera_Mapper, 2, 9, 3>( -// new UW_Camera_Mapper(data->observations[2*i + 0], -// data->observations[2*i + 1])); +// new UW_Camera_Mapper(feature_x, feature_y)); // // LossFunctionWrapper* loss_function(new HuberLoss(1.0), TAKE_OWNERSHIP); // // problem.AddResidualBlock(cost_function, loss_function, parameters); // // Solver::Options options; -// scoped_ptr<Solver::Summary> summary1(Solve(problem, options)); +// Solger::Summary summary; +// +// Solve(options, &problem, &summary) // // loss_function->Reset(new HuberLoss(1.0), TAKE_OWNERSHIP); // -// scoped_ptr<Solver::Summary> summary2(Solve(problem, options)); +// Solve(options, &problem, &summary) // class LossFunctionWrapper : public LossFunction { public:
diff --git a/include/ceres/numeric_diff_cost_function.h b/include/ceres/numeric_diff_cost_function.h index 6c44b58..a47a66d 100644 --- a/include/ceres/numeric_diff_cost_function.h +++ b/include/ceres/numeric_diff_cost_function.h
@@ -82,14 +82,14 @@ // // CostFunction* cost_function // = new NumericDiffCostFunction<MyScalarCostFunctor, CENTRAL, 1, 2, 2>( -// new MyScalarCostFunctor(1.0)); ^ ^ ^ -// | | | | -// Finite Differencing Scheme -+ | | | -// Dimension of residual ----------+ | | -// Dimension of x --------------------+ | -// Dimension of y -----------------------+ +// new MyScalarCostFunctor(1.0)); ^ ^ ^ ^ +// | | | | +// Finite Differencing Scheme -+ | | | +// Dimension of residual ------------+ | | +// Dimension of x ----------------------+ | +// Dimension of y -------------------------+ // -// In this example, there is usually an instance for each measumerent of k. +// In this example, there is usually an instance for each measurement of k. // // In the instantiation above, the template parameters following // "MyScalarCostFunctor", "1, 2, 2", describe the functor as computing @@ -126,7 +126,7 @@ // To get a numerically differentiated cost function, define a // subclass of CostFunction such that the Evaluate() function ignores // the jacobian parameter. The numeric differentiation wrapper will -// fill in the jacobian parameter if nececssary by repeatedly calling +// fill in the jacobian parameter if necessary by repeatedly calling // the Evaluate() function with small changes to the appropriate // parameters, and computing the slope. For performance, the numeric // differentiation wrapper class is templated on the concrete cost