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
