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Replace NULL with nullptr in the documentation.

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Change-Id: I995f68770e2a4b6027c0a1d3edf5eb5132b081d7
This commit is contained in:
Sameer Agarwal
2020-08-03 04:17:33 -07:00
parent ee280e27a6
commit ab4ed32cda
5 changed files with 34 additions and 34 deletions
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docs/source/nnls_tutorial.rst
+11 -11
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@@ -111,7 +111,7 @@ Ceres solve it.
// auto-differentiation to obtain the derivative (jacobian).
CostFunction* cost_function =
new AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor);
problem.AddResidualBlock(cost_function, NULL, &x);
problem.AddResidualBlock(cost_function, nullptr, &x);
// Run the solver!
Solver::Options options;
@@ -212,7 +212,7 @@ Which is added to the :class:`Problem` as:
CostFunction* cost_function =
new NumericDiffCostFunction<NumericDiffCostFunctor, ceres::CENTRAL, 1, 1>(
new NumericDiffCostFunctor);
problem.AddResidualBlock(cost_function, NULL, &x);
problem.AddResidualBlock(cost_function, nullptr, &x);
Notice the parallel from when we were using automatic differentiation
@@ -220,7 +220,7 @@ Notice the parallel from when we were using automatic differentiation
CostFunction* cost_function =
new AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor);
problem.AddResidualBlock(cost_function, NULL, &x);
problem.AddResidualBlock(cost_function, nullptr, &x);
The construction looks almost identical to the one used for automatic
differentiation, except for an extra template parameter that indicates
@@ -261,7 +261,7 @@ x`.
residuals[0] = 10 - x;
// Compute the Jacobian if asked for.
if (jacobians != NULL && jacobians[0] != NULL) {
if (jacobians != nullptr && jacobians[0] != nullptr) {
jacobians[0][0] = -1;
}
return true;
@@ -358,13 +358,13 @@ respectively. Using these, the problem can be constructed as follows:
// Add residual terms to the problem using the using the autodiff
// wrapper to get the derivatives automatically.
problem.AddResidualBlock(
new AutoDiffCostFunction<F1, 1, 1, 1>(new F1), NULL, &x1, &x2);
new AutoDiffCostFunction<F1, 1, 1, 1>(new F1), nullptr, &x1, &x2);
problem.AddResidualBlock(
new AutoDiffCostFunction<F2, 1, 1, 1>(new F2), NULL, &x3, &x4);
new AutoDiffCostFunction<F2, 1, 1, 1>(new F2), nullptr, &x3, &x4);
problem.AddResidualBlock(
new AutoDiffCostFunction<F3, 1, 1, 1>(new F3), NULL, &x2, &x3)
new AutoDiffCostFunction<F3, 1, 1, 1>(new F3), nullptr, &x2, &x3)
problem.AddResidualBlock(
new AutoDiffCostFunction<F4, 1, 1, 1>(new F4), NULL, &x1, &x4);
new AutoDiffCostFunction<F4, 1, 1, 1>(new F4), nullptr, &x1, &x4);
Note that each ``ResidualBlock`` only depends on the two parameters
@@ -496,7 +496,7 @@ Assuming the observations are in a :math:`2n` sized array called
CostFunction* cost_function =
new AutoDiffCostFunction<ExponentialResidual, 1, 1, 1>(
new ExponentialResidual(data[2 * i], data[2 * i + 1]));
problem.AddResidualBlock(cost_function, NULL, &m, &c);
problem.AddResidualBlock(cost_function, nullptr, &m, &c);
}
Compiling and running `examples/curve_fitting.cc
@@ -568,7 +568,7 @@ outliers. To associate a loss function with a residual block, we change
.. code-block:: c++
problem.AddResidualBlock(cost_function, NULL , &m, &c);
problem.AddResidualBlock(cost_function, nullptr , &m, &c);
to
@@ -697,7 +697,7 @@ as follows:
bal_problem.observations()[2 * i + 0],
bal_problem.observations()[2 * i + 1]);
problem.AddResidualBlock(cost_function,
NULL /* squared loss */,
nullptr /* squared loss */,
bal_problem.mutable_camera_for_observation(i),
bal_problem.mutable_point_for_observation(i));
}