Untabify changes from Jim Roseborough
Change-Id: Ic640b34ba785669b415acfbeb2c931bea768f985
diff --git a/docs/source/analytical_derivatives.rst b/docs/source/analytical_derivatives.rst
index cfd5028..2a3a404 100644
--- a/docs/source/analytical_derivatives.rst
+++ b/docs/source/analytical_derivatives.rst
@@ -58,22 +58,22 @@
virtual ~Rat43Analytic() {}
virtual bool Evaluate(double const* const* parameters,
double* residuals,
- double** jacobians) const {
- const double b1 = parameters[0][0];
- const double b2 = parameters[0][1];
- const double b3 = parameters[0][2];
- const double b4 = parameters[0][3];
+ double** jacobians) const {
+ const double b1 = parameters[0][0];
+ const double b2 = parameters[0][1];
+ const double b3 = parameters[0][2];
+ const double b4 = parameters[0][3];
- residuals[0] = b1 * pow(1 + exp(b2 - b3 * x_), -1.0 / b4) - y_;
+ residuals[0] = b1 * pow(1 + exp(b2 - b3 * x_), -1.0 / b4) - y_;
if (!jacobians) return true;
- double* jacobian = jacobians[0];
- if (!jacobian) return true;
+ double* jacobian = jacobians[0];
+ if (!jacobian) return true;
jacobian[0] = pow(1 + exp(b2 - b3 * x_), -1.0 / b4);
jacobian[1] = -b1 * exp(b2 - b3 * x_) *
pow(1 + exp(b2 - b3 * x_), -1.0 / b4 - 1) / b4;
- jacobian[2] = x_ * b1 * exp(b2 - b3 * x_) *
+ jacobian[2] = x_ * b1 * exp(b2 - b3 * x_) *
pow(1 + exp(b2 - b3 * x_), -1.0 / b4 - 1) / b4;
jacobian[3] = b1 * log(1 + exp(b2 - b3 * x_)) *
pow(1 + exp(b2 - b3 * x_), -1.0 / b4) / (b4 * b4);
@@ -97,27 +97,27 @@
virtual ~Rat43AnalyticOptimized() {}
virtual bool Evaluate(double const* const* parameters,
double* residuals,
- double** jacobians) const {
- const double b1 = parameters[0][0];
- const double b2 = parameters[0][1];
- const double b3 = parameters[0][2];
- const double b4 = parameters[0][3];
+ double** jacobians) const {
+ const double b1 = parameters[0][0];
+ const double b2 = parameters[0][1];
+ const double b3 = parameters[0][2];
+ const double b4 = parameters[0][3];
- const double t1 = exp(b2 - b3 * x_);
+ const double t1 = exp(b2 - b3 * x_);
const double t2 = 1 + t1;
- const double t3 = pow(t2, -1.0 / b4);
- residuals[0] = b1 * t3 - y_;
+ const double t3 = pow(t2, -1.0 / b4);
+ residuals[0] = b1 * t3 - y_;
if (!jacobians) return true;
- double* jacobian = jacobians[0];
- if (!jacobian) return true;
+ double* jacobian = jacobians[0];
+ if (!jacobian) return true;
- const double t4 = pow(t2, -1.0 / b4 - 1);
- jacobian[0] = t3;
- jacobian[1] = -b1 * t1 * t4 / b4;
- jacobian[2] = -x_ * jacobian[1];
- jacobian[3] = b1 * log(t2) * t3 / (b4 * b4);
- return true;
+ const double t4 = pow(t2, -1.0 / b4 - 1);
+ jacobian[0] = t3;
+ jacobian[1] = -b1 * t1 * t4 / b4;
+ jacobian[2] = -x_ * jacobian[1];
+ jacobian[3] = b1 * log(t2) * t3 / (b4 * b4);
+ return true;
}
private:
@@ -182,11 +182,11 @@
.. rubric:: Footnotes
.. [#f1] The notion of best fit depends on the choice of the objective
- function used to measure the quality of fit, which in turn
- depends on the underlying noise process which generated the
- observations. Minimizing the sum of squared differences is
- the right thing to do when the noise is `Gaussian
- <https://en.wikipedia.org/wiki/Normal_distribution>`_. In
- that case the optimal value of the parameters is the `Maximum
- Likelihood Estimate
- <https://en.wikipedia.org/wiki/Maximum_likelihood_estimation>`_.
+ function used to measure the quality of fit, which in turn
+ depends on the underlying noise process which generated the
+ observations. Minimizing the sum of squared differences is
+ the right thing to do when the noise is `Gaussian
+ <https://en.wikipedia.org/wiki/Normal_distribution>`_. In
+ that case the optimal value of the parameters is the `Maximum
+ Likelihood Estimate
+ <https://en.wikipedia.org/wiki/Maximum_likelihood_estimation>`_.
diff --git a/docs/source/automatic_derivatives.rst b/docs/source/automatic_derivatives.rst
index 47a10af..1251814 100644
--- a/docs/source/automatic_derivatives.rst
+++ b/docs/source/automatic_derivatives.rst
@@ -39,7 +39,7 @@
CostFunction* cost_function =
new AutoDiffCostFunction<Rat43CostFunctor, 1, 4>(
- new Rat43CostFunctor(x, y));
+ new Rat43CostFunctor(x, y));
Notice that compared to numeric differentiation, the only difference
when defining the functor for use with automatic differentiation is
@@ -220,7 +220,7 @@
template <int N> Jet<N> pow(const Jet<N>& f, const Jet<N>& g) {
return Jet<N>(pow(f.a, g.a),
g.a * pow(f.a, g.a - 1.0) * f.v +
- pow(f.a, g.a) * log(f.a); * g.v);
+ pow(f.a, g.a) * log(f.a); * g.v);
}
