Fixup for optimizer docs (will be squashed).

lsst · May 12, 2016 · 0daccd0 · 0daccd0
1 parent 54c2674
commit 0daccd0
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Showing 2 changed files with 7 additions and 17 deletions.
diff --git a/include/lsst/meas/modelfit/optimizer.h b/include/lsst/meas/modelfit/optimizer.h
@@ -187,7 +187,7 @@ class OptimizerObjective {
      *  compute analytic derivatives are not supported.
      *
      *  @param[in]  parameters    An array of parameters with shape (parameterSize).
-     *  @param[out] deerivatives  Output array that will contain d(model - data)/d(parameters) on
+     *  @param[out] derivatives   Output array that will contain d(model - data)/d(parameters) on
      *                            return.  Must be allocated to shape (dataSize, parameterSize),
      *                            but need not be initialized.
      */
@@ -212,12 +212,12 @@ class OptimizerObjective {
      *  The default implementation simply returns 1.0 (appropriate for an unnormalized constant prior,
      *  which is mathematically equivalent to no prior).
      */
-    virtual Scalar computePrior(ndarray::Array<Scalar const,1,1> const & parameters) const;
+    virtual Scalar computePrior(ndarray::Array<Scalar const,1,1> const & parameters) const { return 1.0; }
 
     /**
      *  Compute the first and second derivatives of the Bayesian prior with respect to the parameters.
      *
-     *  The default implementation simply returns 0.0 (appropriate for an constant prior,
+     *  The default implementation simply sets the output arrays to 0.0 (appropriate for an constant prior,
      *  which is mathematically equivalent to no prior).
      *
      *  @param[in]  parameters    An array of parameters with shape (parameterSize).
@@ -232,7 +232,10 @@ class OptimizerObjective {
         ndarray::Array<Scalar const,1,1> const & parameters,
         ndarray::Array<Scalar,1,1> const & gradient,
         ndarray::Array<Scalar,2,1> const & hessian
-    ) const;
+    ) const {
+        gradient.deep() = 0.0;
+        hessian.deep() = 0.0;
+    }
 
     virtual ~OptimizerObjective() {}
 };

diff --git a/src/optimizer.cc b/src/optimizer.cc
@@ -271,19 +271,6 @@ PTR(OptimizerObjective) OptimizerObjective::makeFromLikelihood(
     return boost::make_shared<LikelihoodOptimizerObjective>(likelihood, prior);
 }
 
-Scalar OptimizerObjective::computePrior(ndarray::Array<Scalar const,1,1> const & parameters) const {
-    return 1.0;
-}
-
-void OptimizerObjective::differentiatePrior(
-    ndarray::Array<Scalar const,1,1> const & parameters,
-    ndarray::Array<Scalar,1,1> const & gradient,
-    ndarray::Array<Scalar,2,1> const & hessian
-) const {
-    gradient.deep() = 0.0;
-    hessian.deep() = 0.0;
-}
-
 // ----------------- OptimizerIterationData -----------------------------------------------------------------
 
 OptimizerIterationData::OptimizerIterationData(int dataSize, int parameterSize) :