Update documentation to rename FeatureGradient -> PartialGradient

doc/policies/functiontype.hpp

@@ -11,7 +11,7 @@ algorithm.
 The \c FunctionType template parameter required by the Optimizer class can have
 additional requirements imposed on it, depending on the type of optimizer used.
 
-@section Interface requirements
+@section requirements Interface requirements
 
 The most basic requirements for the \c FunctionType parameter are the
 implementations of two public member functions, with the following interface

src/mlpack/core/optimizers/parallel_sgd/sparse_test_function.hpp

@@ -72,7 +72,7 @@ class SparseTestFunction
   }
 
   //! Evaluate the gradient of a feature function.
-  void FeatureGradient(const arma::mat& coordinates,
+  void PartialGradient(const arma::mat& coordinates,
                        const size_t j,
                        arma::sp_mat& gradient) const
   {

src/mlpack/core/optimizers/scd/descent_policies/greedy_descent.hpp

@@ -58,7 +58,7 @@ class GreedyDescent
     {
       arma::sp_mat fGrad;
 
-      function.FeatureGradient(iterate, i, fGrad);
+      function.PartialGradient(iterate, i, fGrad);
 
       double descent = arma::accu(fGrad);
       if (descent > bestDescent)

src/mlpack/core/optimizers/scd/scd.hpp

@@ -34,13 +34,13 @@ namespace optimization {
  *
  *  size_t NumFeatures();
  *  double Evaluate(const arma::mat& coordinates);
- *  void FeatureGradient(const arma::mat& coordinates,
+ *  void PartialGradient(const arma::mat& coordinates,
  *                       const size_t j,
  *                       arma::sp_mat& gradient);
  *
  *  NumFeatures() should return the number of features in the decision variable.
  *  Evaluate gives the value of the loss function at the current decision
- *  variable and FeatureGradient is used to evaluate the partial gradient with
+ *  variable and PartialGradient is used to evaluate the partial gradient with
  *  respect to the jth feature.
  *
  *  @tparam ResolvableFunctionType A function whose partial gradients with

src/mlpack/core/optimizers/scd/scd_impl.hpp

@@ -50,7 +50,7 @@ double SCD<DescentPolicyType>::Optimize(ResolvableFunctionType& function,
     size_t featureIdx = descentPolicy.DescentFeature(i, iterate, function);
 
     // Get the partial gradient with respect to this feature.
-    function.FeatureGradient(iterate, featureIdx, gradient);
+    function.PartialGradient(iterate, featureIdx, gradient);
 
     // Update the decision variable with the partial gradient.
     iterate.col(featureIdx) -= stepSize * gradient.col(featureIdx);

src/mlpack/methods/logistic_regression/logistic_regression_function.hpp

@@ -118,7 +118,7 @@ class LogisticRegressionFunction
    *    be computed.
    * @param gradient Sparse matrix to output gradient into.
    */
-  void FeatureGradient(const arma::mat& parameters,
+  void PartialGradient(const arma::mat& parameters,
                        const size_t j,
                        arma::sp_mat& gradient) const;
 

src/mlpack/methods/logistic_regression/logistic_regression_function_impl.hpp

@@ -177,7 +177,7 @@ void LogisticRegressionFunction<MatType>::Gradient(
  * function with respect to the individual features in the parameter.
  */
 template <typename MatType>
-void LogisticRegressionFunction<MatType>::FeatureGradient(
+void LogisticRegressionFunction<MatType>::PartialGradient(
     const arma::mat& parameters,
     const size_t j,
     arma::sp_mat& gradient) const

src/mlpack/methods/softmax_regression/softmax_regression_function.cpp

@@ -206,7 +206,7 @@ void SoftmaxRegressionFunction::Gradient(const arma::mat& parameters,
   }
 }
 
-void SoftmaxRegressionFunction::FeatureGradient(const arma::mat& parameters,
+void SoftmaxRegressionFunction::PartialGradient(const arma::mat& parameters,
                                                 const size_t j,
                                                 arma::sp_mat& gradient) const
 {

src/mlpack/methods/softmax_regression/softmax_regression_function.hpp

@@ -120,7 +120,7 @@ class SoftmaxRegressionFunction
    *    gradient is to be computed.
    * @param gradient Out param for the gradient value.
    */
-  void FeatureGradient(const arma::mat& parameters,
+  void PartialGradient(const arma::mat& parameters,
                        size_t j,
                        arma::sp_mat& gradient) const;
 

src/mlpack/tests/scd_test.cpp

@@ -96,9 +96,9 @@ BOOST_AUTO_TEST_CASE(GreedyDescentTest)
 }
 
 /**
- * Test that LogisticRegressionFunction::FeatureGradient() works as expected.
+ * Test that LogisticRegressionFunction::PartialGradient() works as expected.
  */
-BOOST_AUTO_TEST_CASE(LogisticRegressionFunctionFeatureGradientTest)
+BOOST_AUTO_TEST_CASE(LogisticRegressionFunctionPartialGradientTest)
 {
   // Evaluate the gradient and feature gradient and equate.
   arma::mat predictors("0 0 0.4; 0 0 0.6; 0 0.3 0; 0.2 0 0; 0.2 -0.5 0;");
@@ -115,16 +115,16 @@ BOOST_AUTO_TEST_CASE(LogisticRegressionFunctionFeatureGradientTest)
   for (size_t i = 0; i < f.NumFeatures(); ++i)
   {
     arma::sp_mat fGrad;
-    f.FeatureGradient(testPoint, i, fGrad);
+    f.PartialGradient(testPoint, i, fGrad);
 
     CheckMatrices(testGradient.col(i), arma::mat(fGrad.col(i)));
   }
 }
 
 /**
- * Test that SoftmaxRegressionFunction::FeatureGradient() works as expected.
+ * Test that SoftmaxRegressionFunction::PartialGradient() works as expected.
  */
-BOOST_AUTO_TEST_CASE(SoftmaxRegressionFunctionFeatureGradientTest)
+BOOST_AUTO_TEST_CASE(SoftmaxRegressionFunctionPartialGradientTest)
 {
   const size_t points = 1000;
   const size_t inputSize = 10;
@@ -157,7 +157,7 @@ BOOST_AUTO_TEST_CASE(SoftmaxRegressionFunctionFeatureGradientTest)
     // Get the gradient for this feature.
     arma::sp_mat fGrad;
 
-    srf.FeatureGradient(parameters, j, fGrad);
+    srf.PartialGradient(parameters, j, fGrad);
 
     CheckMatrices(gradient.col(j), arma::mat(fGrad.col(j)));
   }