[opengm] 95/386: bugfix: gradient-accumulator needs to pass local configuration to function

[Ghislain Vaillant]

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ghisvail-guest pushed a commit to branch debian/master
in repository opengm.

commit d2e7849beb2f250e8d6b13dd0628df543f70fcf4
Author: Jan Funke <funke at ini.ch>
Date:   Wed Dec 17 14:35:27 2014 +0100

    bugfix: gradient-accumulator needs to pass local configuration to function
---
 include/opengm/learning/gradient-accumulator.hxx | 27 +++++++++++++++++++-----
 include/opengm/learning/struct-max-margin.hxx    |  4 ++--
 2 files changed, 24 insertions(+), 7 deletions(-)

diff --git a/include/opengm/learning/gradient-accumulator.hxx b/include/opengm/learning/gradient-accumulator.hxx
index 151967c..f702aeb 100644
--- a/include/opengm/learning/gradient-accumulator.hxx
+++ b/include/opengm/learning/gradient-accumulator.hxx
@@ -25,9 +25,17 @@ public:
 
 	/**
 	 * @param gradient
-	 *              ModelWeights reference to store the gradients.
+	 *              ModelWeights reference to store the gradients. Gradient 
+	 *              values will only be added (or subtracted, if mode == 
+	 *              Subtract), so you have to make sure gradient is properly 
+	 *              initialized to zero.
+	 *
 	 * @param configuration
-	 *              Current configuration of the variables in the model.
+	 *              Configuration of the variables in the model, to evaluate the 
+	 *              gradient for.
+	 *
+	 * @param mode
+	 *              Add or Subtract the weight gradients from gradient.
 	 */
 	GradientAccumulator(ModelWeights& gradient, const ConfigurationType& configuration, Mode mode = Add) :
 		_gradient(gradient),
@@ -38,14 +46,23 @@ public:
 			gradient[i] = 0;
 	}
 
-	template <typename FunctionType>
-	void operator()(const FunctionType& function) {
+	template <typename Iterator, typename FunctionType>
+	void operator()(Iterator begin, Iterator end, const FunctionType& function) {
+
+		ConfigurationType localConfiguration;
+		for (Iterator j = begin; j != end; j++)
+			localConfiguration.push_back(_configuration[*j]);
+
+		std::cout << "asking a function for gradient with configuration " << localConfiguration << std::endl;
 
 		for (int i = 0; i < function.numberOfWeights(); i++) {
 
 			int index = function.weightIndex(i);
 
-			double g = function.weightGradient(i, _configuration.begin());
+			double g = function.weightGradient(i, localConfiguration.begin());
+
+			std::cout << "gradient for weight " << index << " is " << g << std::endl;
+
 			if (_mode == Add)
 				_gradient[index] += g;
 			else
diff --git a/include/opengm/learning/struct-max-margin.hxx b/include/opengm/learning/struct-max-margin.hxx
index 47031e1..405489e 100644
--- a/include/opengm/learning/struct-max-margin.hxx
+++ b/include/opengm/learning/struct-max-margin.hxx
@@ -132,8 +132,8 @@ private:
 					GA gaMostViolated(gradient, mostViolated, GA::Subtract);
 					for (size_t j = 0; j < gm.numberOfFactors(); j++) {
 
-						gm[j].callFunctor(gaBestEffort);
-						gm[j].callFunctor(gaMostViolated);
+						gm[j].callViFunctor(gaBestEffort);
+						gm[j].callViFunctor(gaMostViolated);
 					}
 
 					_dataset.unlockModel(i);

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