[opengm] 283/386: add model print out in struct-max-margin iteration

[Ghislain Vaillant]

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

commit 695964c3cfce7bb2ad06ba241085a2685af9f238
Author: Janez Ales <janez.ales at iwr.uni-heidelberg.de>
Date:   Sat Oct 24 19:30:26 2015 +0200

    add model print out in struct-max-margin iteration
---
 include/opengm/learning/bundle-optimizer.hxx  |  4 +---
 include/opengm/learning/struct-max-margin.hxx | 19 ++++++++++---------
 2 files changed, 11 insertions(+), 12 deletions(-)

diff --git a/include/opengm/learning/bundle-optimizer.hxx b/include/opengm/learning/bundle-optimizer.hxx
index 7f99d5d..4be57e8 100644
--- a/include/opengm/learning/bundle-optimizer.hxx
+++ b/include/opengm/learning/bundle-optimizer.hxx
@@ -157,7 +157,7 @@ BundleOptimizer<T>::optimize(Oracle& oracle, Weights& w) {
 
 		t++;
 
-        std::cout << std::endl << "----------------- iteration " << t << std::endl;
+        std::cout << std::endl << "-----------------------------------------------------------------------------> iteration      " << t << std::endl;
 
         Weights w_tm1 = w;
 
@@ -286,8 +286,6 @@ BundleOptimizer<T>::findMinLowerBound(ModelWeights& w, T& value) {
 
 	for (size_t i = 0; i < w.numberOfWeights(); i++)
 		w[i] = x[i];
-    for (size_t i = 0; i < w.numberOfWeights(); i++)
-        std::cout << "x[" << i << "]=" << x[i] << std::endl;
 }
 
 template <typename T>
diff --git a/include/opengm/learning/struct-max-margin.hxx b/include/opengm/learning/struct-max-margin.hxx
index 55b03a1..f3d0b72 100644
--- a/include/opengm/learning/struct-max-margin.hxx
+++ b/include/opengm/learning/struct-max-margin.hxx
@@ -99,10 +99,11 @@ private:
 
 				// set the weights w in E(x,y) and F(x,y)
 				_dataset.getWeights() = w;
-
+                std::cout << std::endl << " MODEL : ";
 				for (int i = 0; i < _dataset.getNumberOfModels(); i++) {
+                    std::cout << i << " ";
 
-					// get E(x,y) and F(x,y)
+                    // get E(x,y) and F(x,y)
 					//std::cout << "locking model " << i << " of " << _dataset.getNumberOfModels() <<  std::endl;
 					_dataset.lockModel(i);
 					const GMType &     gm  = _dataset.getModel(i);
@@ -117,24 +118,24 @@ private:
 					// find the minimizer y* of F(y,w)
 					ConfigurationType mostViolated;
                     InferenceType inference(gml, _infParam);
-					inference.infer();
+                    inference.infer();
 					inference.arg(mostViolated);
 
 					// the optimal value of (1) is now c - F(y*,w)
-					value += c - gml.evaluate(mostViolated);
+                    value += c - gml.evaluate(mostViolated);
 
 					// the gradients are
 					typedef GradientAccumulator<Weights, ConfigurationType> GA;
-					GA gaBestEffort(gradient, bestEffort, GA::Add);
-					GA gaMostViolated(gradient, mostViolated, GA::Subtract);
-					for (size_t j = 0; j < gm.numberOfFactors(); j++) {
+                    GA gaBestEffort(gradient, bestEffort, GA::Add);
+                    GA gaMostViolated(gradient, mostViolated, GA::Subtract);
+                    for (size_t j = 0; j < gm.numberOfFactors(); j++) {
 
 						gm[j].callViFunctor(gaBestEffort);
 						gm[j].callViFunctor(gaMostViolated);
 					}
-
-					_dataset.unlockModel(i);
+                    _dataset.unlockModel(i);
 				}
+                std::cout << std::endl;
 			}
 
 		private:

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