[opengm] 216/386: added missing file

[Ghislain Vaillant]

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

commit 0a672d855fc1a0995a492a4fb9b01665722f0c9e
Author: DerThorsten <thorsten.beier at iwr.uni-heidelberg.de>
Date:   Mon Jan 12 11:49:47 2015 +0100

    added missing file
---
 include/opengm/learning/weight_averaging.hxx | 68 ++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)

diff --git a/include/opengm/learning/weight_averaging.hxx b/include/opengm/learning/weight_averaging.hxx
new file mode 100644
index 0000000..815ab4b
--- /dev/null
+++ b/include/opengm/learning/weight_averaging.hxx
@@ -0,0 +1,68 @@
+#ifndef OPENGM_LEARNING_WEIGHT_AVERAGING_HXX
+#define OPENGM_LEARNING_WEIGHT_AVERAGING_HXX
+
+
+
+namespace opengm{
+namespace learning{
+
+
+    template<class T>
+    class WeightAveraging{
+    public:
+        WeightAveraging(Weights<T> & weights, int order=2)
+        :   weights_(&weights),
+            order_(order),
+            iteration_(1){
+        }
+        WeightAveraging()
+        :   weights_(NULL),
+            order_(2),
+            iteration_(1){
+        }
+        void setWeights(Weights<T> & weights){
+            weights_ = &weights;
+        }
+
+        template<class U>
+        void operator()(const Weights<U> & weights){
+            const T t = static_cast<T>(iteration_);
+            if(order_ == -1){
+                *weights_ = weights;
+            }
+            else if(order_ == 0){
+                throw opengm::RuntimeError("running average is not yet implemented");
+            }
+            else if(order_==1){
+                const T rho = 2.0 / (t + 2.0);
+                for(size_t i=0; i<weights_->size(); ++i){
+                    (*weights_)[i] =  (*weights_)[i]*(1.0 - rho) + weights[i]*rho;
+                }
+            }
+            else if(order_ == 2){
+                const T rho = 6.0 * (t+1.0) / ( (t+2.0)*(2.0*t + 3.0) );
+                for(size_t i=0; i<weights_->size(); ++i){
+                    (*weights_)[i] =  (*weights_)[i]*(1.0 - rho) + weights[i]*rho;
+                }
+            }
+            else{
+                throw opengm::RuntimeError("order must be -1,0,1 or 2");
+            }
+            ++iteration_;
+        }
+        const Weights<T> & weights()const{
+            return weights_;
+        }
+    private:
+        Weights<T>  * weights_;
+        int order_;
+        size_t iteration_;
+    };
+
+
+
+}   // end namespace opengm
+}   // end namespace opengm
+
+
+#endif /*OPENGM_LEARNING_WEIGHT_AVERAGING_HXX*/

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