[opengm] 268/386: seems to be working

[Ghislain Vaillant]

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

commit 1c9a6e17bab42aea1bd16fd5c43dbb68635e4a5f
Author: DerThorsten <thorsten.beier at iwr.uni-heidelberg.de>
Date:   Tue Mar 10 16:46:56 2015 +0100

    seems to be working
---
 fubar/noisy_squares_3_labels.py |  4 ++--
 include/opengm/learning/rws.hxx | 12 ++++++------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/fubar/noisy_squares_3_labels.py b/fubar/noisy_squares_3_labels.py
index 75e3482..2d9bf7a 100644
--- a/fubar/noisy_squares_3_labels.py
+++ b/fubar/noisy_squares_3_labels.py
@@ -98,10 +98,10 @@ infCls = opengm.inference.TrwsExternal
 param = opengm.InfParam()
 
 
-learner =  learning.subgradientSSVM(dataset, learningRate=1.0, C=0.9, learningMode='batch',maxIterations=200, averaging=-1)
+learner =  learning.subgradientSSVM(dataset, learningRate=1.0, C=0.9, learningMode='batch',maxIterations=5, averaging=-1)
 learner.learn(infCls=infCls,parameter=param,connectedComponents=False,infMode='n')
 
-learner =  learning.rws(dataset, learningRate=1.0, C=0.05,maxIterations=5000, p=20, sigma=2.0)
+learner =  learning.rws(dataset, learningRate=1.0, C=1.0,maxIterations=5000, p=100, sigma=1.3)
 learner.learn(infCls=infCls,parameter=param,connectedComponents=False,infMode='n')
 
 
diff --git a/include/opengm/learning/rws.hxx b/include/opengm/learning/rws.hxx
index dadc60a..42c7cd0 100644
--- a/include/opengm/learning/rws.hxx
+++ b/include/opengm/learning/rws.hxx
@@ -210,14 +210,14 @@ namespace opengm {
                 opengm::infer<INF>(gm, para, arg);
                 lossVec[p] = dataset_.getLoss(arg, gmi);
                 
-                featureAcc_.accumulateModelFeatures(gm, dataset_.getGT(gmi).begin(), arg.begin());
+                //featureAcc_.accumulateModelFeatures(gm, dataset_.getGT(gmi).begin(), arg.begin());
                 // update weights
-                const double wChange =updateWeights();      
+                //const double wChange =updateWeights();      
             }
 
-            for(size_t wi=0; wi<nWegihts; ++wi){
-                gradient[wi] = featureAcc_.getWeight(wi);
-            }
+            //for(size_t wi=0; wi<nWegihts; ++wi){
+            //    gradient[wi] = featureAcc_.getWeight(wi);
+            //}
             std::fill(gradient.begin(), gradient.end(),0.0);
             for(size_t p=0; p<para_.p_; ++p){
                 for(size_t wi=0; wi<nWegihts; ++wi){
@@ -230,7 +230,7 @@ namespace opengm {
             // do update
             for(size_t wi=0; wi<nWegihts; ++wi){
                 const ValueType oldWeight = currentWeights[wi];
-                const ValueType newWeights = (1.0 + oldWeight - actualLearningRate*gradient[wi])*para_.C_;
+                const ValueType newWeights = (oldWeight - actualLearningRate*gradient[wi])*para_.C_;
                 //std::cout<<"wi "<<newWeights<<"\n";
                 dataset_.getWeights().setWeight(wi, newWeights);
             }

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