[opengm] 299/386: most certainly fixed bugs
Ghislain Vaillant
ghisvail-guest at moszumanska.debian.org
Wed Aug 31 08:38:15 UTC 2016
This is an automated email from the git hooks/post-receive script.
ghisvail-guest pushed a commit to branch debian/master
in repository opengm.
commit 909a05073f6b6f190de89cd9d0c37664bef628dd
Author: DerThorsten <thorsten.beier at iwr.uni-heidelberg.de>
Date: Thu Dec 10 17:26:04 2015 -0300
most certainly fixed bugs
---
.../opengm/functions/learnable/lsum_of_experts.hxx | 261 ---------------------
include/opengm/learning/dataset/testdatasets.hxx | 8 +-
.../learning/maximum-likelihood-learning.hxx | 4 +-
src/unittest/learning/test_dataset.cxx | 4 +-
src/unittest/learning/test_dataset_io.cxx | 4 +-
src/unittest/learning/test_gridsearch_learner.cxx | 4 +-
src/unittest/learning/test_learning.cxx | 4 +-
.../learning/test_maximum_likelihood_learner.cxx | 2 +-
src/unittest/learning/test_subgradient_ssvm.cxx | 4 +-
src/unittest/test_linear_constraint.cxx | 1 +
10 files changed, 18 insertions(+), 278 deletions(-)
diff --git a/include/opengm/functions/learnable/lsum_of_experts.hxx b/include/opengm/functions/learnable/lsum_of_experts.hxx
deleted file mode 100644
index b86dedb..0000000
--- a/include/opengm/functions/learnable/lsum_of_experts.hxx
+++ /dev/null
@@ -1,261 +0,0 @@
-#pragma once
-#ifndef OPENGM_LEARNABLE_LSUM_OF_EXPERTS_FUNCTION_HXX
-#define OPENGM_LEARNABLE_LSUM_OF_EXPERTS_FUNCTION_HXX
-#warning Due to an ambiguity in the class name this header is obsolet! Please use lweightedsum_of_functions.hxx instead.
-
-#include <algorithm>
-#include <vector>
-#include <cmath>
-
-#include "opengm/opengm.hxx"
-#include "opengm/functions/function_registration.hxx"
-#include "opengm/functions/function_properties_base.hxx"
-#include "opengm/datastructures/marray/marray.hxx"
-#include "opengm/graphicalmodel/weights.hxx"
-
-namespace opengm {
-namespace functions {
-namespace learnable {
-
-/// Learnable feature function for two variables
-///
-/// f(x) = \sum_i w(i) * feat(i)(x)
-/// - w = parameter vector
-/// - feat = feature vector
-///
-///
-/// \ingroup functions
-template<class T, class I = size_t, class L = size_t>
-class LSumOfExperts
- : public opengm::FunctionBase<opengm::functions::learnable::LSumOfExperts<T, I, L>, T, I, L>
-{
-public:
- typedef T ValueType;
- typedef L LabelType;
- typedef I IndexType;
-
- LSumOfExperts();
- LSumOfExperts(const std::vector<L>& shape,
- const opengm::learning::Weights<T>& weights,
- const std::vector<size_t>& weightIDs,
- const std::vector<marray::Marray<T> >& feat
- );
-
- L shape(const size_t) const;
- size_t size() const;
- size_t dimension() const;
- template<class ITERATOR> T operator()(ITERATOR) const;
-
- // parameters
- void setWeights(const opengm::learning::Weights<T>& weights) const
- {weights_ = &weights;}
- size_t numberOfWeights()const
- {return weightIDs_.size();}
- I weightIndex(const size_t weightNumber) const
- {return weightIDs_[weightNumber];} //dummy
- template<class ITERATOR>
- T weightGradient(size_t,ITERATOR) const;
-
-protected:
- mutable const opengm::learning::Weights<T>* weights_;
- std::vector<L> shape_;
- std::vector<size_t> weightIDs_;
- std::vector<marray::Marray<T> > feat_;
-
- friend class opengm::FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> >;
-};
-
-
-template <class T, class I, class L>
-inline
-LSumOfExperts<T, I, L>::LSumOfExperts
-(
- const std::vector<L>& shape,
- const opengm::learning::Weights<T>& weights,
- const std::vector<size_t>& weightIDs,
- const std::vector<marray::Marray<T> >& feat
- )
- : shape_(shape), weights_(&weights), weightIDs_(weightIDs),feat_(feat)
-{
- OPENGM_ASSERT( size() == feat_[0].size() );
- OPENGM_ASSERT( weightIDs_.size() == feat_.size() );
-}
-
-template <class T, class I, class L>
-inline
-LSumOfExperts<T, I, L>::LSumOfExperts()
- : shape_(std::vector<L>(0)), weightIDs_(std::vector<size_t>(0)), feat_(std::vector<marray::Marray<T> >(0))
-{
- ;
-}
-
-
-template <class T, class I, class L>
-template <class ITERATOR>
-inline T
-LSumOfExperts<T, I, L>::weightGradient
-(
- size_t weightNumber,
- ITERATOR begin
-) const {
- OPENGM_ASSERT(weightNumber< numberOfWeights());
- return feat_[weightNumber](*begin);
-}
-
-template <class T, class I, class L>
-template <class ITERATOR>
-inline T
-LSumOfExperts<T, I, L>::operator()
-(
- ITERATOR begin
-) const {
- T val = 0;
- for(size_t i=0;i<numberOfWeights();++i){
- val += weights_->getWeight(weightIDs_[i]) * weightGradient(i,begin);
- }
- return val;
-}
-
-
-template <class T, class I, class L>
-inline L
-LSumOfExperts<T, I, L>::shape
-(
- const size_t i
-) const {
- return shape_[i];
-}
-
-template <class T, class I, class L>
-inline size_t
-LSumOfExperts<T, I, L>::dimension() const {
- return shape_.size();
-}
-
-template <class T, class I, class L>
-inline size_t
-LSumOfExperts<T, I, L>::size() const {
- size_t s = 1;
- for(size_t i=0; i<dimension(); ++i)
- s *=shape_[i];
- return s;
-}
-
-} // namespace learnable
-} // namespace functions
-
-
-/// FunctionSerialization
-template<class T, class I, class L>
-class FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> > {
-public:
- typedef typename opengm::functions::learnable::LSumOfExperts<T, I, L>::ValueType ValueType;
-
- static size_t indexSequenceSize(const opengm::functions::learnable::LSumOfExperts<T, I, L>&);
- static size_t valueSequenceSize(const opengm::functions::learnable::LSumOfExperts<T, I, L>&);
- template<class INDEX_OUTPUT_ITERATOR, class VALUE_OUTPUT_ITERATOR>
- static void serialize(const opengm::functions::learnable::LSumOfExperts<T, I, L>&, INDEX_OUTPUT_ITERATOR, VALUE_OUTPUT_ITERATOR);
- template<class INDEX_INPUT_ITERATOR, class VALUE_INPUT_ITERATOR>
- static void deserialize( INDEX_INPUT_ITERATOR, VALUE_INPUT_ITERATOR, opengm::functions::learnable::LSumOfExperts<T, I, L>&);
-};
-
-template<class T, class I, class L>
-struct FunctionRegistration<opengm::functions::learnable::LSumOfExperts<T, I, L> > {
- enum ID {
- Id = opengm::FUNCTION_TYPE_ID_OFFSET + 100 + 67
- };
-};
-
-template<class T, class I, class L>
-inline size_t
-FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> >::indexSequenceSize
-(
- const opengm::functions::learnable::LSumOfExperts<T, I, L> & src
-) {
- return 1+src.shape_.size()+1+src.weightIDs_.size();
-}
-
-template<class T, class I, class L>
-inline size_t
-FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> >::valueSequenceSize
-(
- const opengm::functions::learnable::LSumOfExperts<T, I, L> & src
-) {
- return src.feat_.size()*src.size();
-}
-
-template<class T, class I, class L>
-template<class INDEX_OUTPUT_ITERATOR, class VALUE_OUTPUT_ITERATOR >
-inline void
-FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> >::serialize
-(
- const opengm::functions::learnable::LSumOfExperts<T, I, L> & src,
- INDEX_OUTPUT_ITERATOR indexOutIterator,
- VALUE_OUTPUT_ITERATOR valueOutIterator
-) {
- // save shape
- *indexOutIterator = src.shape_.size();
- ++indexOutIterator;
- for(size_t i=0; i<src.shape_.size();++i){
- *indexOutIterator = src.shape_[i];
- ++indexOutIterator;
- }
- //save parameter ids
- *indexOutIterator = src.weightIDs_.size();
- ++indexOutIterator;
- for(size_t i=0; i<src.weightIDs_.size();++i){
- *indexOutIterator = src.weightIDs_[i];
- ++indexOutIterator;
- }
-
- OPENGM_ASSERT_OP(src.weightIDs_.size(), ==, src.feat_.size());
-
- // save features
- for(size_t i=0; i<src.weightIDs_.size();++i){
- for(size_t j=0; j<src.feat_[i].size();++j){
- *valueOutIterator = src.feat_[i](j);
- ++valueOutIterator;
- }
- }
-}
-
-template<class T, class I, class L>
-template<class INDEX_INPUT_ITERATOR, class VALUE_INPUT_ITERATOR >
-inline void
-FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> >::deserialize
-(
- INDEX_INPUT_ITERATOR indexInIterator,
- VALUE_INPUT_ITERATOR valueInIterator,
- opengm::functions::learnable::LSumOfExperts<T, I, L> & dst
-) {
- //read shape
- size_t dim = *indexInIterator;
- size_t size = 1;
- ++indexInIterator;
- std::vector<L> shape(dim);
- for(size_t i=0; i<dim;++i){
- shape[i] = *indexInIterator;
- size *= *indexInIterator;
- ++indexInIterator;
- }
- //read parameter ids
- size_t numW =*indexInIterator;
- ++indexInIterator;
- std::vector<size_t> parameterIDs(numW);
- for(size_t i=0; i<numW;++i){
- parameterIDs[i] = *indexInIterator;
- ++indexInIterator;
- }
- //read features
- std::vector<marray::Marray<T> > feat(numW,marray::Marray<T>(shape.begin(),shape.end()));
- for(size_t i=0; i<numW;++i){
- for(size_t j=0; j<size;++j){
- feat[i](j)=*valueInIterator;
- ++valueInIterator;
- }
- }
-}
-
-} // namespace opengm
-
-#endif // #ifndef OPENGM_LEARNABLE_FUNCTION_HXX
diff --git a/include/opengm/learning/dataset/testdatasets.hxx b/include/opengm/learning/dataset/testdatasets.hxx
index c5f20f8..e2f1e85 100644
--- a/include/opengm/learning/dataset/testdatasets.hxx
+++ b/include/opengm/learning/dataset/testdatasets.hxx
@@ -8,7 +8,7 @@
#include <opengm/learning/dataset/dataset.hxx>
#include <opengm/learning/dataset/editabledataset.hxx>
#include <opengm/functions/learnable/lpotts.hxx>
-#include <opengm/functions/learnable/lsum_of_experts.hxx>
+#include <opengm/functions/learnable/lweightedsum_of_functions.hxx>
namespace opengm {
namespace datasets{
@@ -230,7 +230,7 @@ namespace opengm {
feat[1](1) = std::fabs(val1-1);
std::vector<size_t> wID(2);
wID[0]=1; wID[1]=2;
- opengm::functions::learnable::LSumOfExperts<ValueType,IndexType,LabelType> f(shape,this->weights_, wID, feat);
+ opengm::functions::learnable::LWeightedSumOfFunctions<ValueType,IndexType,LabelType> f(shape,this->weights_, wID, feat);
typename GM::FunctionIdentifier fid = this->gms_[m].addFunction(f);
// factor
@@ -292,7 +292,7 @@ namespace opengm {
feat[1](1) = val1-1;
std::vector<size_t> wID(2);
wID[0]=0; wID[1]=1;
- opengm::functions::learnable::LSumOfExperts<ValueType,IndexType,LabelType> f(shape,this->weights_, wID, feat);
+ opengm::functions::learnable::LWeightedSumOfFunctions<ValueType,IndexType,LabelType> f(shape,this->weights_, wID, feat);
typename GM::FunctionIdentifier fid = this->gms_[m].addFunction(f);
// factor
@@ -339,7 +339,7 @@ namespace opengm {
feat[1](1) = std::fabs(val1-1);
std::vector<size_t> wID(2);
wID[0]=1; wID[1]=2;
- opengm::functions::learnable::LSumOfExperts<ValueType,IndexType,LabelType> f(shape,this->weights_, wID, feat);
+ opengm::functions::learnable::LWeightedSumOfFunctions<ValueType,IndexType,LabelType> f(shape,this->weights_, wID, feat);
typename GM::FunctionIdentifier fid = this->gms_[m].addFunction(f);
// factor
diff --git a/include/opengm/learning/maximum-likelihood-learning.hxx b/include/opengm/learning/maximum-likelihood-learning.hxx
index 15cf819..3bac158 100644
--- a/include/opengm/learning/maximum-likelihood-learning.hxx
+++ b/include/opengm/learning/maximum-likelihood-learning.hxx
@@ -8,7 +8,7 @@
//#include <opengm/functions/explicit_function.hxx>
#include <opengm/functions/view_convert_function.hxx>
//#include <opengm/functions/learnable/lpotts.hxx>
-//#include <opengm/functions/learnable/lsum_of_experts.hxx>
+//#include <opengm/functions/learnable/lweightedsum_of_functions.hxx>
#include <opengm/graphicalmodel/graphicalmodel.hxx>
//#include <opengm/inference/icm.hxx>
//
@@ -18,7 +18,7 @@
//typedef opengm::meta::TypeListGenerator<
// opengm::ExplicitFunction<ValueType,IndexType,LabelType>,
// opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>,
-// opengm::functions::learnable::LSumOfExperts<ValueType,IndexType,LabelType>
+// opengm::functions::learnable::LWeightedSumOfFunctions<ValueType,IndexType,LabelType>
//>::type FunctionListType;
//
//typedef opengm::GraphicalModel<
diff --git a/src/unittest/learning/test_dataset.cxx b/src/unittest/learning/test_dataset.cxx
index f4d5367..b84e2ec 100644
--- a/src/unittest/learning/test_dataset.cxx
+++ b/src/unittest/learning/test_dataset.cxx
@@ -9,7 +9,7 @@
#include <opengm/utilities/metaprogramming.hxx>
#include <opengm/functions/learnable/lpotts.hxx>
-#include <opengm/functions/learnable/lsum_of_experts.hxx>
+#include <opengm/functions/learnable/lweightedsum_of_functions.hxx>
//#include <opengm/learning/dataset/testdataset.hxx>
//#include <opengm/learning/dataset/testdataset2.hxx>
#include <opengm/learning/dataset/dataset_io.hxx>
@@ -23,7 +23,7 @@
typedef double ValueType;
typedef size_t IndexType;
typedef size_t LabelType;
-typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::LSumOfExperts<ValueType,IndexType,LabelType> >::type FunctionListType;
+typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::LWeightedSumOfFunctions<ValueType,IndexType,LabelType> >::type FunctionListType;
typedef opengm::GraphicalModel<ValueType,opengm::Adder, FunctionListType, opengm::DiscreteSpace<IndexType,LabelType> > GM;
//typedef opengm::datasets::TestDataset<GM> DS1;
//typedef opengm::datasets::Dataset<GM> DS;
diff --git a/src/unittest/learning/test_dataset_io.cxx b/src/unittest/learning/test_dataset_io.cxx
index aa2a2a5..4df5471 100644
--- a/src/unittest/learning/test_dataset_io.cxx
+++ b/src/unittest/learning/test_dataset_io.cxx
@@ -9,7 +9,7 @@
#include <opengm/utilities/metaprogramming.hxx>
#include <opengm/functions/learnable/lpotts.hxx>
-#include <opengm/functions/learnable/lsum_of_experts.hxx>
+#include <opengm/functions/learnable/lweightedsum_of_functions.hxx>
//#include <opengm/learning/dataset/testdataset.hxx>
//#include <opengm/learning/dataset/testdataset2.hxx>
#include <opengm/learning/dataset/dataset_io.hxx>
@@ -24,7 +24,7 @@
typedef double ValueType;
typedef size_t IndexType;
typedef size_t LabelType;
-typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::LSumOfExperts<ValueType,IndexType,LabelType> >::type FunctionListType;
+typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::LWeightedSumOfFunctions<ValueType,IndexType,LabelType> >::type FunctionListType;
typedef opengm::GraphicalModel<ValueType,opengm::Adder, FunctionListType, opengm::DiscreteSpace<IndexType,LabelType> > GM;
typedef opengm::learning::NoLoss LOSS1;
typedef opengm::learning::HammingLoss LOSS2;
diff --git a/src/unittest/learning/test_gridsearch_learner.cxx b/src/unittest/learning/test_gridsearch_learner.cxx
index cef54d8..3684cc2 100644
--- a/src/unittest/learning/test_gridsearch_learner.cxx
+++ b/src/unittest/learning/test_gridsearch_learner.cxx
@@ -9,7 +9,7 @@
#include <opengm/utilities/metaprogramming.hxx>
#include <opengm/functions/learnable/lpotts.hxx>
-#include <opengm/functions/learnable/lsum_of_experts.hxx>
+#include <opengm/functions/learnable/lweightedsum_of_functions.hxx>
#include <opengm/learning/gridsearch-learning.hxx>
#include <opengm/learning/loss/hammingloss.hxx>
//#include <opengm/learning/dataset/testdataset.hxx>
@@ -21,7 +21,7 @@
typedef double ValueType;
typedef size_t IndexType;
typedef size_t LabelType;
-typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::LSumOfExperts<ValueType,IndexType,LabelType> >::type FunctionListType;
+typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::LWeightedSumOfFunctions<ValueType,IndexType,LabelType> >::type FunctionListType;
typedef opengm::GraphicalModel<ValueType,opengm::Adder, FunctionListType, opengm::DiscreteSpace<IndexType,LabelType> > GM;
//typedef opengm::datasets::TestDataset<GM> DS;
//typedef opengm::datasets::TestDataset2<GM> DS2;
diff --git a/src/unittest/learning/test_learning.cxx b/src/unittest/learning/test_learning.cxx
index 24b7ecb..289dfdc 100644
--- a/src/unittest/learning/test_learning.cxx
+++ b/src/unittest/learning/test_learning.cxx
@@ -16,7 +16,7 @@
#include <opengm/functions/learnable/lpotts.hxx>
-#include <opengm/functions/learnable/lsum_of_experts.hxx>
+#include <opengm/functions/learnable/lweightedsum_of_functions.hxx>
#include <opengm/learning/struct-max-margin.hxx>
#include <opengm/learning/loss/hammingloss.hxx>
//#include <opengm/learning/dataset/testdataset.hxx>
@@ -29,7 +29,7 @@
typedef double ValueType;
typedef size_t IndexType;
typedef size_t LabelType;
-typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::LSumOfExperts<ValueType,IndexType,LabelType> >::type FunctionListType;
+typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::LWeightedSumOfFunctions<ValueType,IndexType,LabelType> >::type FunctionListType;
typedef opengm::GraphicalModel<ValueType,opengm::Adder, FunctionListType, opengm::DiscreteSpace<IndexType,LabelType> > GM;
typedef opengm::learning::HammingLoss LOSS;
diff --git a/src/unittest/learning/test_maximum_likelihood_learner.cxx b/src/unittest/learning/test_maximum_likelihood_learner.cxx
index 2530849..58b2862 100644
--- a/src/unittest/learning/test_maximum_likelihood_learner.cxx
+++ b/src/unittest/learning/test_maximum_likelihood_learner.cxx
@@ -23,7 +23,7 @@ typedef size_t LabelType;
typedef opengm::meta::TypeListGenerator<
opengm::ExplicitFunction<ValueType,IndexType,LabelType>,
opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>,
- opengm::functions::learnable::LSumOfExperts<ValueType,IndexType,LabelType>
+ opengm::functions::learnable::LWeightedSumOfFunctions<ValueType,IndexType,LabelType>
>::type FunctionListType;
typedef opengm::GraphicalModel<
diff --git a/src/unittest/learning/test_subgradient_ssvm.cxx b/src/unittest/learning/test_subgradient_ssvm.cxx
index 11ddfc5..fd009a0 100644
--- a/src/unittest/learning/test_subgradient_ssvm.cxx
+++ b/src/unittest/learning/test_subgradient_ssvm.cxx
@@ -14,7 +14,7 @@
#include <opengm/functions/learnable/lpotts.hxx>
-#include <opengm/functions/learnable/lsum_of_experts.hxx>
+#include <opengm/functions/learnable/lweightedsum_of_functions.hxx>
#include <opengm/learning/subgradient_ssvm.hxx>
#include <opengm/learning/loss/hammingloss.hxx>
//#include <opengm/learning/dataset/testdataset.hxx>
@@ -27,7 +27,7 @@
typedef double ValueType;
typedef size_t IndexType;
typedef size_t LabelType;
-typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::LSumOfExperts<ValueType,IndexType,LabelType> >::type FunctionListType;
+typedef opengm::meta::TypeListGenerator<opengm::ExplicitFunction<ValueType,IndexType,LabelType>, opengm::functions::learnable::LPotts<ValueType,IndexType,LabelType>, opengm::functions::learnable::LWeightedSumOfFunctions<ValueType,IndexType,LabelType> >::type FunctionListType;
typedef opengm::GraphicalModel<ValueType,opengm::Adder, FunctionListType, opengm::DiscreteSpace<IndexType,LabelType> > GM;
typedef opengm::learning::HammingLoss LOSS;
diff --git a/src/unittest/test_linear_constraint.cxx b/src/unittest/test_linear_constraint.cxx
index aa7a09f..7521458 100644
--- a/src/unittest/test_linear_constraint.cxx
+++ b/src/unittest/test_linear_constraint.cxx
@@ -36,6 +36,7 @@ int main(int argc, char** argv){
std::cout << "done..." << std::endl;
return 0;
+
}
template<class VALUE_TYPE, class INDEX_TYPE, class LABEL_TYPE>
--
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