[opengm] 270/386: fix ambiguities in names
Ghislain Vaillant
ghisvail-guest at moszumanska.debian.org
Wed Aug 31 08:38:08 UTC 2016
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ghisvail-guest pushed a commit to branch debian/master
in repository opengm.
commit 5d98b60e6d609030cd0d8c052c260c819c12f682
Author: joergkappes <kappes at math.uni-heidelberg.de>
Date: Tue Jul 7 11:25:03 2015 +0200
fix ambiguities in names
---
.../opengm/functions/learnable/lsum_of_experts.hxx | 1 +
...f_experts.hxx => lweightedsum_of_functions.hxx} | 67 +++++++++++-----------
2 files changed, 35 insertions(+), 33 deletions(-)
diff --git a/include/opengm/functions/learnable/lsum_of_experts.hxx b/include/opengm/functions/learnable/lsum_of_experts.hxx
index bb366a6..b86dedb 100644
--- a/include/opengm/functions/learnable/lsum_of_experts.hxx
+++ b/include/opengm/functions/learnable/lsum_of_experts.hxx
@@ -1,6 +1,7 @@
#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>
diff --git a/include/opengm/functions/learnable/lsum_of_experts.hxx b/include/opengm/functions/learnable/lweightedsum_of_functions.hxx
similarity index 72%
copy from include/opengm/functions/learnable/lsum_of_experts.hxx
copy to include/opengm/functions/learnable/lweightedsum_of_functions.hxx
index bb366a6..f831329 100644
--- a/include/opengm/functions/learnable/lsum_of_experts.hxx
+++ b/include/opengm/functions/learnable/lweightedsum_of_functions.hxx
@@ -1,6 +1,6 @@
#pragma once
-#ifndef OPENGM_LEARNABLE_LSUM_OF_EXPERTS_FUNCTION_HXX
-#define OPENGM_LEARNABLE_LSUM_OF_EXPERTS_FUNCTION_HXX
+#ifndef OPENGM_LEARNABLE_LWEIGHTEDSUM_OF_FUNCTIONS_FUNCTION_HXX
+#define OPENGM_LEARNABLE_LWEIGHTEDSUM_OF_FUNCTIONS_FUNCTION_HXX
#include <algorithm>
#include <vector>
@@ -16,25 +16,25 @@ namespace opengm {
namespace functions {
namespace learnable {
-/// Learnable feature function for two variables
+/// Learnable weighted sum of feature-functions
///
/// f(x) = \sum_i w(i) * feat(i)(x)
/// - w = parameter vector
-/// - feat = feature vector
+/// - feat = feature-function 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>
+class LWeightedSumOfFunctions
+ : public opengm::FunctionBase<opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L>, T, I, L>
{
public:
typedef T ValueType;
typedef L LabelType;
typedef I IndexType;
- LSumOfExperts();
- LSumOfExperts(const std::vector<L>& shape,
+ LWeightedSumOfFunctions();
+ LWeightedSumOfFunctions(const std::vector<L>& shape,
const opengm::learning::Weights<T>& weights,
const std::vector<size_t>& weightIDs,
const std::vector<marray::Marray<T> >& feat
@@ -61,13 +61,13 @@ protected:
std::vector<size_t> weightIDs_;
std::vector<marray::Marray<T> > feat_;
- friend class opengm::FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> >;
+ friend class opengm::FunctionSerialization<opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L> >;
};
template <class T, class I, class L>
inline
-LSumOfExperts<T, I, L>::LSumOfExperts
+LWeightedSumOfFunctions<T, I, L>::LWeightedSumOfFunctions
(
const std::vector<L>& shape,
const opengm::learning::Weights<T>& weights,
@@ -76,13 +76,14 @@ LSumOfExperts<T, I, L>::LSumOfExperts
)
: shape_(shape), weights_(&weights), weightIDs_(weightIDs),feat_(feat)
{
- OPENGM_ASSERT( size() == feat_[0].size() );
OPENGM_ASSERT( weightIDs_.size() == feat_.size() );
+ for(size_t i=0 i<weightIDs_.size();++1)
+ OPENGM_ASSERT( size() == feat_[i].size() );
}
template <class T, class I, class L>
inline
-LSumOfExperts<T, I, L>::LSumOfExperts()
+LWeightedSumOfFunctions<T, I, L>::LWeightedSumOfFunctions()
: shape_(std::vector<L>(0)), weightIDs_(std::vector<size_t>(0)), feat_(std::vector<marray::Marray<T> >(0))
{
;
@@ -92,7 +93,7 @@ LSumOfExperts<T, I, L>::LSumOfExperts()
template <class T, class I, class L>
template <class ITERATOR>
inline T
-LSumOfExperts<T, I, L>::weightGradient
+LWeightedSumOfFunctions<T, I, L>::weightGradient
(
size_t weightNumber,
ITERATOR begin
@@ -104,7 +105,7 @@ LSumOfExperts<T, I, L>::weightGradient
template <class T, class I, class L>
template <class ITERATOR>
inline T
-LSumOfExperts<T, I, L>::operator()
+LWeightedSumOfFunctions<T, I, L>::operator()
(
ITERATOR begin
) const {
@@ -118,7 +119,7 @@ LSumOfExperts<T, I, L>::operator()
template <class T, class I, class L>
inline L
-LSumOfExperts<T, I, L>::shape
+LWeightedSumOfFunctions<T, I, L>::shape
(
const size_t i
) const {
@@ -127,13 +128,13 @@ LSumOfExperts<T, I, L>::shape
template <class T, class I, class L>
inline size_t
-LSumOfExperts<T, I, L>::dimension() const {
+LWeightedSumOfFunctions<T, I, L>::dimension() const {
return shape_.size();
}
template <class T, class I, class L>
inline size_t
-LSumOfExperts<T, I, L>::size() const {
+LWeightedSumOfFunctions<T, I, L>::size() const {
size_t s = 1;
for(size_t i=0; i<dimension(); ++i)
s *=shape_[i];
@@ -146,20 +147,20 @@ LSumOfExperts<T, I, L>::size() const {
/// FunctionSerialization
template<class T, class I, class L>
-class FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> > {
+class FunctionSerialization<opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L> > {
public:
- typedef typename opengm::functions::learnable::LSumOfExperts<T, I, L>::ValueType ValueType;
+ typedef typename opengm::functions::learnable::LWeightedSumOfFunctions<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>&);
+ static size_t indexSequenceSize(const opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L>&);
+ static size_t valueSequenceSize(const opengm::functions::learnable::LWeightedSumOfFunctions<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);
+ static void serialize(const opengm::functions::learnable::LWeightedSumOfFunctions<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>&);
+ static void deserialize( INDEX_INPUT_ITERATOR, VALUE_INPUT_ITERATOR, opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L>&);
};
template<class T, class I, class L>
-struct FunctionRegistration<opengm::functions::learnable::LSumOfExperts<T, I, L> > {
+struct FunctionRegistration<opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L> > {
enum ID {
Id = opengm::FUNCTION_TYPE_ID_OFFSET + 100 + 67
};
@@ -167,18 +168,18 @@ struct FunctionRegistration<opengm::functions::learnable::LSumOfExperts<T, I, L>
template<class T, class I, class L>
inline size_t
-FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> >::indexSequenceSize
+FunctionSerialization<opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L> >::indexSequenceSize
(
- const opengm::functions::learnable::LSumOfExperts<T, I, L> & src
+ const opengm::functions::learnable::LWeightedSumOfFunctions<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
+FunctionSerialization<opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L> >::valueSequenceSize
(
- const opengm::functions::learnable::LSumOfExperts<T, I, L> & src
+ const opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L> & src
) {
return src.feat_.size()*src.size();
}
@@ -186,9 +187,9 @@ FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> >::va
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
+FunctionSerialization<opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L> >::serialize
(
- const opengm::functions::learnable::LSumOfExperts<T, I, L> & src,
+ const opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L> & src,
INDEX_OUTPUT_ITERATOR indexOutIterator,
VALUE_OUTPUT_ITERATOR valueOutIterator
) {
@@ -221,11 +222,11 @@ FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> >::se
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
+FunctionSerialization<opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L> >::deserialize
(
INDEX_INPUT_ITERATOR indexInIterator,
VALUE_INPUT_ITERATOR valueInIterator,
- opengm::functions::learnable::LSumOfExperts<T, I, L> & dst
+ opengm::functions::learnable::LWeightedSumOfFunctions<T, I, L> & dst
) {
//read shape
size_t dim = *indexInIterator;
@@ -257,4 +258,4 @@ FunctionSerialization<opengm::functions::learnable::LSumOfExperts<T, I, L> >::de
} // namespace opengm
-#endif // #ifndef OPENGM_LEARNABLE_FUNCTION_HXX
+#endif //OPENGM_LEARNABLE_LWEIGHTEDSUM_OF_FUNCTIONS_FUNCTION_HXX
--
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