From b187c9a635b3fae949efe8176e9eec44c3bf04ae Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?C=C3=A9dric=20Traizet?= <traizetc@cesbio.cnes.fr>
Date: Wed, 17 May 2017 13:57:26 +0200
Subject: [PATCH] confidence map removed
---
app/cbDimensionalityReduction.cxx | 18 +---
include/AutoencoderModel.h | 9 +-
include/AutoencoderModel.txx | 6 +-
include/DimensionalityReductionModel.h | 39 ++------
include/DimensionalityReductionModel.txx | 95 ++++++-------------
.../DimensionalityReductionModelFactory.txx | 9 +-
.../ImageDimensionalityReductionFilter.txx | 35 +------
include/PCAModel.h | 9 +-
include/PCAModel.txx | 6 +-
include/SOMModel.h | 7 +-
include/SOMModel.txx | 6 +-
11 files changed, 66 insertions(+), 173 deletions(-)
diff --git a/app/cbDimensionalityReduction.cxx b/app/cbDimensionalityReduction.cxx
index 7b1063147b..6d5366e9eb 100644
--- a/app/cbDimensionalityReduction.cxx
+++ b/app/cbDimensionalityReduction.cxx
@@ -145,8 +145,6 @@ private:
AddParameter(ParameterType_InputImage, "in", "Input Image");
SetParameterDescription( "in", "The input image to predict.");
- // TODO : use CSV input/output ?
-
AddParameter(ParameterType_InputImage, "mask", "Input Mask");
SetParameterDescription( "mask", "The mask allow restricting "
"classification of the input image to the area where mask pixel values "
@@ -200,7 +198,6 @@ private:
}
m_Model->Load(GetParameterString("model"));
- m_Model->SetRegressionMode(true);
otbAppLogINFO("Model loaded");
// Classify
@@ -218,25 +215,14 @@ private:
MeasurementType meanMeasurementVector;
MeasurementType stddevMeasurementVector;
m_Rescaler = RescalerType::New();
+
// Load input image statistics
statisticsReader->SetFileName(GetParameterString("imstat"));
meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
otbAppLogINFO( "mean used: " << meanMeasurementVector );
otbAppLogINFO( "standard deviation used: " << stddevMeasurementVector );
- /*if (meanMeasurementVector.Size() == nbFeatures + 1)
- {
- double outMean = meanMeasurementVector[nbFeatures];
- double outStdDev = stddevMeasurementVector[nbFeatures];
- meanMeasurementVector.SetSize(nbFeatures,false);
- stddevMeasurementVector.SetSize(nbFeatures,false);
- m_OutRescaler = OutputRescalerType::New();
- m_OutRescaler->SetInput(m_ClassificationFilter->GetOutput());
- m_OutRescaler->GetFunctor().SetA(outStdDev);
- m_OutRescaler->GetFunctor().SetB(outMean);
- outputImage = m_OutRescaler->GetOutput();
- }
- else*/ if (meanMeasurementVector.Size() != nbFeatures)
+ if (meanMeasurementVector.Size() != nbFeatures)
{
otbAppLogFATAL("Wrong number of components in statistics file : "<<meanMeasurementVector.Size());
}
diff --git a/include/AutoencoderModel.h b/include/AutoencoderModel.h
index 7ebb95de6f..eb65fdb1df 100644
--- a/include/AutoencoderModel.h
+++ b/include/AutoencoderModel.h
@@ -23,10 +23,7 @@ public:
typedef typename Superclass::TargetValueType TargetValueType;
typedef typename Superclass::TargetSampleType TargetSampleType;
typedef typename Superclass::TargetListSampleType TargetListSampleType;
- typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
- typedef typename Superclass::ConfidenceSampleType ConfidenceSampleType;
- typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
-
+
itkNewMacro(Self);
itkTypeMacro(AutoencoderModel, DimensionalityReductionModel);
@@ -54,8 +51,8 @@ protected:
AutoencoderModel();
~AutoencoderModel() ITK_OVERRIDE;
- virtual TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType *quality=ITK_NULLPTR) const ITK_OVERRIDE;
- virtual void DoPredictBatch(const InputListSampleType *, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType *, ConfidenceListSampleType * = ITK_NULLPTR) const ITK_OVERRIDE;
+ virtual TargetSampleType DoPredict(const InputSampleType& input) const ITK_OVERRIDE;
+ virtual void DoPredictBatch(const InputListSampleType *, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType *) const ITK_OVERRIDE;
private:
diff --git a/include/AutoencoderModel.txx b/include/AutoencoderModel.txx
index b0e206532f..7f2b451647 100644
--- a/include/AutoencoderModel.txx
+++ b/include/AutoencoderModel.txx
@@ -19,7 +19,7 @@ namespace otb
template <class TInputValue, class AutoencoderType>
AutoencoderModel<TInputValue,AutoencoderType>::AutoencoderModel()
{
- this->m_IsRegressionSupported = true;
+ this->m_IsDoPredictBatchMultiThreaded = true;
}
@@ -117,7 +117,7 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Load(const std::string & fil
template <class TInputValue, class AutoencoderType>
typename AutoencoderModel<TInputValue,AutoencoderType>::TargetSampleType
-AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType & value, ConfidenceValueType *quality) const
+AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType & value) const
{
shark::RealVector samples(value.Size());
for(size_t i = 0; i < value.Size();i++)
@@ -143,7 +143,7 @@ AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType &
template <class TInputValue, class AutoencoderType>
void AutoencoderModel<TInputValue,AutoencoderType>
-::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets, ConfidenceListSampleType * quality) const
+::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets) const
{
std::cout << "BATCH" << std::endl;
std::vector<shark::RealVector> features;
diff --git a/include/DimensionalityReductionModel.h b/include/DimensionalityReductionModel.h
index ba9888b1fb..4f12b1e2ef 100644
--- a/include/DimensionalityReductionModel.h
+++ b/include/DimensionalityReductionModel.h
@@ -62,7 +62,7 @@ namespace otb
*
* \ingroup OTBSupervised
*/
-template <class TInputValue, class TTargetValue, class TConfidenceValue = double >
+template <class TInputValue, class TTargetValue>
class ITK_EXPORT DimensionalityReductionModel
: public itk::Object
{
@@ -89,11 +89,6 @@ public:
typedef itk::Statistics::ListSample<TargetSampleType> TargetListSampleType;
//@}
- /**\name Confidence value typedef */
- typedef TConfidenceValue ConfidenceValueType;
- typedef itk::FixedArray<ConfidenceValueType,1> ConfidenceSampleType;
- typedef itk::Statistics::ListSample<ConfidenceSampleType> ConfidenceListSampleType;
-
/**\name Standard macros */
//@{
/** Run-time type information (and related methods). */
@@ -109,7 +104,7 @@ public:
* quality value, or NULL
* \return The predicted label
*/
- TargetSampleType Predict(const InputSampleType& input, ConfidenceValueType *quality = ITK_NULLPTR) const;
+ TargetSampleType Predict(const InputSampleType& input) const;
@@ -121,7 +116,7 @@ public:
* Note that this method will be multi-threaded if OTB is built
* with OpenMP.
*/
- typename TargetListSampleType::Pointer PredictBatch(const InputListSampleType * input, ConfidenceListSampleType * quality = ITK_NULLPTR) const;
+ typename TargetListSampleType::Pointer PredictBatch(const InputListSampleType * input) const;
/** THIS METHOD IS DEPRECATED AND SHOULD NOT BE USED. */
void PredictAll();
@@ -147,9 +142,6 @@ public:
virtual bool CanWriteFile(const std::string &) = 0;
//@}
- /** Query capacity to produce a confidence index */
- bool HasConfidenceIndex() const {return m_ConfidenceIndex;}
-
/**\name Input list of samples accessors */
//@{
itkSetObjectMacro(InputListSample,InputListSampleType);
@@ -165,13 +157,7 @@ public:
itkGetObjectMacro(TargetListSample,TargetListSampleType);
//@}
- itkGetObjectMacro(ConfidenceListSample,ConfidenceListSampleType);
-
- /**\name Use model in regression mode */
- //@{
- itkGetMacro(RegressionMode,bool);
- void SetRegressionMode(bool flag);
- //@}
+
protected:
/** Constructor */
@@ -189,19 +175,6 @@ protected:
/** Target list sample */
typename TargetListSampleType::Pointer m_TargetListSample;
- typename ConfidenceListSampleType::Pointer m_ConfidenceListSample;
-
- /** flag to choose between classification and regression modes */
- bool m_RegressionMode;
-
- /** flag that indicates if the model supports regression, child
- * classes should modify it in their constructor if they support
- * regression mode */
- bool m_IsRegressionSupported;
-
- /** flag that tells if the model support confidence index output */
- bool m_ConfidenceIndex;
-
/** Is DoPredictBatch multi-threaded ? */
bool m_IsDoPredictBatchMultiThreaded;
@@ -225,7 +198,7 @@ private:
* Also set m_IsDoPredictBatchMultiThreaded to true if internal
* implementation allows for parallel batch prediction.
*/
- virtual void DoPredictBatch(const InputListSampleType * input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * target, ConfidenceListSampleType * quality = ITK_NULLPTR) const;
+ virtual void DoPredictBatch(const InputListSampleType * input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * target) const;
/** Actual implementation of single sample prediction
* \param input sample to predict
@@ -233,7 +206,7 @@ private:
* or NULL
* \return The predicted label
*/
- virtual TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType * quality= ITK_NULLPTR) const = 0;
+ virtual TargetSampleType DoPredict(const InputSampleType& input) const = 0;
DimensionalityReductionModel(const Self &); //purposely not implemented
void operator =(const Self&); //purposely not implemented
diff --git a/include/DimensionalityReductionModel.txx b/include/DimensionalityReductionModel.txx
index 68db8019a3..ba20ee8e28 100644
--- a/include/DimensionalityReductionModel.txx
+++ b/include/DimensionalityReductionModel.txx
@@ -29,40 +29,21 @@
namespace otb
{
-template <class TInputValue, class TOutputValue, class TConfidenceValue>
-DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
-::DimensionalityReductionModel() :
- m_RegressionMode(false),
- m_IsRegressionSupported(false),
- m_ConfidenceIndex(false),
- m_IsDoPredictBatchMultiThreaded(false)
-{}
+template <class TInputValue, class TOutputValue>
+DimensionalityReductionModel<TInputValue,TOutputValue>
+::DimensionalityReductionModel()
+{ this->m_IsDoPredictBatchMultiThreaded=false;}
-template <class TInputValue, class TOutputValue, class TConfidenceValue>
-DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
+template <class TInputValue, class TOutputValue>
+DimensionalityReductionModel<TInputValue,TOutputValue>
::~DimensionalityReductionModel()
{}
-template <class TInputValue, class TOutputValue, class TConfidenceValue>
-void
-DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
-::SetRegressionMode(bool flag)
-{
- if (flag && !m_IsRegressionSupported)
- {
- itkGenericExceptionMacro(<< "Regression mode not implemented.");
- }
- if (m_RegressionMode != flag)
- {
- m_RegressionMode = flag;
- this->Modified();
- }
-}
-template <class TInputValue, class TOutputValue, class TConfidenceValue>
+template <class TInputValue, class TOutputValue>
void
-DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
+DimensionalityReductionModel<TInputValue,TOutputValue>
::PredictAll()
{
itkWarningMacro("DimensionalityReductionModel::PredictAll() has been DEPRECATED. Use DimensionalityReductionModel::PredictBatch() instead.");
@@ -75,36 +56,31 @@ DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
targets->Graft(tmpTargets);
}
-template <class TInputValue, class TOutputValue, class TConfidenceValue>
-typename DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
+template <class TInputValue, class TOutputValue>
+typename DimensionalityReductionModel<TInputValue,TOutputValue>
::TargetSampleType
-DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
-::Predict(const InputSampleType& input, ConfidenceValueType *quality) const
+DimensionalityReductionModel<TInputValue,TOutputValue>
+::Predict(const InputSampleType& input) const
{
// Call protected specialization entry point
- return this->DoPredict(input,quality);
+ return this->DoPredict(input);
}
-template <class TInputValue, class TOutputValue, class TConfidenceValue>
-typename DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
+template <class TInputValue, class TOutputValue>
+typename DimensionalityReductionModel<TInputValue,TOutputValue>
::TargetListSampleType::Pointer
-DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
-::PredictBatch(const InputListSampleType * input, ConfidenceListSampleType * quality) const
+DimensionalityReductionModel<TInputValue,TOutputValue>
+::PredictBatch(const InputListSampleType * input) const
{
typename TargetListSampleType::Pointer targets = TargetListSampleType::New();
targets->Resize(input->Size());
- if(quality!=ITK_NULLPTR)
- {
- quality->Clear();
- quality->Resize(input->Size());
- }
if(m_IsDoPredictBatchMultiThreaded)
{
// Simply calls DoPredictBatch
- this->DoPredictBatch(input,0,input->Size(),targets,quality);
+ this->DoPredictBatch(input,0,input->Size(),targets);
return targets;
}
else
@@ -131,11 +107,11 @@ DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
batch_size+=input->Size()%nb_batches;
}
- this->DoPredictBatch(input,batch_start,batch_size,targets,quality);
+ this->DoPredictBatch(input,batch_start,batch_size,targets);
}
}
#else
- this->DoPredictBatch(input,0,input->Size(),targets,quality);
+ this->DoPredictBatch(input,0,input->Size(),targets);
#endif
return targets;
}
@@ -143,10 +119,10 @@ DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
-template <class TInputValue, class TOutputValue, class TConfidenceValue>
+template <class TInputValue, class TOutputValue>
void
-DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
-::DoPredictBatch(const InputListSampleType * input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets, ConfidenceListSampleType * quality) const
+DimensionalityReductionModel<TInputValue,TOutputValue>
+::DoPredictBatch(const InputListSampleType * input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets) const
{
assert(input != ITK_NULLPTR);
assert(targets != ITK_NULLPTR);
@@ -159,29 +135,18 @@ DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
itkExceptionMacro(<<"requested range ["<<startIndex<<", "<<startIndex+size<<"[ partially outside input sample list range.[0,"<<input->Size()<<"[");
}
- if(quality != ITK_NULLPTR)
- {
- for(unsigned int id = startIndex;id<startIndex+size;++id)
- {
- ConfidenceValueType confidence = 0;
- const TargetSampleType target = this->DoPredict(input->GetMeasurementVector(id),&confidence);
- quality->SetMeasurementVector(id,confidence);
- targets->SetMeasurementVector(id,target);
- }
- }
- else
+
+ for(unsigned int id = startIndex;id<startIndex+size;++id)
{
- for(unsigned int id = startIndex;id<startIndex+size;++id)
- {
- const TargetSampleType target = this->DoPredict(input->GetMeasurementVector(id));
- targets->SetMeasurementVector(id,target);
- }
+ const TargetSampleType target = this->DoPredict(input->GetMeasurementVector(id));
+ targets->SetMeasurementVector(id,target);
}
+
}
-template <class TInputValue, class TOutputValue, class TConfidenceValue>
+template <class TInputValue, class TOutputValue>
void
-DimensionalityReductionModel<TInputValue,TOutputValue,TConfidenceValue>
+DimensionalityReductionModel<TInputValue,TOutputValue>
::PrintSelf(std::ostream& os, itk::Indent indent) const
{
// Call superclass implementation
diff --git a/include/DimensionalityReductionModelFactory.txx b/include/DimensionalityReductionModelFactory.txx
index 585af208aa..5fba93346e 100644
--- a/include/DimensionalityReductionModelFactory.txx
+++ b/include/DimensionalityReductionModelFactory.txx
@@ -42,6 +42,9 @@ template <class TInputValue, class TTargetValue>
using TiedAutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::TiedAutoencoder< shark::TanhNeuron, shark::LinearNeuron>> ;
+template <class TInputValue, class TTargetValue>
+using SOM3DModelFactory = SOMModelFactory<TInputValue, TTargetValue, 2> ;
+
template <class TInputValue, class TOutputValue>
typename DimensionalityReductionModel<TInputValue,TOutputValue>::Pointer
DimensionalityReductionModelFactory<TInputValue,TOutputValue>
@@ -99,7 +102,7 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
- RegisterFactory(SOMModelFactory<TInputValue,TOutputValue>::New());
+ RegisterFactory(SOM3DModelFactory<TInputValue,TOutputValue>::New());
#ifdef OTB_USE_SHARK
RegisterFactory(PCAModelFactory<TInputValue,TOutputValue>::New());
@@ -135,8 +138,8 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
{
// SOM
- SOMModelFactory<TInputValue,TOutputValue> *somFactory =
- dynamic_cast<SOMModelFactory<TInputValue,TOutputValue> *>(*itFac);
+ SOM3DModelFactory<TInputValue,TOutputValue> *somFactory =
+ dynamic_cast<SOM3DModelFactory<TInputValue,TOutputValue> *>(*itFac);
if (somFactory)
{
itk::ObjectFactoryBase::UnRegisterFactory(somFactory);
diff --git a/include/ImageDimensionalityReductionFilter.txx b/include/ImageDimensionalityReductionFilter.txx
index 9f5b34cab2..9060270636 100644
--- a/include/ImageDimensionalityReductionFilter.txx
+++ b/include/ImageDimensionalityReductionFilter.txx
@@ -144,8 +144,6 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
::BatchThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, itk::ThreadIdType threadId)
{
- bool computeConfidenceMap(m_UseConfidenceMap && m_Model->HasConfidenceIndex()
- && !m_Model->GetRegressionMode());
// Get the input pointers
InputImageConstPointerType inputPtr = this->GetInput();
MaskImageConstPointerType inputMaskPtr = this->GetInputMask();
@@ -172,8 +170,7 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
typedef typename ModelType::TargetListSampleType TargetListSampleType;
// typedef typename ModelType::ConfidenceValueType ConfidenceValueType;
// typedef typename ModelType::ConfidenceSampleType ConfidenceSampleType;
- typedef typename ModelType::ConfidenceListSampleType ConfidenceListSampleType;
-
+
typename InputListSampleType::Pointer samples = InputListSampleType::New();
unsigned int num_features = inputPtr->GetNumberOfComponentsPerPixel();
samples->SetMeasurementVectorSize(num_features);
@@ -193,45 +190,23 @@ ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>
}
//Make the batch prediction
typename TargetListSampleType::Pointer labels;
- typename ConfidenceListSampleType::Pointer confidences;
- if(computeConfidenceMap)
- confidences = ConfidenceListSampleType::New();
-
+
// This call is threadsafe
//labels = m_Model->PredictBatch(samples,confidences);
labels = m_Model->PredictBatch(samples);
// Set the output values
- ConfidenceMapIteratorType confidenceIt;
- if (computeConfidenceMap)
- {
- confidenceIt = ConfidenceMapIteratorType(confidencePtr,outputRegionForThread);
- confidenceIt.GoToBegin();
- }
+
typename TargetListSampleType::ConstIterator labIt = labels->Begin();
for (outIt.GoToBegin(); !outIt.IsAtEnd(); ++outIt)
{
- double confidenceIndex = 0.0;
-
+
itk::VariableLengthVector<TargetValueType> labelValue;
-
+
labelValue = labIt.GetMeasurementVector();
-
- if(computeConfidenceMap)
- {
- confidenceIndex = confidences->GetMeasurementVector(labIt.GetInstanceIdentifier())[0];
- }
-
++labIt;
-
outIt.Set(labelValue);
- if(computeConfidenceMap)
- {
- confidenceIt.Set(confidenceIndex);
- ++confidenceIt;
- }
-
progress.CompletedPixel();
}
}
diff --git a/include/PCAModel.h b/include/PCAModel.h
index 4ea1370a9f..45693dfc8a 100644
--- a/include/PCAModel.h
+++ b/include/PCAModel.h
@@ -23,10 +23,7 @@ public:
typedef typename Superclass::TargetValueType TargetValueType;
typedef typename Superclass::TargetSampleType TargetSampleType;
typedef typename Superclass::TargetListSampleType TargetListSampleType;
- typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
- typedef typename Superclass::ConfidenceSampleType ConfidenceSampleType;
- typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
-
+
itkNewMacro(Self);
itkTypeMacro(PCAModel, DimensionalityReductionModel);
@@ -47,8 +44,8 @@ protected:
PCAModel();
~PCAModel() ITK_OVERRIDE;
- virtual TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType *quality=ITK_NULLPTR) const ITK_OVERRIDE;
- virtual void DoPredictBatch(const InputListSampleType *, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType *, ConfidenceListSampleType * = ITK_NULLPTR) const ITK_OVERRIDE;
+ virtual TargetSampleType DoPredict(const InputSampleType& input) const ITK_OVERRIDE;
+ virtual void DoPredictBatch(const InputListSampleType *, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType *) const ITK_OVERRIDE;
private:
shark::LinearModel<> m_encoder;
diff --git a/include/PCAModel.txx b/include/PCAModel.txx
index 92fdc3ee4c..8f48040388 100644
--- a/include/PCAModel.txx
+++ b/include/PCAModel.txx
@@ -19,7 +19,7 @@ namespace otb
template <class TInputValue>
PCAModel<TInputValue>::PCAModel()
{
- this->m_IsRegressionSupported = true;
+ this->m_IsDoPredictBatchMultiThreaded = true;
}
@@ -100,7 +100,7 @@ void PCAModel<TInputValue>::Load(const std::string & filename, const std::string
template <class TInputValue>
typename PCAModel<TInputValue>::TargetSampleType
-PCAModel<TInputValue>::DoPredict(const InputSampleType & value, ConfidenceValueType *quality) const
+PCAModel<TInputValue>::DoPredict(const InputSampleType & value) const
{
shark::RealVector samples(value.Size());
for(size_t i = 0; i < value.Size();i++)
@@ -126,7 +126,7 @@ PCAModel<TInputValue>::DoPredict(const InputSampleType & value, ConfidenceValueT
template <class TInputValue>
void PCAModel<TInputValue>
-::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets, ConfidenceListSampleType * quality) const
+::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets) const
{
std::vector<shark::RealVector> features;
diff --git a/include/SOMModel.h b/include/SOMModel.h
index b7adddd663..bec029b9a4 100644
--- a/include/SOMModel.h
+++ b/include/SOMModel.h
@@ -15,7 +15,7 @@
namespace otb
{
-template <class TInputValue>
+template <class TInputValue, unsigned int MapDimension>>
class ITK_EXPORT SOMModel: public DimensionalityReductionModel<TInputValue,TInputValue>
{
@@ -33,9 +33,6 @@ public:
typedef typename Superclass::TargetValueType TargetValueType;
typedef typename Superclass::TargetSampleType TargetSampleType;
typedef typename Superclass::TargetListSampleType TargetListSampleType;
- typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
- typedef typename Superclass::ConfidenceSampleType ConfidenceSampleType;
- typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
typedef SOMMap<itk::VariableLengthVector<TInputValue>,itk::Statistics::EuclideanDistanceMetric<itk::VariableLengthVector<TInputValue>>, 3> MapType;
typedef typename MapType::SizeType SizeType;
@@ -85,7 +82,7 @@ protected:
SOMModel();
~SOMModel() ITK_OVERRIDE;
- virtual TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType *quality=ITK_NULLPTR) const ITK_OVERRIDE;
+ virtual TargetSampleType DoPredict(const InputSampleType& input) const ITK_OVERRIDE;
private:
typename MapType::Pointer m_SOMMap;
diff --git a/include/SOMModel.txx b/include/SOMModel.txx
index 5c6be685ba..872cd34c02 100644
--- a/include/SOMModel.txx
+++ b/include/SOMModel.txx
@@ -14,7 +14,6 @@ namespace otb
template <class TInputValue>
SOMModel<TInputValue>::SOMModel()
{
- this->m_IsRegressionSupported = true;
}
@@ -42,6 +41,7 @@ void SOMModel<TInputValue>::Train()
estimator->Update();
m_SOMMap = estimator->GetOutput();
+ std::cout << "dr of the first sample : " << m_SOMMap->GetWinner(m_ListSample->GetMeasurementVector(0)) << std::endl;
}
@@ -95,14 +95,14 @@ void SOMModel<TInputValue>::Load(const std::string & filename, const std::string
template <class TInputValue>
typename SOMModel<TInputValue>::TargetSampleType
-SOMModel<TInputValue>::DoPredict(const InputSampleType & value, ConfidenceValueType *quality) const
+SOMModel<TInputValue>::DoPredict(const InputSampleType & value) const
{
unsigned int dimension =MapType::ImageDimension;
TargetSampleType target;
target.SetSize(dimension);
auto winner =m_SOMMap->GetWinner(value);
-
+ // std::cout << winner << std::endl;
for (int i=0; i< dimension ;i++) {
target[i] = winner.GetElement(i);
--
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