diff --git a/app/cbDimensionalityReduction.cxx b/app/cbDimensionalityReduction.cxx
index 106f609d48078a1e5cee94ed5276571dd589e61c..6a8ed36866d842a9ea28c30497e13301a9c18ecf 100644
--- a/app/cbDimensionalityReduction.cxx
+++ b/app/cbDimensionalityReduction.cxx
@@ -222,7 +222,7 @@ private:
}
// Rescale vector image
- m_Rescaler->SetScale(stddevMeasurementVector);
+ m_Rescaler->SetScale(stddevMeasurementVector*5);
m_Rescaler->SetShift(meanMeasurementVector);
m_Rescaler->SetInput(inImage);
diff --git a/app/cbDimensionalityReductionTrainer.cxx b/app/cbDimensionalityReductionTrainer.cxx
index aee6fb3ed966fb657980b5063ef4b35efdaa5e4e..1b273650c99d1f5a1c18712e2ed1059e45236240 100644
--- a/app/cbDimensionalityReductionTrainer.cxx
+++ b/app/cbDimensionalityReductionTrainer.cxx
@@ -37,7 +37,7 @@ public:
typedef Superclass::ListSampleType ListSampleType;
typedef Superclass::SampleImageType SampleImageType;
- typedef double ValueType;
+ typedef float ValueType;
typedef itk::VariableLengthVector<ValueType> MeasurementType;
typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
@@ -126,7 +126,7 @@ private:
ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
trainingShiftScaleFilter->SetInput(input);
trainingShiftScaleFilter->SetShifts(meanMeasurementVector);
- trainingShiftScaleFilter->SetScales(stddevMeasurementVector);
+ trainingShiftScaleFilter->SetScales(stddevMeasurementVector*3);
trainingShiftScaleFilter->Update();
ListSampleType::Pointer trainingListSample= trainingShiftScaleFilter->GetOutput();
diff --git a/app/cbDimensionalityReductionVector.cxx b/app/cbDimensionalityReductionVector.cxx
index ce4e1fa1ca35437a92762a8bab2297faba3fe3b3..b598a0310a2120ab666138ca8ed190dab5497124 100644
--- a/app/cbDimensionalityReductionVector.cxx
+++ b/app/cbDimensionalityReductionVector.cxx
@@ -159,12 +159,7 @@ class CbDimensionalityReductionVector : public Application
std::string tmpKey="feat."+key;
AddChoice(tmpKey,item);
}
- /*
- if(fieldType == OFTInteger || ogr::version_proxy::IsOFTInteger64(fieldType) || fieldType == OFTReal)
- {
- std::string tmpKey="featout."+key;
- AddChoice(tmpKey,item);
- }*/
+
}
}
}
@@ -192,8 +187,8 @@ class CbDimensionalityReductionVector : public Application
mv[idx] = (*it)[GetSelectedItems("feat")[idx]].GetValue<double>();
}
input->PushBack(mv);
+
}
-
/** Statistics for shift/scale */
MeasurementType meanMeasurementVector;
@@ -218,7 +213,7 @@ class CbDimensionalityReductionVector : public Application
ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
trainingShiftScaleFilter->SetInput(input);
trainingShiftScaleFilter->SetShifts(meanMeasurementVector);
- trainingShiftScaleFilter->SetScales(stddevMeasurementVector);
+ trainingShiftScaleFilter->SetScales(stddevMeasurementVector*3);
trainingShiftScaleFilter->Update();
otbAppLogINFO("mean used: " << meanMeasurementVector);
otbAppLogINFO("standard deviation used: " << stddevMeasurementVector);
@@ -246,8 +241,7 @@ class CbDimensionalityReductionVector : public Application
/** Perform Dimensionality Reduction */
ListSampleType::Pointer listSample = trainingShiftScaleFilter->GetOutput();
- ListSampleType::Pointer target;
- target = m_Model->PredictBatch(listSample);
+ ListSampleType::Pointer target = m_Model->PredictBatch(listSample);
/** Create/Update Output Shape file */
@@ -276,8 +270,8 @@ class CbDimensionalityReductionVector : public Application
OGRFeatureDefn &inLayerDefn = layer.GetLayerDefn();
for (int k=0 ; k<inLayerDefn.GetFieldCount()-nbBands ; k++) // we don't copy the original bands
{
- OGRFieldDefn fieldDefn(inLayerDefn.GetFieldDefn(k));
- newLayer.CreateField(fieldDefn);
+ OGRFieldDefn fieldDefn(inLayerDefn.GetFieldDefn(k));
+ newLayer.CreateField(fieldDefn);
}
}
else if (GetParameterString("mode")=="update")
@@ -306,20 +300,7 @@ class CbDimensionalityReductionVector : public Application
}
- /*
- else
- {
- // Update mode
- updateMode = true;
- otbAppLogINFO("Update input vector data.");
- // fill temporary buffer for the transfer
- otb::ogr::Layer inputLayer = layer;
- layer = buffer->CopyLayer(inputLayer, std::string("Buffer"));
- // close input data source
- source->Clear();
- // Re-open input data source in update mode
- output = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Update_LayerUpdate);
- }*/
+
otb::ogr::Layer outLayer = output->GetLayer(0);
@@ -351,34 +332,26 @@ class CbDimensionalityReductionVector : public Application
}
}
- // Add an ID field. (The ID already contained in the layer refers to the polygon)
- /*
- OGRFieldDefn IDField("ID_point", OFTInteger);
- ogr::FieldDefn IDFieldDef(IDField);
- outLayer.CreateField(IDFieldDef);
- */
+
// Fill output layer
unsigned int count=0;
auto classfieldname = GetParameterStringList("featout");
it = layer.cbegin();
itEnd = layer.cend();
- int id=0;
+
for( ; it!=itEnd ; ++it, ++count)
{
ogr::Feature dstFeature(outLayer.GetLayerDefn());
- /*
- if (GetParameterString("mode")=="overwrite")
- {*/
- dstFeature.SetFrom( *it , TRUE);
- dstFeature.SetFID(it->GetFID());
- //}
+
+ dstFeature.SetFrom( *it , TRUE);
+ dstFeature.SetFID(it->GetFID());
+
for (std::size_t i=0; i<classfieldname.size(); ++i){
dstFeature[classfieldname[i]].SetValue<ValueType>(target->GetMeasurementVector(count)[i]);
}
- //dstFeature["ID_point"].SetValue<int>(id);
if (updateMode)
{
outLayer.SetFeature(dstFeature);
@@ -387,7 +360,6 @@ class CbDimensionalityReductionVector : public Application
{
outLayer.CreateFeature(dstFeature);
}
- id++;
}
if(outLayer.ogr().TestCapability("Transactions"))
diff --git a/include/DimensionalityReductionModelFactory.txx b/include/DimensionalityReductionModelFactory.txx
index 23fcadddde79b3bbdf2973351f844523ca1b180d..183156c3e593bba276b39ee0d604c5497d39333c 100644
--- a/include/DimensionalityReductionModelFactory.txx
+++ b/include/DimensionalityReductionModelFactory.txx
@@ -36,12 +36,12 @@ namespace otb
template <class TInputValue, class TTargetValue>
// using AutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::Autoencoder<shark::TanhNeuron, shark::LinearNeuron>> ;
-using AutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::Autoencoder<shark::TanhNeuron, shark::TanhNeuron>> ;
+using AutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::Autoencoder<shark::TanhNeuron, shark::LinearNeuron>> ;
template <class TInputValue, class TTargetValue>
// using TiedAutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::TiedAutoencoder< shark::TanhNeuron, shark::LinearNeuron>> ;
-using TiedAutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::TiedAutoencoder< shark::TanhNeuron, shark::TanhNeuron>> ;
+using TiedAutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::TiedAutoencoder< shark::TanhNeuron, shark::LinearNeuron>> ;
template <class TInputValue, class TTargetValue>
diff --git a/include/PCAModel.txx b/include/PCAModel.txx
index e9ea1e2a2b94d3cc1331b65a2123f340087943f6..364dd8def35fa51373f3b0cf30e927622044d736 100644
--- a/include/PCAModel.txx
+++ b/include/PCAModel.txx
@@ -43,7 +43,6 @@ void PCAModel<TInputValue>::Train()
shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange( features );
m_pca.setData(inputSamples);
m_pca.encoder(m_encoder, this->m_Dimension);
- std::cout << m_encoder.matrix() << std::endl;
m_pca.decoder(m_decoder, this->m_Dimension);
}
@@ -175,8 +174,6 @@ void PCAModel<TInputValue>
//target[a]=1;
//target.SetElement(a,p[a]);
-
-
}
//std::cout << p << std::endl;
targets->SetMeasurementVector(id,target);
diff --git a/include/cbLearningApplicationBaseDR.h b/include/cbLearningApplicationBaseDR.h
index 6207527baadd28134925ab380ea80401ca3466c3..3b7557afbf355e12479be9c1e39a9df70e507bc4 100644
--- a/include/cbLearningApplicationBaseDR.h
+++ b/include/cbLearningApplicationBaseDR.h
@@ -103,11 +103,11 @@ public:
#ifdef OTB_USE_SHARK
// typedef shark::Autoencoder< shark::TanhNeuron, shark::LinearNeuron> AutoencoderType;
- typedef shark::Autoencoder< shark::TanhNeuron, shark::TanhNeuron> AutoencoderType;
+ typedef shark::Autoencoder< shark::TanhNeuron, shark::LinearNeuron> AutoencoderType;
typedef otb::AutoencoderModel<InputValueType, AutoencoderType> AutoencoderModelType;
// typedef shark::TiedAutoencoder< shark::TanhNeuron, shark::LinearNeuron> TiedAutoencoderType;
- typedef shark::TiedAutoencoder< shark::TanhNeuron, shark::TanhNeuron> TiedAutoencoderType;
+ typedef shark::TiedAutoencoder< shark::TanhNeuron, shark::LinearNeuron> TiedAutoencoderType;
typedef otb::AutoencoderModel<InputValueType, TiedAutoencoderType> TiedAutoencoderModelType;
typedef otb::PCAModel<InputValueType> PCAModelType;