From 0d8f47f8746f6a176b7e130206b8c53127d4c268 Mon Sep 17 00:00:00 2001
From: Jonathan Guinet <jonathan.guinet@c-s.fr>
Date: Fri, 4 Nov 2011 12:38:27 +0100
Subject: [PATCH] ENH: Train SVM Application change.
---
.../otbTrainSVMImagesClassifier.cxx | 36 ++++++++-----------
1 file changed, 15 insertions(+), 21 deletions(-)
diff --git a/Applications/Classification/otbTrainSVMImagesClassifier.cxx b/Applications/Classification/otbTrainSVMImagesClassifier.cxx
index 67c36d0cfe..99e6f9d781 100644
--- a/Applications/Classification/otbTrainSVMImagesClassifier.cxx
+++ b/Applications/Classification/otbTrainSVMImagesClassifier.cxx
@@ -165,16 +165,13 @@ private:
AddParameter(ParameterType_Filename, "imstat", "XML image statistics file");
MandatoryOff("imstat");
SetParameterDescription("imstat", "filename of an XML file containing mean and standard deviation of input images.");
- AddParameter(ParameterType_Filename, "out", "Output SVM model");
- SetParameterDescription("out", "Output SVM model");
AddParameter(ParameterType_Float, "m", "Margin for SVM learning");
- MandatoryOff("m");
- SetParameterDescription("m", "Margin for SVM learning.");
+ SetParameterFloat("m", 1.0);
+ SetParameterDescription("m", "Margin for SVM learning.(1 by default).");
AddParameter(ParameterType_Int, "b", "Balance and grow the training set");
SetParameterDescription("b", "Balance and grow the training set.");
MandatoryOff("b");
AddParameter(ParameterType_Choice, "k", "SVM Kernel Type");
- MandatoryOff("k");
AddChoice("k.linear", "Linear");
AddChoice("k.rbf", "Neareast Neighbor");
AddChoice("k.poly", "Polynomial");
@@ -182,25 +179,26 @@ private:
SetParameterString("k", "linear");
SetParameterDescription("k", "SVM Kernel Type.");
AddParameter(ParameterType_Int, "mt", "Maximum training sample size");
- MandatoryOff("mt");
+ //MandatoryOff("mt");
SetDefaultParameterInt("mt", -1);
SetParameterDescription("mt", "Maximum size of the training sample (default = -1).");
AddParameter(ParameterType_Int, "mv", "Maximum validation sample size");
- MandatoryOff("mv");
+ // MandatoryOff("mv");
SetDefaultParameterInt("mv", -1);
SetParameterDescription("mv", "Maximum size of the validation sample (default = -1)");
AddParameter(ParameterType_Float, "vtr", "training and validation sample ratio");
SetParameterDescription("vtr",
"Ratio between training and validation sample (0.0 = all training, 1.0 = all validation) default = 0.5.");
- MandatoryOff("vtr");
SetParameterFloat("vtr", 0.5);
AddParameter(ParameterType_Empty, "opt", "parameters optimization");
MandatoryOff("opt");
SetParameterDescription("opt", "SVM parameters optimization");
AddParameter(ParameterType_Filename, "vfn", "Name of the discrimination field");
- MandatoryOff("vfn");
SetParameterDescription("vfn", "Name of the field using to discriminate class in the vector data files.");
SetParameterString("vfn", "Class");
+ AddParameter(ParameterType_Filename, "out", "Output SVM model");
+ SetParameterDescription("out", "Output SVM model");
+ SetParameterRole("out",Role_Output);
}
@@ -252,7 +250,7 @@ private:
vdreproj->SetUseOutputSpacingAndOriginFromImage(false);
// Configure DEM directory
- if (HasUserValue("dem"))
+ if (IsParameterEnabled("dem"))
{
vdreproj->SetDEMDirectory(GetParameterString("dem"));
}
@@ -274,14 +272,11 @@ private:
sampleGenerator->SetInput(image);
sampleGenerator->SetInputVectorData(vdreproj->GetOutput());
- if (HasUserValue("vfn"))
- {
- sampleGenerator->SetClassKey(GetParameterString("vfn"));
- }
-
+ sampleGenerator->SetClassKey(GetParameterString("vfn"));
sampleGenerator->SetMaxTrainingSize(GetParameterInt("mt"));
sampleGenerator->SetMaxValidationSize(GetParameterInt("mv"));
sampleGenerator->SetValidationTrainingProportion(GetParameterFloat("vtr"));
+
sampleGenerator->Update();
//Concatenate training and validation samples from the image
@@ -296,7 +291,7 @@ private:
concatenateValidationSamples->Update();
concatenateValidationLabels->Update();
- if (HasValue("imstat"))
+ if (IsParameterEnabled("imstat"))
{
StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
statisticsReader->SetFileName(GetParameterString("imstat"));
@@ -328,7 +323,7 @@ private:
LabelListSampleType::Pointer labelListSample;
//--------------------------
// Balancing training sample (if needed)
- if (HasUserValue("b"))
+ if (IsParameterEnabled("b"))
{
// Balance the list sample.
otbAppLogINFO("Number of training samples before balancing: " << concatenateTrainingSamples->GetOutputSampleList()->Size())
@@ -371,10 +366,9 @@ private:
svmestimator->SetParametersOptimization(true);
}
- if (HasUserValue("m"))
- {
- svmestimator->SetC(GetParameterFloat("m"));
- }
+
+ svmestimator->SetC(GetParameterFloat("m"));
+
switch (GetParameterInt("k"))
{
--
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