From 58bcfd76ac279b587b979208240e041ff336e6ac Mon Sep 17 00:00:00 2001
From: Jordi Inglada <jordi.inglada@cesbio.cnes.fr>
Date: Thu, 11 Dec 2014 15:58:49 +0100
Subject: [PATCH] ENH: add regression mode to Random Forests
---
.../otbRandomForestsMachineLearningModel.h | 4 ++++
.../otbRandomForestsMachineLearningModel.txx | 18 +++++++++++-------
2 files changed, 15 insertions(+), 7 deletions(-)
diff --git a/Code/UtilitiesAdapters/OpenCVAdapters/otbRandomForestsMachineLearningModel.h b/Code/UtilitiesAdapters/OpenCVAdapters/otbRandomForestsMachineLearningModel.h
index 2906f18c0a..917202e95c 100644
--- a/Code/UtilitiesAdapters/OpenCVAdapters/otbRandomForestsMachineLearningModel.h
+++ b/Code/UtilitiesAdapters/OpenCVAdapters/otbRandomForestsMachineLearningModel.h
@@ -161,6 +161,9 @@ public:
/* The type of the termination criteria */
itkGetMacro(TerminationCriteria, int);
itkSetMacro(TerminationCriteria, int);
+ /* Perform regression instead of classification */
+ itkGetMacro(RegressionMode, bool);
+ itkSetMacro(RegressionMode, bool);
/** Returns a matrix containing variable importance */
VariableImportanceMatrixType GetVariableImportance();
@@ -199,6 +202,7 @@ private:
int m_MaxNumberOfTrees;
float m_ForestAccuracy;
int m_TerminationCriteria;
+ bool m_RegressionMode;
};
} // end namespace otb
diff --git a/Code/UtilitiesAdapters/OpenCVAdapters/otbRandomForestsMachineLearningModel.txx b/Code/UtilitiesAdapters/OpenCVAdapters/otbRandomForestsMachineLearningModel.txx
index 001096c81b..78caff9866 100644
--- a/Code/UtilitiesAdapters/OpenCVAdapters/otbRandomForestsMachineLearningModel.txx
+++ b/Code/UtilitiesAdapters/OpenCVAdapters/otbRandomForestsMachineLearningModel.txx
@@ -40,7 +40,8 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
m_MaxNumberOfVariables(0),
m_MaxNumberOfTrees(100),
m_ForestAccuracy(0.01),
- m_TerminationCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS)
+ m_TerminationCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS),
+ m_RegressionMode(false)
{
}
@@ -95,11 +96,14 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
cv::Mat var_type = cv::Mat(this->GetInputListSample()->GetMeasurementVectorSize() + 1, 1, CV_8U );
var_type.setTo(cv::Scalar(CV_VAR_NUMERICAL) ); // all inputs are numerical
- var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
+ if(m_RegressionMode)
+ var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_NUMERICAL;
+ else
+ var_type.at<uchar>(this->GetInputListSample()->GetMeasurementVectorSize(), 0) = CV_VAR_CATEGORICAL;
//train the RT model
m_RFModel->train(samples, CV_ROW_SAMPLE, labels,
- cv::Mat(), cv::Mat(), var_type, cv::Mat(), params);
+ cv::Mat(), cv::Mat(), var_type, cv::Mat(), params);
}
template <class TInputValue, class TOutputValue>
@@ -113,13 +117,13 @@ RandomForestsMachineLearningModel<TInputValue,TOutputValue>
otb::SampleToMat<InputSampleType>(value,sample);
- double result = m_RFModel->predict(sample);
+ double result = m_RFModel->predict(sample);
- TargetSampleType target;
+ TargetSampleType target;
- target[0] = static_cast<TOutputValue>(result);
+ target[0] = static_cast<TOutputValue>(result);
- return target[0];
+ return target[0];
}
template <class TInputValue, class TOutputValue>
--
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