static private void SVMExample()
{
SVMLearn svmLearn = new SVMLearn();
svmLearn.ExecuteLearner("svm_learn.exe", "example/train.dat", "example/model.txt", "example/learnLog.txt", false);
SVMClassify svmClassify = new SVMClassify();
svmClassify.ExecuteClassifier("svm_classify.exe", "example/test.dat", "example/model.txt", "example/output.txt", "example/clasifylog.txt", false);
}
public double[] distributionForInstances(Instances instances)
{
double[] ret = new double[instances.numInstances()];
if (m_mustValue.HasValue)
{
for (int i = 0; i < ret.Length; ++i)
{
ret[i] = m_mustValue == 0 ? m_delta.Value - 1 : m_delta.Value + 1;
}
return(ret);
}
if (System.IO.File.Exists(m_testFile))
{
System.IO.File.Delete(m_testFile);
}
libsvmSaver.setInstances(instances);
libsvmSaver.setFile(new java.io.File(m_testFile));
libsvmSaver.writeBatch();
//ConvertNorminalToString(m_testFile);
if (System.IO.File.Exists(m_testOutputFile))
{
System.IO.File.Delete(m_testOutputFile);
}
classifier.ExecuteClassifier(s_classifierPath, m_testFile, m_modelFile, m_testOutputFile);
if (!System.IO.File.Exists(m_testOutputFile))
{
throw new InvalidOperationException(classifier.Output);
}
using (System.IO.StreamReader sr = new System.IO.StreamReader(m_testOutputFile))
{
for (int i = 0; i < ret.Length; ++i)
{
string s = sr.ReadLine();
ret[i] = Double.Parse(s);
}
}
if (System.IO.File.Exists(m_testFile))
{
System.IO.File.Delete(m_testFile);
}
if (System.IO.File.Exists(m_testOutputFile))
{
System.IO.File.Delete(m_testOutputFile);
}
return(ret);
}