/// <summary>
/// this is a static duplicate of LoadModel. at some point i need to remove this duplicate..
/// </summary>
/// <param name="filename"></param>
/// <returns></returns>
public static bool loadModelStatic(string filename)
{
try
{
Preferences.Instance.modelLoaded = false;
if (GuiPreferences.Instance.TrainType != TrainingType.CrossValidation)
{
libSVM _svm = libSVM.Load("ori.svm");
if (_svm != null)
{
Preferences.Instance.modelLoaded = true;
GuiPreferences.Instance.setLog("new loaded model overwritten the old model if there was any");
Preferences.Instance.svmModel = _svm;
return(true);
}
else
{
return(false);
}
}
else
{
GuiPreferences.Instance.setLog("cant load a model for cross validation (it has K models)");
return(false);
}
}
catch (Exception e)
{
GuiPreferences.Instance.setLog("loadModel: " + e.ToString());
return(false);
}
}
/// <summary>
/// Load model from file
/// </summary>
/// <param name="_filename">Model file</param>
public static libSVM Load(string _filename)
{
libSVM svm = new libSVM();
svm.Reload(_filename);
return(svm);
}
/// <summary>
/// wrapper function for GetAccuracy, the name was not logical and i have to maintain a subversion pristine copy.
/// </summary>
/// <param name="problem">problem</param>
/// <returns>accuracy in %</returns>
///
public double GetAccuracyFromTestSingleSample(libSVM_Problem problem, libSVM svmModel)
{
if (problem.samples == null || problem.samples.Length == 0)
{
throw new Exception("GetAccuracy no samples");
}
if (problem.labels == null || problem.labels.Length == 0)
{
throw new Exception("GetAccuracy no labels");
}
if (problem.samples.Length != problem.labels.Length)
{
throw new Exception("GetAccuracy labels.length!=samples.length");
}
output = new libSVM_output();
int correctPredictions = 0;
double pred;
int index = problem.samples.Length - 1;
if (problem.samples[index] != null && problem.samples[index].Count > 0)
{
pred = svmModel.Predict(problem.samples[index]);
return(pred);
/*output.setValue(index, pred);
* if (pred == problem.labels[index])
* correctPredictions++;*/
}
output.accuracy = correctPredictions / (double)problem.samples.Length * 100.0;
return(1.0);
}