/// <summary>
/// Predicts the class memberships of all the vectors in the problem.
/// </summary>
/// <param name="problem">The SVM Problem to solve</param>
/// <param name="model">The Model to use</param>
/// <param name="predictProbability">Whether to output a distribution over the classes</param>
/// <returns>Percentage correctly labelled</returns>
public static PredictionResult Predict(
Problem problem,
Model model,
bool predictProbability)
{
int correct = 0;
PredictionResult result = new PredictionResult(model.Parameter);
SvmType svmType = Procedures.SvmGetSvmType(model);
int numberOfClasses = Procedures.SvmGetNrClass(model);
int[] labels = new int[numberOfClasses];
double[] probEstimates = null;
if (predictProbability)
{
if (svmType == SvmType.EPSILON_SVR || svmType == SvmType.NU_SVR)
{
log.Info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" + Procedures.SvmGetSvrProbability(model));
}
else
{
probEstimates = new double[numberOfClasses];
}
}
Procedures.SvmGetLabels(model, labels);
for (int j = 0; j < numberOfClasses; j++)
{
result.AddLabel(labels[j]);
}
for (int i = 0; i < problem.Count; i++)
{
ClassificationClass item = new ClassificationClass();
item.Target = problem.Y[i];
Node[] xValues = problem.X[i];
if (predictProbability &&
(svmType == SvmType.C_SVC || svmType == SvmType.NU_SVC))
{
Procedures.SvmPredictProbability(model, xValues, probEstimates);
double max = 0;
for (int j = 0; j < numberOfClasses; j++)
{
if (probEstimates[j] > max)
{
max = probEstimates[j];
item.Actual = labels[j];
}
item.Add(probEstimates[j]);
}
}
else
{
item.Actual = (int)Procedures.SvmPredict(model, xValues);
}
if (item.Actual == item.Target)
{
correct++;
}
result.Set(item);
}
result.CorrectProbability = (double)correct / problem.Count;
return(result);
}
public void Set(ClassificationClass item)
{
Statistics.Add(item.Target, item.Actual);
classes.Add(item);
}
