/// <summary>
/// Predict the probability distributions over all labels for each data point in a problem.
/// </summary>
/// <param name="model">The model to use</param>
/// <param name="problem">The problem to solve</param>
/// <returns>A distribution over labels for each data point</returns>
public static double[][] PredictLabelsProbability(this Model model, Problem problem)
{
return(problem.X.Select(o => model.PredictProbability(o)).ToArray());
}
/// <summary>
/// Predicts the class memberships of all the vectors in the problem.
/// </summary>
/// <param name="problem">The SVM Problem to solve</param>
/// <param name="outputFile">File for result output</param>
/// <param name="model">The Model to use</param>
/// <param name="predict_probability">Whether to output a distribution over the classes</param>
/// <returns>Percentage correctly labelled</returns>
public static double Predict(
Problem problem,
string outputFile,
Model model,
bool predict_probability)
{
int correct = 0;
int total = 0;
double error = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
StreamWriter output = outputFile != null ? new StreamWriter(outputFile) : null;
SvmType svm_type = Procedures.svm_get_svm_type(model);
int nr_class = Procedures.svm_get_nr_class(model);
int[] labels = new int[nr_class];
double[] prob_estimates = null;
if (predict_probability)
{
if (svm_type == SvmType.EPSILON_SVR || svm_type == SvmType.NU_SVR)
{
Console.WriteLine("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" + Procedures.svm_get_svr_probability(model));
}
else
{
Procedures.svm_get_labels(model, labels);
prob_estimates = new double[nr_class];
if (output != null)
{
output.Write("labels");
for (int j = 0; j < nr_class; j++)
{
output.Write(" " + labels[j]);
}
output.Write("\n");
}
}
}
for (int i = 0; i < problem.Count; i++)
{
double target = problem.Y[i];
Node[] x = problem.X[i];
double v;
if (predict_probability && (svm_type == SvmType.C_SVC || svm_type == SvmType.NU_SVC))
{
v = Procedures.svm_predict_probability(model, x, prob_estimates);
if (output != null)
{
output.Write(v + " ");
for (int j = 0; j < nr_class; j++)
{
output.Write(prob_estimates[j] + " ");
}
output.Write("\n");
}
}
else
{
v = Procedures.svm_predict(model, x);
if (output != null)
{
output.Write(v + "\n");
}
}
if (v == target)
{
++correct;
}
error += (v - target) * (v - target);
sumv += v;
sumy += target;
sumvv += v * v;
sumyy += target * target;
sumvy += v * target;
++total;
}
if (output != null)
{
output.Close();
}
if (model.Parameter.SvmType == SvmType.EPSILON_SVR || model.Parameter.SvmType == SvmType.NU_SVR)
{
return(error / total);
}
else
{
return((double)correct / total);
}
}
/// <summary>
/// Determines the Range transform for the provided problem. Uses the default lower and upper bounds.
/// </summary>
/// <param name="prob">The Problem to analyze</param>
/// <returns>The Range transform for the problem</returns>
public static RangeTransform Compute(Problem prob)
{
return(Compute(prob, DEFAULT_LOWER_BOUND, DEFAULT_UPPER_BOUND));
}