// Return parameter of a Laplace distribution
private static double svm_svr_probability(Problem prob, Parameter param)
{
int i;
int nr_fold = 5;
double[] ymv = new double[prob.Count];
double mae = 0;
Parameter newparam = (Parameter)param.Clone();
newparam.Probability = false;
svm_cross_validation(prob, newparam, nr_fold, ymv);
for (i = 0; i < prob.Count; i++)
{
ymv[i] = prob.Y[i] - ymv[i];
mae += Math.Abs(ymv[i]);
}
mae /= prob.Count;
double std = Math.Sqrt(2 * mae * mae);
int count = 0;
mae = 0;
for (i = 0; i < prob.Count; i++)
if (Math.Abs(ymv[i]) > 5 * std)
count = count + 1;
else
mae += Math.Abs(ymv[i]);
mae /= (prob.Count - count);
Procedures.info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" + mae + "\n");
return mae;
}
// Cross-validation decision values for probability estimates
private static void svm_binary_svc_probability(Problem prob, Parameter param, double Cp, double Cn, double[] probAB)
{
int i;
int nr_fold = 5;
int[] perm = new int[prob.Count];
double[] dec_values = new double[prob.Count];
// random shuffle
Random rand = new Random();
for (i = 0; i < prob.Count; i++) perm[i] = i;
for (i = 0; i < prob.Count; i++)
{
int j = i + (int)(rand.NextDouble() * (prob.Count - i));
do { int _ = perm[i]; perm[i] = perm[j]; perm[j] = _; } while (false);
}
for (i = 0; i < nr_fold; i++)
{
int begin = i * prob.Count / nr_fold;
int end = (i + 1) * prob.Count / nr_fold;
int j, k;
Problem subprob = new Problem();
subprob.Count = prob.Count - (end - begin);
subprob.X = new Node[subprob.Count][];
subprob.Y = new double[subprob.Count];
k = 0;
for (j = 0; j < begin; j++)
{
subprob.X[k] = prob.X[perm[j]];
subprob.Y[k] = prob.Y[perm[j]];
++k;
}
for (j = end; j < prob.Count; j++)
{
subprob.X[k] = prob.X[perm[j]];
subprob.Y[k] = prob.Y[perm[j]];
++k;
}
int p_count = 0, n_count = 0;
for (j = 0; j < k; j++)
if (subprob.Y[j] > 0)
p_count++;
else
n_count++;
if (p_count == 0 && n_count == 0)
for (j = begin; j < end; j++)
dec_values[perm[j]] = 0;
else if (p_count > 0 && n_count == 0)
for (j = begin; j < end; j++)
dec_values[perm[j]] = 1;
else if (p_count == 0 && n_count > 0)
for (j = begin; j < end; j++)
dec_values[perm[j]] = -1;
else
{
Parameter subparam = (Parameter)param.Clone();
subparam.Probability = false;
subparam.C = 1.0;
subparam.Weights[1] = Cp;
subparam.Weights[-1] = Cn;
Model submodel = svm_train(subprob, subparam);
for (j = begin; j < end; j++)
{
double[] dec_value = new double[1];
svm_predict_values(submodel, prob.X[perm[j]], dec_value);
dec_values[perm[j]] = dec_value[0];
// ensure +1 -1 order; reason not using CV subroutine
dec_values[perm[j]] *= submodel.ClassLabels[0];
}
}
}
sigmoid_train(prob.Count, dec_values, prob.Y, probAB);
}
