// Return parameter of a Laplace distribution
private static double svm_svr_probability(svm_problem prob, svm_parameter param)
{
int i;
int nr_fold = 5;
double[] ymv = new double[prob.l];
double mae = 0;
svm_parameter newparam = (svm_parameter)param.Clone();
newparam.probability = 0;
svm_cross_validation(prob,newparam,nr_fold,ymv);
for(i=0;i<prob.l;i++)
{
ymv[i]=prob.y[i]-ymv[i];
mae += Math.Abs(ymv[i]);
}
mae /= prob.l;
double std=Math.Sqrt(2*mae*mae);
int count=0;
mae=0;
for(i=0;i<prob.l;i++)
if (Math.Abs(ymv[i]) > 5*std)
count=count+1;
else
mae+=Math.Abs(ymv[i]);
mae /= (prob.l-count);
svm.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(svm_problem prob, svm_parameter param, double Cp, double Cn, double[] probAB)
{
int i;
int nr_fold = 5;
int[] perm = new int[prob.l];
double[] dec_values = new double[prob.l];
// random shuffle
for(i=0;i<prob.l;i++) perm[i]=i;
for(i=0;i<prob.l;i++)
{
int j = i+rand.Next(prob.l-i);
do {int _=perm[i]; perm[i]=perm[j]; perm[j]=_;} while(false);
}
for(i=0;i<nr_fold;i++)
{
int begin = i*prob.l/nr_fold;
int end = (i+1)*prob.l/nr_fold;
int j,k;
svm_problem subprob = new svm_problem();
subprob.l = prob.l-(end-begin);
subprob.x = new svm_node[subprob.l][];
subprob.y = new double[subprob.l];
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.l;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
{
svm_parameter subparam = (svm_parameter)param.Clone();
subparam.probability=0;
subparam.C=1.0;
subparam.nr_weight=2;
subparam.weight_label = new int[2];
subparam.weight = new double[2];
subparam.weight_label[0]=+1;
subparam.weight_label[1]=-1;
subparam.weight[0]=Cp;
subparam.weight[1]=Cn;
svm_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.label[0];
}
}
}
sigmoid_train(prob.l,dec_values,prob.y,probAB);
}