// Return parameter of a Laplace distribution
private static double svm_svr_probability(SvmProblem prob, SvmParameter param)
{
int i;
int nr_fold = 5;
double[] ymv = new double[prob.Lenght];
double mae = 0;
var newparam = (SvmParameter)param.Clone();
newparam.Probability = false;
CrossValidation(prob, newparam, nr_fold, ymv);
for (i = 0; i < prob.Lenght; i++)
{
ymv[i] = prob.Y[i] - ymv[i];
mae += Math.Abs(ymv[i]);
}
mae /= prob.Lenght;
double std = Math.Sqrt(2 * mae * mae);
int count = 0;
mae = 0;
for (i = 0; i < prob.Lenght; i++)
{
if (Math.Abs(ymv[i]) > 5 * std)
{
count = count + 1;
}
else
{
mae += Math.Abs(ymv[i]);
}
}
mae /= (prob.Lenght - 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(SvmProblem prob, SvmParameter param, double Cp, double Cn, double[] probAB)
{
//int i;
int nr_fold = 5;
int[] perm = new int[prob.Lenght];
double[] dec_values = new double[prob.Lenght];
// random shuffle
var rnd = new Random();
for (int i = 0; i < prob.Lenght; i++) perm[i] = i;
for (int i = 0; i < prob.Lenght; i++)
{
int j = i + (int)(rnd.NextDouble() * (prob.Lenght - i));
//do { int _ = perm[i]; perm[i] = perm[j]; perm[j] = _; } while (false);
Common.Swap(ref perm[i], ref perm[j]);
}
for (int i = 0; i < nr_fold; i++)
{
int begin = i * prob.Lenght / nr_fold;
int end = (i + 1) * prob.Lenght / nr_fold;
//int j;
var subprobLenght = prob.Lenght - (end - begin);
var subprob = new SvmProblem
{
X = new SvmNode[subprobLenght][],
Y = new double[subprobLenght]
};
int k = 0;
for (int j = 0; j < begin; j++)
{
subprob.X[k] = prob.X[perm[j]];
subprob.Y[k] = prob.Y[perm[j]];
++k;
}
for (int j = end; j < prob.Lenght; j++)
{
subprob.X[k] = prob.X[perm[j]];
subprob.Y[k] = prob.Y[perm[j]];
++k;
}
int p_count = 0, n_count = 0;
for (int j = 0; j < k; j++)
if (subprob.Y[j] > 0)
p_count++;
else
n_count++;
if (p_count == 0 && n_count == 0)
for (int j = begin; j < end; j++)
dec_values[perm[j]] = 0;
else if (p_count > 0 && n_count == 0)
for (int j = begin; j < end; j++)
dec_values[perm[j]] = 1;
else if (p_count == 0 && n_count > 0)
for (int j = begin; j < end; j++)
dec_values[perm[j]] = -1;
else
{
var subparam = (SvmParameter)param.Clone();
subparam.Probability = false;
subparam.C = 1.0;
subparam.WeightLabel = new int[2];
subparam.Weight = new double[2];
subparam.WeightLabel[0] = +1;
subparam.WeightLabel[1] = -1;
subparam.Weight[0] = Cp;
subparam.Weight[1] = Cn;
var submodel = Train(subprob, subparam);
for (int j = begin; j < end; j++)
{
double[] dec_value = new double[1];
submodel.PredictValues(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.Lenght, dec_values, prob.Y, probAB);
}
// Return parameter of a Laplace distribution
private static double svm_svr_probability(SvmProblem prob, SvmParameter param)
{
int i;
int nr_fold = 5;
double[] ymv = new double[prob.Lenght];
double mae = 0;
var newparam = (SvmParameter)param.Clone();
newparam.Probability = false;
CrossValidation(prob, newparam, nr_fold, ymv);
for (i = 0; i < prob.Lenght; i++)
{
ymv[i] = prob.Y[i] - ymv[i];
mae += Math.Abs(ymv[i]);
}
mae /= prob.Lenght;
double std = Math.Sqrt(2 * mae * mae);
int count = 0;
mae = 0;
for (i = 0; i < prob.Lenght; i++)
if (Math.Abs(ymv[i]) > 5 * std)
count = count + 1;
else
mae += Math.Abs(ymv[i]);
mae /= (prob.Lenght - 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(SvmProblem prob, SvmParameter param, double Cp, double Cn, double[] probAB)
{
//int i;
int nr_fold = 5;
int[] perm = new int[prob.Lenght];
double[] dec_values = new double[prob.Lenght];
// random shuffle
var rnd = new Random();
for (int i = 0; i < prob.Lenght; i++)
{
perm[i] = i;
}
for (int i = 0; i < prob.Lenght; i++)
{
int j = i + (int)(rnd.NextDouble() * (prob.Lenght - i));
//do { int _ = perm[i]; perm[i] = perm[j]; perm[j] = _; } while (false);
Common.Swap(ref perm[i], ref perm[j]);
}
for (int i = 0; i < nr_fold; i++)
{
int begin = i * prob.Lenght / nr_fold;
int end = (i + 1) * prob.Lenght / nr_fold;
//int j;
var subprobLenght = prob.Lenght - (end - begin);
var subprob = new SvmProblem
{
X = new SvmNode[subprobLenght][],
Y = new double[subprobLenght]
};
int k = 0;
for (int j = 0; j < begin; j++)
{
subprob.X[k] = prob.X[perm[j]];
subprob.Y[k] = prob.Y[perm[j]];
++k;
}
for (int j = end; j < prob.Lenght; j++)
{
subprob.X[k] = prob.X[perm[j]];
subprob.Y[k] = prob.Y[perm[j]];
++k;
}
int p_count = 0, n_count = 0;
for (int j = 0; j < k; j++)
{
if (subprob.Y[j] > 0)
{
p_count++;
}
else
{
n_count++;
}
}
if (p_count == 0 && n_count == 0)
{
for (int j = begin; j < end; j++)
{
dec_values[perm[j]] = 0;
}
}
else if (p_count > 0 && n_count == 0)
{
for (int j = begin; j < end; j++)
{
dec_values[perm[j]] = 1;
}
}
else if (p_count == 0 && n_count > 0)
{
for (int j = begin; j < end; j++)
{
dec_values[perm[j]] = -1;
}
}
else
{
var subparam = (SvmParameter)param.Clone();
subparam.Probability = false;
subparam.C = 1.0;
subparam.WeightLabel = new int[2];
subparam.Weight = new double[2];
subparam.WeightLabel[0] = +1;
subparam.WeightLabel[1] = -1;
subparam.Weight[0] = Cp;
subparam.Weight[1] = Cn;
var submodel = Train(subprob, subparam);
for (int j = begin; j < end; j++)
{
double[] dec_value = new double[1];
submodel.PredictValues(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.Lenght, dec_values, prob.Y, probAB);
}
