// Return parameter of a Laplace distribution
internal static double SvmSvrProbability(SvmProblem prob, SvmParameter param)
{
int i;
const int nrFold = 5;
double[] ymv = new double[prob.Count];
double mae = 0;
SvmParameter newparam = (SvmParameter)param.Clone();
newparam.probability = false;
SvmCrossValidation(prob, newparam, nrFold, 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);
Info(
"Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" +
mae + "\n");
return(mae);
}
// Return parameter of a Laplace distribution
internal static double SvmSvrProbability(SvmProblem prob, SvmParameter param)
{
int i;
const int nrFold = 5;
double[] ymv = new double[prob.Count];
double mae = 0;
SvmParameter newparam = (SvmParameter) param.Clone();
newparam.probability = false;
SvmCrossValidation(prob, newparam, nrFold, 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);
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
internal static void SvmBinarySvcProbability(SvmProblem prob, SvmParameter param, double cp, double cn,
IList <double> probAb)
{
int i;
const int nrFold = 5;
int[] perm = new int[prob.Count];
double[] decValues = new double[prob.Count];
// random shuffle
for (i = 0; i < prob.Count; i++)
{
perm[i] = i;
}
for (i = 0; i < prob.Count; i++)
{
int j = i + rand.Next(prob.Count - i);
do
{
int _ = perm[i];
perm[i] = perm[j];
perm[j] = _;
} while (false);
}
for (i = 0; i < nrFold; i++)
{
int begin = i * prob.Count / nrFold;
int end = (i + 1) * prob.Count / nrFold;
int j;
int count = prob.Count - (end - begin);
SvmProblem subprob = new SvmProblem {
x = new BaseVector[count], y = new float[count]
};
int 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 pCount = 0, nCount = 0;
for (j = 0; j < k; j++)
{
if (subprob.y[j] > 0)
{
pCount++;
}
else
{
nCount++;
}
}
if (pCount == 0 && nCount == 0)
{
for (j = begin; j < end; j++)
{
decValues[perm[j]] = 0;
}
}
else if (pCount > 0 && nCount == 0)
{
for (j = begin; j < end; j++)
{
decValues[perm[j]] = 1;
}
}
else if (pCount == 0 && nCount > 0)
{
for (j = begin; j < end; j++)
{
decValues[perm[j]] = -1;
}
}
else
{
SvmParameter subparam = (SvmParameter)param.Clone();
subparam.probability = false;
subparam.c = 1.0;
subparam.nrWeight = 2;
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;
SvmModel submodel = SvmTrain(subprob, subparam);
for (j = begin; j < end; j++)
{
double[] decValue = new double[1];
SvmPredictValues(submodel, prob.x[perm[j]], decValue);
decValues[perm[j]] = decValue[0];
// ensure +1 -1 order; reason not using CV subroutine
decValues[perm[j]] *= submodel.label[0];
}
}
}
SigmoidTrain(prob.Count, decValues, prob.y, probAb);
}
// Cross-validation decision values for probability estimates
internal static void SvmBinarySvcProbability(SvmProblem prob, SvmParameter param, double cp, double cn,
IList<double> probAb)
{
int i;
const int nrFold = 5;
int[] perm = new int[prob.Count];
double[] decValues = new double[prob.Count];
// random shuffle
for (i = 0; i < prob.Count; i++){
perm[i] = i;
}
for (i = 0; i < prob.Count; i++){
int j = i + rand.Next(prob.Count - i);
do{
int _ = perm[i];
perm[i] = perm[j];
perm[j] = _;
} while (false);
}
for (i = 0; i < nrFold; i++){
int begin = i*prob.Count/nrFold;
int end = (i + 1)*prob.Count/nrFold;
int j;
int count = prob.Count - (end - begin);
SvmProblem subprob = new SvmProblem{x = new BaseVector[count], y = new float[count]};
int 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 pCount = 0, nCount = 0;
for (j = 0; j < k; j++){
if (subprob.y[j] > 0){
pCount++;
} else{
nCount++;
}
}
if (pCount == 0 && nCount == 0){
for (j = begin; j < end; j++){
decValues[perm[j]] = 0;
}
} else if (pCount > 0 && nCount == 0){
for (j = begin; j < end; j++){
decValues[perm[j]] = 1;
}
} else if (pCount == 0 && nCount > 0){
for (j = begin; j < end; j++){
decValues[perm[j]] = -1;
}
} else{
SvmParameter subparam = (SvmParameter) param.Clone();
subparam.probability = false;
subparam.c = 1.0;
subparam.nrWeight = 2;
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;
SvmModel submodel = SvmTrain(subprob, subparam);
for (j = begin; j < end; j++){
double[] decValue = new double[1];
SvmPredictValues(submodel, prob.x[perm[j]], decValue);
decValues[perm[j]] = decValue[0];
// ensure +1 -1 order; reason not using CV subroutine
decValues[perm[j]] *= submodel.label[0];
}
}
}
SigmoidTrain(prob.Count, decValues, prob.y, probAb);
}
