private static void SolveEpsilonSvr(SvmProblem prob, SvmParameter param, IList <double> alpha,
SvmSolver.SolutionInfo si)
{
int l = prob.Count;
double[] alpha2 = new double[2 * l];
double[] linearTerm = new double[2 * l];
short[] y = new short[2 * l];
int i;
for (i = 0; i < l; i++)
{
alpha2[i] = 0;
linearTerm[i] = param.p - prob.y[i];
y[i] = 1;
alpha2[i + l] = 0;
linearTerm[i + l] = param.p + prob.y[i];
y[i + l] = -1;
}
SvmSolver s = new SvmSolver();
s.Solve(2 * l, new SvrQ(prob, param), linearTerm, y, alpha2, param.c, param.c, param.eps, si, param.shrinking);
double sumAlpha = 0;
for (i = 0; i < l; i++)
{
alpha[i] = alpha2[i] - alpha2[i + l];
sumAlpha += Math.Abs(alpha[i]);
}
Info("nu = " + sumAlpha / (param.c * l) + "\n");
}
private static void SolveNuSvr(SvmProblem prob, SvmParameter param, IList <double> alpha, SvmSolver.SolutionInfo si)
{
int l = prob.Count;
double c = param.c;
double[] alpha2 = new double[2 * l];
double[] linearTerm = new double[2 * l];
short[] y = new short[2 * l];
int i;
double sum = c * param.nu * l / 2;
for (i = 0; i < l; i++)
{
alpha2[i] = alpha2[i + l] = Math.Min(sum, c);
sum -= alpha2[i];
linearTerm[i] = -prob.y[i];
y[i] = 1;
linearTerm[i + l] = prob.y[i];
y[i + l] = -1;
}
SvmSolverNu s = new SvmSolverNu();
s.Solve(2 * l, new SvrQ(prob, param), linearTerm, y, alpha2, c, c, param.eps, si, param.shrinking);
Info("epsilon = " + (-si.r) + "\n");
for (i = 0; i < l; i++)
{
alpha[i] = alpha2[i] - alpha2[i + l];
}
}
private static void SolveOneClass(SvmProblem prob, SvmParameter param, double[] alpha, SvmSolver.SolutionInfo si)
{
int l = prob.Count;
double[] zeros = new double[l];
short[] ones = new short[l];
int i;
int n = (int)(param.nu * prob.Count); // # of alpha's at upper bound
for (i = 0; i < n; i++)
{
alpha[i] = 1;
}
if (n < prob.Count)
{
alpha[n] = param.nu * prob.Count - n;
}
for (i = n + 1; i < l; i++)
{
alpha[i] = 0;
}
for (i = 0; i < l; i++)
{
zeros[i] = 0;
ones[i] = 1;
}
SvmSolver s = new SvmSolver();
s.Solve(l, new OneClassQ(prob, param), zeros, ones, alpha, 1.0, 1.0, param.eps, si, param.shrinking);
}
private static DecisionFunction SvmTrainOne(SvmProblem prob, SvmParameter param, double cp, double cn)
{
double[] alpha = new double[prob.Count];
SvmSolver.SolutionInfo si = new SvmSolver.SolutionInfo();
switch (param.svmType)
{
case SvmType.CSvc:
SolveCSvc(prob, param, alpha, si, cp, cn);
break;
case SvmType.NuSvc:
SolveNuSvc(prob, param, alpha, si);
break;
case SvmType.OneClass:
SolveOneClass(prob, param, alpha, si);
break;
case SvmType.EpsilonSvr:
SolveEpsilonSvr(prob, param, alpha, si);
break;
case SvmType.NuSvr:
SolveNuSvr(prob, param, alpha, si);
break;
}
Info("obj = " + si.obj + ", rho = " + si.rho + "\n");
// output SVs
int nSv = 0;
int nBsv = 0;
for (int i = 0; i < prob.Count; i++)
{
if (Math.Abs(alpha[i]) > 0)
{
++nSv;
if (prob.y[i] > 0)
{
if (Math.Abs(alpha[i]) >= si.upperBoundP)
{
++nBsv;
}
}
else
{
if (Math.Abs(alpha[i]) >= si.upperBoundN)
{
++nBsv;
}
}
}
}
Info("nSV = " + nSv + ", nBSV = " + nBsv + "\n");
DecisionFunction f = new DecisionFunction {
alpha = alpha, rho = si.rho
};
return(f);
}
private static void SolveNuSvc(SvmProblem prob, SvmParameter param, double[] alpha, SvmSolver.SolutionInfo si)
{
int i;
int l = prob.Count;
double nu = param.nu;
short[] y = new short[l];
for (i = 0; i < l; i++)
{
if (prob.y[i] > 0)
{
y[i] = +1;
}
else
{
y[i] = -1;
}
}
double sumPos = nu * l / 2;
double sumNeg = nu * l / 2;
for (i = 0; i < l; i++)
{
if (y[i] == +1)
{
alpha[i] = Math.Min(1.0, sumPos);
sumPos -= alpha[i];
}
else
{
alpha[i] = Math.Min(1.0, sumNeg);
sumNeg -= alpha[i];
}
}
double[] zeros = new double[l];
for (i = 0; i < l; i++)
{
zeros[i] = 0;
}
SvmSolverNu s = new SvmSolverNu();
s.Solve(l, new SvcQ(prob, param, y), zeros, y, alpha, 1.0, 1.0, param.eps, si, param.shrinking);
double r = si.r;
Info("C = " + 1 / r + "\n");
for (i = 0; i < l; i++)
{
alpha[i] *= y[i] / r;
}
si.rho /= r;
si.obj /= (r * r);
si.upperBoundP = 1 / r;
si.upperBoundN = 1 / r;
}
public SvmProblem ExtractFeatures(int[] indices)
{
SvmProblem reducedData = new SvmProblem {
x = new BaseVector[Count], y = new double[Count]
};
for (int i = 0; i < Count; i++)
{
reducedData.x[i] = x[i].SubArray(indices);
reducedData.y[i] = y[i];
}
return(reducedData);
}
public SvmProblem Copy()
{
SvmProblem newProb = new SvmProblem {
x = new BaseVector[Count], y = new double[Count]
};
for (int i = 0; i < Count; ++i)
{
newProb.x[i] = x[i].Copy();
newProb.y[i] = y[i];
}
return(newProb);
}
private static void SolveCSvc(SvmProblem prob, SvmParameter param, double[] alpha, SvmSolver.SolutionInfo si,
double cp, double cn)
{
int l = prob.Count;
double[] minusOnes = new double[l];
short[] y = new short[l];
int i;
for (i = 0; i < l; i++)
{
alpha[i] = 0;
minusOnes[i] = -1;
if (prob.y[i] > 0)
{
y[i] = +1;
}
else
{
y[i] = -1;
}
}
SvmSolver s = new SvmSolver();
s.Solve(l, new SvcQ(prob, param, y), minusOnes, y, alpha, cp, cn, param.eps, si, param.shrinking);
double sumAlpha = 0;
for (i = 0; i < l; i++)
{
sumAlpha += alpha[i];
}
if (cp == cn)
{
Info("nu = " + sumAlpha / (cp * prob.Count) + "\n");
}
for (i = 0; i < l; i++)
{
alpha[i] *= y[i];
}
}
// 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);
}
// Stratified cross validation
public static void SvmCrossValidation(SvmProblem prob, SvmParameter param, int nrFold, double[] target)
{
int i;
int[] foldStart = new int[nrFold + 1];
int l = prob.Count;
int[] perm = new int[l];
// stratified cv may not give leave-one-out rate
// Each class to l folds -> some folds may have zero elements
if ((param.svmType == SvmType.CSvc || param.svmType == SvmType.NuSvc) && nrFold < l)
{
int[] tmpNrClass = new int[1];
int[][] tmpLabel = new int[1][];
int[][] tmpStart = new int[1][];
int[][] tmpCount = new int[1][];
SvmGroupClasses(prob, tmpNrClass, tmpLabel, tmpStart, tmpCount, perm);
int nrClass = tmpNrClass[0];
int[] start = tmpStart[0];
int[] count = tmpCount[0];
// random shuffle and then data grouped by fold using the array perm
int[] foldCount = new int[nrFold];
int[] index = new int[l];
for (i = 0; i < l; i++)
{
index[i] = perm[i];
}
for (int c = 0; c < nrClass; c++)
{
for (i = 0; i < count[c]; i++)
{
int j = i + rand.Next(count[c] - i);
do
{
int _ = index[start[c] + j];
index[start[c] + j] = index[start[c] + i];
index[start[c] + i] = _;
} while (false);
}
}
for (i = 0; i < nrFold; i++)
{
foldCount[i] = 0;
for (int c = 0; c < nrClass; c++)
{
foldCount[i] += (i + 1) * count[c] / nrFold - i * count[c] / nrFold;
}
}
foldStart[0] = 0;
for (i = 1; i <= nrFold; i++)
{
foldStart[i] = foldStart[i - 1] + foldCount[i - 1];
}
for (int c = 0; c < nrClass; c++)
{
for (i = 0; i < nrFold; i++)
{
int begin = start[c] + i * count[c] / nrFold;
int end = start[c] + (i + 1) * count[c] / nrFold;
for (int j = begin; j < end; j++)
{
perm[foldStart[i]] = index[j];
foldStart[i]++;
}
}
}
foldStart[0] = 0;
for (i = 1; i <= nrFold; i++)
{
foldStart[i] = foldStart[i - 1] + foldCount[i - 1];
}
}
else
{
for (i = 0; i < l; i++)
{
perm[i] = i;
}
for (i = 0; i < l; i++)
{
int j = i + rand.Next(l - i);
do
{
int _ = perm[i];
perm[i] = perm[j];
perm[j] = _;
} while (false);
}
for (i = 0; i <= nrFold; i++)
{
foldStart[i] = i * l / nrFold;
}
}
for (i = 0; i < nrFold; i++)
{
int begin = foldStart[i];
int end = foldStart[i + 1];
int j;
int count = l - (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 < l; j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
SvmModel submodel = SvmTrain(subprob, param);
if (param.probability && (param.svmType == SvmType.CSvc || param.svmType == SvmType.NuSvc))
{
double[] probEstimates = new double[submodel.nrClass];
for (j = begin; j < end; j++)
{
target[perm[j]] = SvmPredictProbability(submodel, prob.x[perm[j]], probEstimates);
}
}
else
{
for (j = begin; j < end; j++)
{
target[perm[j]] = SvmPredict(submodel, prob.x[perm[j]]);
}
}
}
}
//
// Interface functions
//
public static SvmModel SvmTrain(SvmProblem prob, SvmParameter param)
{
SvmModel model = new SvmModel {
param = param
};
if (param.svmType == SvmType.OneClass || param.svmType == SvmType.EpsilonSvr || param.svmType == SvmType.NuSvr)
{
// regression or one-class-svm
model.nrClass = 2;
model.label = null;
model.nSv = null;
model.probA = null;
model.probB = null;
model.svCoef = new double[1][];
if (param.probability && (param.svmType == SvmType.EpsilonSvr || param.svmType == SvmType.NuSvr))
{
model.probA = new double[1];
model.probA[0] = SvmSvrProbability(prob, param);
}
DecisionFunction f = SvmTrainOne(prob, param, 0, 0);
model.rho = new double[1];
model.rho[0] = f.rho;
int nSv = 0;
int i;
for (i = 0; i < prob.Count; i++)
{
if (Math.Abs(f.alpha[i]) > 0)
{
++nSv;
}
}
model.l = nSv;
model.sv = new BaseVector[nSv];
model.svCoef[0] = new double[nSv];
int j = 0;
for (i = 0; i < prob.Count; i++)
{
if (Math.Abs(f.alpha[i]) > 0)
{
model.sv[j] = prob.x[i];
model.svCoef[0][j] = f.alpha[i];
++j;
}
}
}
else
{
// classification
int l = prob.Count;
int[] tmpNrClass = new int[1];
int[][] tmpLabel = new int[1][];
int[][] tmpStart = new int[1][];
int[][] tmpCount = new int[1][];
int[] perm = new int[l];
// group training data of the same class
SvmGroupClasses(prob, tmpNrClass, tmpLabel, tmpStart, tmpCount, perm);
int nrClass = tmpNrClass[0];
int[] label = tmpLabel[0];
int[] start = tmpStart[0];
int[] count = tmpCount[0];
if (nrClass == 1)
{
Info("WARNING: training data in only one class. See README for details.\n");
}
BaseVector[] x = new BaseVector[l];
int i;
for (i = 0; i < l; i++)
{
x[i] = prob.x[perm[i]];
}
// calculate weighted C
double[] weightedC = new double[nrClass];
for (i = 0; i < nrClass; i++)
{
weightedC[i] = param.c;
}
for (i = 0; i < param.nrWeight; i++)
{
int j;
for (j = 0; j < nrClass; j++)
{
if (param.weightLabel[i] == label[j])
{
break;
}
}
if (j == nrClass)
{
Info("WARNING: class label " + param.weightLabel[i] + " specified in weight is not found\n");
}
else
{
weightedC[j] *= param.weight[i];
}
}
// train k*(k-1)/2 models
bool[] nonzero = new bool[l];
for (i = 0; i < l; i++)
{
nonzero[i] = false;
}
DecisionFunction[] f = new DecisionFunction[nrClass * (nrClass - 1) / 2];
double[] probA = null, probB = null;
if (param.probability)
{
probA = new double[nrClass * (nrClass - 1) / 2];
probB = new double[nrClass * (nrClass - 1) / 2];
}
int p = 0;
for (i = 0; i < nrClass; i++)
{
for (int j = i + 1; j < nrClass; j++)
{
int si = start[i], sj = start[j];
int ci = count[i], cj = count[j];
int c = ci + cj;
SvmProblem subProb = new SvmProblem {
x = new BaseVector[c], y = new float[c]
};
int k;
for (k = 0; k < ci; k++)
{
subProb.x[k] = x[si + k];
subProb.y[k] = +1;
}
for (k = 0; k < cj; k++)
{
subProb.x[ci + k] = x[sj + k];
subProb.y[ci + k] = -1;
}
if (param.probability)
{
double[] probAb = new double[2];
SvmBinarySvcProbability(subProb, param, weightedC[i], weightedC[j], probAb);
probA[p] = probAb[0];
probB[p] = probAb[1];
}
f[p] = SvmTrainOne(subProb, param, weightedC[i], weightedC[j]);
for (k = 0; k < ci; k++)
{
if (!nonzero[si + k] && Math.Abs(f[p].alpha[k]) > 0)
{
nonzero[si + k] = true;
}
}
for (k = 0; k < cj; k++)
{
if (!nonzero[sj + k] && Math.Abs(f[p].alpha[ci + k]) > 0)
{
nonzero[sj + k] = true;
}
}
++p;
}
}
// build output
model.nrClass = nrClass;
model.label = new int[nrClass];
for (i = 0; i < nrClass; i++)
{
model.label[i] = label[i];
}
model.rho = new double[nrClass * (nrClass - 1) / 2];
for (i = 0; i < nrClass * (nrClass - 1) / 2; i++)
{
model.rho[i] = f[i].rho;
}
if (param.probability)
{
model.probA = new double[nrClass * (nrClass - 1) / 2];
model.probB = new double[nrClass * (nrClass - 1) / 2];
for (i = 0; i < nrClass * (nrClass - 1) / 2; i++)
{
model.probA[i] = probA[i];
model.probB[i] = probB[i];
}
}
else
{
model.probA = null;
model.probB = null;
}
int nnz = 0;
int[] nzCount = new int[nrClass];
model.nSv = new int[nrClass];
for (i = 0; i < nrClass; i++)
{
int nSv = 0;
for (int j = 0; j < count[i]; j++)
{
if (nonzero[start[i] + j])
{
++nSv;
++nnz;
}
}
model.nSv[i] = nSv;
nzCount[i] = nSv;
}
Info("Total nSV = " + nnz + "\n");
model.l = nnz;
model.sv = new BaseVector[nnz];
p = 0;
for (i = 0; i < l; i++)
{
if (nonzero[i])
{
model.sv[p++] = x[i];
}
}
int[] nzStart = new int[nrClass];
nzStart[0] = 0;
for (i = 1; i < nrClass; i++)
{
nzStart[i] = nzStart[i - 1] + nzCount[i - 1];
}
model.svCoef = new double[nrClass - 1][];
for (i = 0; i < nrClass - 1; i++)
{
model.svCoef[i] = new double[nnz];
}
p = 0;
for (i = 0; i < nrClass; i++)
{
for (int j = i + 1; j < nrClass; j++)
{
// classifier (i,j): coefficients with
// i are in sv_coef[j-1][nz_start[i]...],
// j are in sv_coef[i][nz_start[j]...]
int si = start[i];
int sj = start[j];
int ci = count[i];
int cj = count[j];
int q = nzStart[i];
int k;
for (k = 0; k < ci; k++)
{
if (nonzero[si + k])
{
model.svCoef[j - 1][q++] = f[p].alpha[k];
}
}
q = nzStart[j];
for (k = 0; k < cj; k++)
{
if (nonzero[sj + k])
{
model.svCoef[i][q++] = f[p].alpha[ci + k];
}
}
++p;
}
}
}
return(model);
}
// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
// perm, length l, must be allocated before calling this subroutine
private static void SvmGroupClasses(SvmProblem prob, IList <int> nrClassRet, IList <int[]> labelRet,
IList <int[]> startRet, IList <int[]> countRet, IList <int> perm)
{
int l = prob.Count;
int maxNrClass = 16;
int nrClass = 0;
int[] label = new int[maxNrClass];
int[] count = new int[maxNrClass];
int[] dataLabel = new int[l];
int i;
for (i = 0; i < l; i++)
{
int thisLabel = (int)(prob.y[i]);
int j;
for (j = 0; j < nrClass; j++)
{
if (thisLabel == label[j])
{
++count[j];
break;
}
}
dataLabel[i] = j;
if (j == nrClass)
{
if (nrClass == maxNrClass)
{
maxNrClass *= 2;
int[] newData = new int[maxNrClass];
Array.Copy(label, 0, newData, 0, label.Length);
label = newData;
newData = new int[maxNrClass];
Array.Copy(count, 0, newData, 0, count.Length);
count = newData;
}
label[nrClass] = thisLabel;
count[nrClass] = 1;
++nrClass;
}
}
int[] start = new int[nrClass];
start[0] = 0;
for (i = 1; i < nrClass; i++)
{
start[i] = start[i - 1] + count[i - 1];
}
for (i = 0; i < l; i++)
{
perm[start[dataLabel[i]]] = i;
++start[dataLabel[i]];
}
start[0] = 0;
for (i = 1; i < nrClass; i++)
{
start[i] = start[i - 1] + count[i - 1];
}
nrClassRet[0] = nrClass;
labelRet[0] = label;
startRet[0] = start;
countRet[0] = count;
}
// 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);
}
public static string SvmCheckParameter(SvmProblem prob, SvmParameter param)
{
SvmType svmType = param.svmType;
// cache_size,eps,C,nu,p,shrinking
if (param.cacheSize <= 0)
{
return("cache_size <= 0");
}
if (param.eps <= 0)
{
return("eps <= 0");
}
if (svmType == SvmType.CSvc || svmType == SvmType.EpsilonSvr || svmType == SvmType.NuSvr)
{
if (param.c <= 0)
{
return("C <= 0");
}
}
if (svmType == SvmType.NuSvc || svmType == SvmType.OneClass || svmType == SvmType.NuSvr)
{
if (param.nu <= 0 || param.nu > 1)
{
return("nu <= 0 or nu > 1");
}
}
if (svmType == SvmType.EpsilonSvr)
{
if (param.p < 0)
{
return("p < 0");
}
}
if (param.probability && svmType == SvmType.OneClass)
{
return("one-class SVM probability output not supported yet");
}
// check whether nu-svc is feasible
if (svmType == SvmType.NuSvc)
{
int l = prob.Count;
int maxNrClass = 16;
int nrClass = 0;
int[] label = new int[maxNrClass];
int[] count = new int[maxNrClass];
int i;
for (i = 0; i < l; i++)
{
int thisLabel = (int)prob.y[i];
int j;
for (j = 0; j < nrClass; j++)
{
if (thisLabel == label[j])
{
++count[j];
break;
}
}
if (j == nrClass)
{
if (nrClass == maxNrClass)
{
maxNrClass *= 2;
int[] newData = new int[maxNrClass];
Array.Copy(label, 0, newData, 0, label.Length);
label = newData;
newData = new int[maxNrClass];
Array.Copy(count, 0, newData, 0, count.Length);
count = newData;
}
label[nrClass] = thisLabel;
count[nrClass] = 1;
++nrClass;
}
}
for (i = 0; i < nrClass; i++)
{
int n1 = count[i];
for (int j = i + 1; j < nrClass; j++)
{
int n2 = count[j];
if (param.nu * (n1 + n2) / 2 > Math.Min(n1, n2))
{
return("specified nu is infeasible");
}
}
}
}
return(null);
}