/// <summary>
/// Classifies the value as belonging to either class -1 or class 1.
/// </summary>
public int Classify(TValue value)
{
var sum = SupportVectors.Sum(
s => s.Alpha * s.Observation.Label * _kernel.Compute(s.Observation.Value, value));
sum -= _b;
return(sum < 0 ? -1 : 1);
}
private int TakeStep(int i1, int i2)
{
if (i1 == i2)
{
return(0);
}
double alpha1 = _alphas[i1];
int y1 = _trainingSet[i1].Label;
double E1;
if (alpha1 > 0 && alpha1 < C)
{
E1 = _errorCache[i1];
}
else
{
E1 = SvmOutput(i1) - y1;
}
int s = y1 * _y2;
double L, H;
if (s == 1)
{
L = Math.Max(0, alpha1 + _alpha2 - C);
H = Math.Min(C, alpha1 + _alpha2);
}
else
{
L = Math.Max(0, _alpha2 - alpha1);
H = Math.Min(C, C + _alpha2 - alpha1);
}
if (Math.Abs(L - H) < 10E-9)
{
return(0);
}
double k11 = _k.Compute(_trainingSet[i1].Value, _trainingSet[i1].Value);
double k12 = _k.Compute(_trainingSet[i1].Value, _trainingSet[i2].Value);
double k22 = _k.Compute(_trainingSet[i2].Value, _trainingSet[i2].Value);
double eta = 2 * k12 - k11 - k22;
double newAlpha2;
double Lobj, Hobj;
if (eta < 0)
{
newAlpha2 = _alpha2 - _y2 * (E1 - _e2) / eta;
if (newAlpha2 < L)
{
newAlpha2 = L;
}
else if (newAlpha2 > H)
{
newAlpha2 = H;
}
}
else
{
double c1 = eta / 2;
double c2 = _y2 * (E1 - _e2) - eta * _alpha2;
Lobj = c1 * L * L + c2 * L;
Hobj = c1 * H * H + c2 * H;
if (Lobj > Hobj + Eps)
{
newAlpha2 = L;
}
else if (Lobj < Hobj - Eps)
{
newAlpha2 = H;
}
else
{
newAlpha2 = _alpha2;
}
}
if (Math.Abs(newAlpha2 - _alpha2) < Eps * (newAlpha2 + _alpha2 + Eps))
{
return(0);
}
var newAlpha1 = alpha1 - s * (newAlpha2 - _alpha2);
if (newAlpha1 < 0)
{
newAlpha2 += s * newAlpha1;
newAlpha1 = 0;
}
else if (newAlpha1 > C)
{
newAlpha2 += s * (newAlpha1 - C);
newAlpha1 = C;
}
double bNew;
if (newAlpha1 > 0 && newAlpha1 < C)
{
bNew = _b + E1 + y1 * (newAlpha1 - alpha1) * k11 + _y2 * (newAlpha2 - _alpha2) * k12;
}
else
{
if (newAlpha2 > 0 && newAlpha2 < C)
{
bNew = _b + _e2 + y1 * (newAlpha1 - alpha1) * k12 + _y2 * (newAlpha2 - _alpha2) * k22;
}
else
{
var b1 = _b + E1 + y1 * (newAlpha1 - alpha1) * k11 + _y2 * (newAlpha2 - _alpha2) * k12;
var b2 = _b + _e2 + y1 * (newAlpha1 - alpha1) * k12 + _y2 * (newAlpha2 - _alpha2) * k22;
bNew = (b1 + b2) / 2;
}
}
var deltaB = _b - bNew;
_b = bNew;
double t1 = y1 * (newAlpha1 - alpha1);
double t2 = _y2 * (newAlpha2 - _alpha2);
for (int i = 0; i < _errorCache.Length; i++)
{
if (_alphas[i] > 0 && _alphas[i] < C)
{
_errorCache[i] += t1 * _k.Compute(_trainingSet[i1].Value, _trainingSet[i].Value) +
t2 * _k.Compute(_trainingSet[i2].Value, _trainingSet[i].Value) + deltaB;
}
}
_errorCache[i1] = _errorCache[i2] = 0;
_alphas[i1] = newAlpha1;
_alphas[i2] = newAlpha2;
return(1);
}