void SupportVectorMachine::add_vector(const vectorD &source,
vectorD& dest, double factor)
{
/**
* @Brief: Add two vector with factor multiplying
* dest += source * factor
*/
EQUAL(source.size(), dest.size());
for (unsigned long i = 0; i < dest.size(); ++i)
{
dest[i] += source[i] * factor;
}
}
GOGONEURO::matrixD GOGONEURO::mat(const vectorD& x){
int n=x.size();
matrixD C(1,n,0.0);
for(int i=0;i<n;i++) C[0][i]=x[i];
return C;
}
SupportVectorMachine::SupportVectorMachine(std::vector<vectorD> data,
vectorD label,double C,
std::function<double(vectorD,vectorD)> kernel)
: C(C), data(data), label(label), b(0), epsilon(0.001), kernel(kernel)
{
/**
* @Brief: Set the default alpha and b to be 0
*/
static std::random_device rd;
static std::mt19937 gen(rd());
static std::uniform_real_distribution<> dis(0, std::min(C, 2./data.size()));
if (data.size() != label.size() || data.size() * C < 1)
{
std::logic_error("Size unfitted\n");
}
alpha = std::vector<double>(label.size(), 0);
omega = std::vector<double>(data[0].size(), 0);
}