Algorithm-SVM

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libsvm.cpp  view on Meta::CPAN

			{
				if (!be_shrunken(active_size, Gmax1, Gmax2))
				{
					swap_index(i,active_size);
					break;
				}
				active_size--;
			}
		}

	// unshrink, check all variables again before final iterations

	if(unshrinked || Gmax1 + Gmax2 > eps*10) return;
	
	unshrinked = true;
	reconstruct_gradient();

	for(i=l-1;i>=active_size;i--)
		if (!be_shrunken(i, Gmax1, Gmax2))
		{
			while (active_size < i)

libsvm.cpp  view on Meta::CPAN

			{
				if (!be_shrunken(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
				{
					swap_index(i,active_size);
					break;
				}
				active_size--;
			}
		}

	// unshrink, check all variables again before final iterations

	if(unshrinked || max(Gmax1+Gmax2,Gmax3+Gmax4) > eps*10) return;
	
	unshrinked = true;
	reconstruct_gradient();

	for(i=l-1;i>=active_size;i--)
		if (!be_shrunken(i, Gmax1, Gmax2, Gmax3, Gmax4))
		{
			while (active_size < i)

libsvm.cpp  view on Meta::CPAN

	int l, const double *dec_values, const double *labels, 
	double& A, double& B)
{
	double prior1=0, prior0 = 0;
	int i;

	for (i=0;i<l;i++)
		if (labels[i] > 0) prior1+=1;
		else prior0+=1;
	
	int max_iter=100; 	// Maximal number of iterations
	double min_step=1e-10;	// Minimal step taken in line search
	double sigma=1e-12;	// For numerically strict PD of Hessian
	double eps=1e-5;
	double hiTarget=(prior1+1.0)/(prior1+2.0);
	double loTarget=1/(prior0+2.0);
	double *t=Malloc(double,l);
	double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize;
	double newA,newB,newf,d1,d2;
	int iter; 
	

libsvm.cpp  view on Meta::CPAN

		}

		if (stepsize < min_step)
		{
			info("Line search fails in two-class probability estimates\n");
			break;
		}
	}

	if (iter>=max_iter)
		info("Reaching maximal iterations in two-class probability estimates\n");
	free(t);
}

double sigmoid_predict(double decision_value, double A, double B)
{
	double fApB = decision_value*A+B;
	if (fApB >= 0)
		return exp(-fApB)/(1.0+exp(-fApB));
	else
		return 1.0/(1+exp(fApB)) ;