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Support for negative examples in one-class added #74

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Support for negative examples in one-class added #74

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7 changes: 7 additions & 0 deletions README
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Original file line number Diff line number Diff line change
Expand Up @@ -768,3 +768,10 @@ The authors thank their group members and users
for many helpful discussions and comments. They are listed in
http://www.csie.ntu.edu.tw/~cjlin/libsvm/acknowledgements



------------
Derived version (by Lukáš Ondráček):
Added support for learning from both positive and negative examples in one-class classification
according to the "One-class classification" thesis written by door David Martinus Johannes TAX.
Array y should now contain +1s for positive examples and -1s for negative ones.
21 changes: 15 additions & 6 deletions svm.cpp
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Expand Up @@ -1316,9 +1316,10 @@ class SVC_Q: public Kernel
class ONE_CLASS_Q: public Kernel
{
public:
ONE_CLASS_Q(const svm_problem& prob, const svm_parameter& param)
ONE_CLASS_Q(const svm_problem& prob, const svm_parameter& param, const schar *y_)
:Kernel(prob.l, prob.x, param)
{
clone(y,y_,prob.l);
cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20)));
QD = new double[prob.l];
for(int i=0;i<prob.l;i++)
Expand All @@ -1332,7 +1333,7 @@ class ONE_CLASS_Q: public Kernel
if((start = cache->get_data(i,&data,len)) < len)
{
for(j=start;j<len;j++)
data[j] = (Qfloat)(this->*kernel_function)(i,j);
data[j] = (Qfloat)(y[i]*y[j]*(this->*kernel_function)(i,j));
}
return data;
}
Expand All @@ -1351,10 +1352,12 @@ class ONE_CLASS_Q: public Kernel

~ONE_CLASS_Q()
{
delete[] y;
delete cache;
delete[] QD;
}
private:
schar *y;
Cache *cache;
double *QD;
};
Expand Down Expand Up @@ -1533,7 +1536,7 @@ static void solve_one_class(
{
int l = prob->l;
double *zeros = new double[l];
schar *ones = new schar[l];
schar *y = new schar[l];
int i;

int n = (int)(param->nu*prob->l); // # of alpha's at upper bound
Expand All @@ -1548,15 +1551,21 @@ static void solve_one_class(
for(i=0;i<l;i++)
{
zeros[i] = 0;
ones[i] = 1;
if(prob->y[i]>0)
y[i] = +1;
else
y[i] = -1;
}

Solver s;
s.Solve(l, ONE_CLASS_Q(*prob,*param), zeros, ones,
s.Solve(l, ONE_CLASS_Q(*prob,*param,y), zeros, y,
alpha, 1.0, 1.0, param->eps, si, param->shrinking);

for(i=0; i<l; i++)
alpha[i] *= y[i];

delete[] zeros;
delete[] ones;
delete[] y;
}

static void solve_epsilon_svr(
Expand Down