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hmmm, there are differences between libsvm and opencv's SVM.

e.g. coef0 is a single constant, and it's not even used in the case of a linear SVM

while you can get at the data, you would be doing something else in the prediction pass, than in the training pass.

it's probably a better idea, to implement a custom kernel:

hmmm, there are differences between libsvm and opencv's SVM.

e.g. coef0 is a single constant, and it's not even used in the case of a linear SVM

while you can get at the data, you would be doing something else in the prediction pass, than in the training pass.

it's probably a better idea, to implement a custom kernel:kernel:

struct XKernel : public ml::SVM::Kernel
{
    virtual ~XKernel() {}

    //! your distance metric between one of the support vecs(sample)
    //   and your query(another) goes here
    virtual float per_elem(int var_count, const float* sample, const float* another) = 0;

    //! post-process results array (if nessecary, e.g. apply bias)
    virtual void post(int vcount, float* results) {}

    void calc(int vcount, int var_count, const float* vecs, const float* another, float* results)
    {
        for (int j=0; j<vcount; j++)
        {
            const float* sample = &vecs[j*var_count];
            results[j] = per_elem(var_count, sample, another);
        }
        post(vcount, result);
    }

    int getType(void) const
    {
        return -1; // we're special.
    }

};

Ptr<ml::SVM> svm = ml::SVM::create();
Ptr<XKernel> kern = makePtr<XKernel>();
svm->setKernel(kern);

hmmm, there are differences between libsvm and opencv's SVM.

e.g. coef0 is a single constant, and it's not even used in the case of a linear SVM

while you can get at the data, you would be doing something else in the prediction pass, than in the training pass.

it's probably a better idea, to implement a custom kernel:

struct XKernel : public ml::SVM::Kernel
{
    virtual ~XKernel() {}

    //! your distance metric between one of the support vecs(sample)
    //   and your query(another) goes here
    virtual float per_elem(int var_count, const float* sample, const float* another) = 0;
{}

    //! post-process results array (if nessecary, e.g. apply bias)
    virtual void post(int vcount, float* results) {}

    void calc(int vcount, int var_count, const float* vecs, const float* another, float* results)
    {
        for (int j=0; j<vcount; j++)
        {
            const float* sample = &vecs[j*var_count];
            results[j] = per_elem(var_count, sample, another);
        }
        post(vcount, result);
    }

    int getType(void) const
    {
        return -1; // we're special.
    }

};

Ptr<ml::SVM> svm = ml::SVM::create();
Ptr<XKernel> kern = makePtr<XKernel>();
svm->setKernel(kern);