# SVM radial basis generate equation for hyperplane

I need to generate an equation for hyperplane, I have two independent variables and one binary dependent variable.

Regarding this following equation for svm , $$f(x)=sgn( sum_i alpha_i K(sv_i,x) + b )$$

I have two independent variables (say P and Q) with 130 point values for each variable. I used svm radial basis function for binary classification (0 and 1) and I calculated for radial basis kernelized case, and now I have

• One column of 51 y (i) alpha (i) or (dual coefficients).

• Two columns of 51 sv (support vectors)for P and Q.

• One single value for b .

I received these using scikit SVC.

So, how can I generate the equation now?

Can I multiply those 51 y (i) alpha (i) or (dual coefficients) with 51 sv (support vectors) for each variable P and Q so that I have two coefficients for P and Q so that finally my equation appears as f(x)=sgn( mP + nQ +b) where m = sum of the (product of 51 sv of P with 51 dual coefficients) and n = sum of the (product of 51 sv of Q with 51 dual coefficients)?

$$k(x,y)=\phi(x)\cdot \phi(y)$$
$$\phi(x)$$ - mapping