title: How to tune the parameters of custom kernel with GridSearchCV in sklearn?
body: <p>I have a custom kernel which needs tuning parameters like α and γ，take this kernel function as K(x,y;α,γ),I needs tuning its parameters for support vector machine as well as the cost parameter C of svm. I found that there is a built-in function in sklearn GridSearchCV which can be used to tune some built-in kernel for svm, Could you tell me how to adapt it for a custom kernel, such as kernel like  K(x,y;α,γ)=np.dot(x,y.T)*(np.sin(α)+np.cos(γ)), α∈[0,π/2],γ∈[0,π/2].</p>