Kernel Ridge Classifier on Python. You can use your own kernel.

KernelRidgeClassifier.py

from sklearn.base import BaseEstimatior
class KernelRidgeClassifier(BaseEstimator):
    def __init__(self,alpha=0.1,**kwds):
        self.alpha=alpha
        self.kwds=kwds

    def fit(self,X,y):
        n=len(y)
        K=array([[self.kwds["kernel"](xi,xj) for xj in X] for xi in X])
        self.theta=pinv(K+self.alpha*identity(n)).dot(c_[y])
        self.X=X
        return self

    def predict_proba(self,X):
        K=array([[self.kwds["kernel"](xi,xj) for xj in self.X] for xi in X])
        return K.dot(self.theta).ravel()

    def predict(self,X):
        proba=self.predict_proba(X)
        return 2*(proba>=0)-1

    def score(self,X,y):
        return sum(self.predict(X)==y)/float(len(y))

how can i put a kernel in this code?
a tried to use it, but i have no idea how to use the code
and some of the functions give me an error
I'll be thankful if you can help me
(sorry for my english)