def fit(self,y):
        self.parest = np.dot(self.Kinv, y) #self.kernel(y,y,scale=self.scale))
        yhat = np.dot(self.distxsample,self.parest)
        return yhat

##        print ds33.shape
##        ds33_2 = kernel(x,x[::k,:],scale=scale)
##        dsinv = np.linalg.inv(ds33+np.eye(*distxsample.shape)*ridgecoeff)
##        B = np.dot(dsinv,y[::k,:])
fit the training explanatory variables to a sample ouput variable
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/sandbox/regression/kernridgeregress_class.py/GaussProcess.fit