def __init__(self, x, y=None, kernel=kernel_rbf,
                 scale=0.5, ridgecoeff = 1e-10, **kwds ):
        '''
        Parameters
        ----------
        x : 2d array (N,K)
           data array of explanatory variables, columns represent variables
           rows represent observations
        y : 2d array (N,1) (optional)
           endogenous variable that should be fitted or predicted
           can alternatively be specified as parameter to fit method
        kernel : function, default: kernel_rbf
           kernel: (x1,x2)->kernel matrix is a function that takes as parameter
           two column arrays and return the kernel or distance matrix
        scale : float (optional)
           smoothing parameter for the rbf kernel
        ridgecoeff : float (optional)
           coefficient that is multiplied with the identity matrix in the
           ridge regression

        Notes
        -----
        After initialization, kernel matrix is calculated and if y is given
        as parameter then also the linear regression parameter and the
        fitted or estimated y values, yest, are calculated. yest is available
        as an attribute in this case.

        Both scale and the ridge coefficient smooth the fitted curve.

        '''

        self.x = x
        self.kernel = kernel
        self.scale = scale
        self.ridgecoeff = ridgecoeff
        self.distxsample = kernel(x,x,scale=scale)
        self.Kinv = np.linalg.inv(self.distxsample -
                             np.eye(*self.distxsample.shape)*ridgecoeff)
        if not y is None:
            self.y = y
            self.yest = self.fit(y)
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/sandbox/regression/kernridgeregress_class.py/GaussProcess.__init__/operator `+` -> `-`