kgof/kernel.py
def eval(self, X, Y):
        """
        Equivalent to dividing each dimension with the corresponding width (not
        width^2) and using the standard Gaussian kernel.
        """
        sigma2s = self.sigma2s
        Xs = old_div(X,np.sqrt(sigma2s))
        Ys = old_div(Y,np.sqrt(sigma2s))
        k = KGauss(1.0)
        return k.eval(Xs, Ys)

        Equivalent to dividing each dimension with the corresponding width (not
        width^2) and using the standard Gaussian kernel.
        
Equivalent to dividing each dimension with the corresponding width (not
width^2) and using the standard Gaussian kernel.
Equivalent to dividing each dimension with the corresponding width ( not width^2 ) and using the standard Gaussian kernel .
def eval(self, X, Y):
        sigma2s = self.sigma2s
        Xs = old_div(X,np.sqrt(sigma2s))
        Ys = old_div(Y,np.sqrt(sigma2s))
        k = KGauss(1.0)
        return k.eval(Xs, Ys)
def eval ( self , X , Y ) : sigma2s = self . sigma2s Xs = old_div ( X , np . sqrt ( sigma2s ) ) Ys = old_div ( Y , np . sqrt ( sigma2s ) ) k = KGauss ( 1.0 ) return k . eval ( Xs , Ys )
Equivalent to dividing each dimension with the corresponding width (not
width^2) and using the standard Gaussian kernel.
Equivalent to dividing each dimension with the corresponding width ( not width^2 ) and using the standard Gaussian kernel .
"""
        Equivalent to dividing each dimension with the corresponding width (not
        width^2) and using the standard Gaussian kernel.
        """
self
X
Y