def predict_y(self, xq, sigma=None, k=None, **kwargs):
        """Provide an prediction of xq in the output space

        @param xq  an array of float of length dim_x
        """
        sigma = sigma or self.sigma
        k = k or self.k
        dists, index = self.dataset.nn_x(xq, k = k)
        w = self._weights(dists, sigma*sigma)
        return np.sum([wi*self.dataset.get_y(idx) for wi, idx in zip(w, index)], axis = 0)
def predict_y ( self , xq , sigma = None , k = None , ** kwargs ) : sigma = sigma or self . sigma k = k or self . k dists , index = self . dataset . nn_x ( xq , k = k ) w = self . _weights ( dists , sigma * sigma ) return np . sum ( [ wi * self . dataset . get_y ( idx ) for wi , idx in zip ( w , index ) ] , axis = 0 )
Provide an prediction of xq in the output space

        @param xq  an array of float of length dim_x