def smoothconf_(self, xs, ys, x):
        xs, ys = self.in_domain(xs, ys, x)

        if len(xs) > 0:
            fittedvals = np.array([self.smooth(xs, ys, xx) for xx in xs])
            rs = square(subtract(ys, fittedvals))
            w = np.sum(square(subtract(1.0, square(divide(subtract(xs, x),
                                                        self.h)))))
            v = np.sum(multiply(rs, square(subtract(1, square(divide(
                                                subtract(xs, x), self.h))))))
            var = v / w
            sd = np.sqrt(var)
            K = self.L2Norm
            yhat = self.smooth(xs, ys, x)
            err = sd * K / np.sqrt(0.9375 * w * self.h)
            return (yhat - err, yhat, yhat + err)
        else:
            return (np.nan, np.nan, np.nan)
Returns the kernel smoothing estimate with confidence 1sigma bounds
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/sandbox/nonparametric/kernels.py/Biweight.smoothconf_