(self, name)
return np.sqrt(max(raw_var - np.square(mean), 0))
r"""Returns the standard deviation of the scalars that were
        accumulated for the given statistic between the last two calls to
        `update()`, or NaN if no scalars were collected.
r"""Returns the standard deviation of the scalars that were
        accumulated for the given statistic between the last two calls to
        `update()`, or NaN if no scalars were collected.
r Returns the standard deviation of the scalars that were accumulated for the given statistic between the last two calls to update () or NaN if no scalars were collected .
def std(self, name):
        r"""Returns the standard deviation of the scalars that were
        accumulated for the given statistic between the last two calls to
        `update()`, or NaN if no scalars were collected.
        """
        delta = self._get_delta(name)
        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
            return float('nan')
        if int(delta[0]) == 1:
            return float(0)
        mean = float(delta[1] / delta[0])
        raw_var = float(delta[2] / delta[0])
        return np.sqrt(max(raw_var - np.square(mean), 0))
def std ( self , name ) : delta = self . _get_delta ( name ) if int ( delta [ 0 ] ) == 0 or not np . isfinite ( float ( delta [ 1 ] ) ) : return float ( 'nan' ) if int ( delta [ 0 ] ) == 1 : return float ( 0 ) mean = float ( delta [ 1 ] / delta [ 0 ] ) raw_var = float ( delta [ 2 ] / delta [ 0 ] ) return np . sqrt ( max ( raw_var - np . square ( mean ) , 0 ) )