def gradient_descent(self, start_x=None, tolerance=1.0e-6):
        """
        Optimise value of x using gradient descent
        """
        if start_x is None:
            start_x = self._analytical_fitter.fit(self._c)
        return optimise_gradient_descent(start_x, self._a, self._c, tolerance)
def gradient_descent ( self , start_x = None , tolerance = 1.0e-6 ) : if start_x is None : start_x = self . _analytical_fitter . fit ( self . _c ) return optimise_gradient_descent ( start_x , self . _a , self . _c , tolerance )
Optimise value of x using gradient descent