def hessfunc(self, p):
        """
        The Hessian function that will be passed to the optimizer,
        if needed.
        """
        self._set_stochastics(p)
        for i in xrange(self.len):

            di = self.diff(i)
            self.hess[i, i] = self.diff(i, 2)

            if i < self.len - 1:

                for j in xrange(i + 1, self.len):
                    dij = self.diff2(i, j)

                    self.hess[i, j] = dij
                    self.hess[j, i] = dij

        return -1. * self.hess
def hessfunc ( self , p ) : self . _set_stochastics ( p ) for i in xrange ( self . len ) : di = self . diff ( i ) self . hess [ i , i ] = self . diff ( i , 2 ) if i < self . len - 1 : for j in xrange ( i + 1 , self . len ) : dij = self . diff2 ( i , j ) self . hess [ i , j ] = dij self . hess [ j , i ] = dij return - 1. * self . hess
The Hessian function that will be passed to the optimizer,
        if needed.