wald_class.py 文件源码

def bellman_operator(self, J):
        """
        Evaluates the value function for a given continuation value
        function; that is, evaluates

            J(p) = min(pL0, (1-p)L1, c + E[J(p')])

        Uses linear interpolation between points
        """
        payoff_choose_f0 = self.payoff_choose_f0
        payoff_choose_f1 = self.payoff_choose_f1
        payoff_continue = self.payoff_continue
        c, L0, L1, f0, f1 = self.c, self.L0, self.L1, self.f0, self.f1
        m, pgrid = self.m, self.pgrid

        J_out = np.empty(m)
        J_interp = interp.UnivariateSpline(pgrid, J, k=1, ext=0)

        for (p_ind, p) in enumerate(pgrid):
            # Payoff of choosing model 0
            p_c_0 = payoff_choose_f0(p)
            p_c_1 = payoff_choose_f1(p)
            p_con = payoff_continue(p, J_interp)

            J_out[p_ind] = min(p_c_0, p_c_1, p_con)

        return J_out