wf_first_pass.py 文件源码

def bellman_operator(pgrid, c, f0, f1, L0, L1, J):
    """
    Evaluates the value function for a given continuation value
    function; that is, evaluates

        J(p) = min((1 - p) L0, p L1, c + E J(p'))

    Uses linear interpolation between points.
    """
    m = np.size(pgrid)
    assert m == np.size(J)

    J_out = np.zeros(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 = expect_loss_choose_0(p, L0)
        p_c_1 = expect_loss_choose_1(p, L1)
        p_con = expect_loss_cont(p, c, f0, f1, J_interp)

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

    return J_out


#  == Now run at given parameters == #

#  First set up distributions