losses.py 文件源码

def find_proximal(x0, gam, lam, eps=1e-6, max_steps=20, debug={}):
    # x0: sorted margins data
    # gam: initial gamma_fast(target, perm)
    # regularisation parameter lam
    x = x0.clone()
    act = (x >= eps).nonzero()
    finished = False
    if not act.size():
        finished = True
    else:
        active = act[-1, 0]
        members = {i: {i} for i in range(active + 1)}
        if active > 0:
            equal = (x[:active] - x[1:active+1]) < eps
            for i, e in enumerate(equal):
                if e:
                    members[i].update(members[i + 1])
                    members[i + 1] = members[i]
            project(gam, active, members)
    step = 0
    while not finished and step < max_steps and active > -1:
        step += 1
        res = compute_step_length(x, gam, active, eps)
        delta, ind = res

        if ind == -1:
            active = active - len(members[active])

        stop = torch.dot(x - x0, gam) / torch.dot(gam, gam) + 1. / lam
        if 0 <= stop < delta:
            delta = stop
            finished = True

        x = x - delta * gam
        if not finished:
            if ind >= 0:
                repr = min(members[ind])
                members[repr].update(members[ind + 1])
                for m in members[ind]:
                    if m != repr:
                        members[m] = members[repr]
            project(gam, active, members)
        if "path" in debug:
            debug["path"].append(x.numpy())

    if "step" in debug:
        debug["step"] = step
    if "finished" in debug:
        debug["finished"] = finished
    return x, gam