learn_control_demo.py 文件源码

def optimize_learner_dad(learner, X, U, iters, train_size = 0.5):
    num_traj = X.shape[2]
    if train_size < 1.0:
        from sklearn import cross_validation
        rs = cross_validation.ShuffleSplit(num_traj, n_iter=1, train_size=train_size, 
                random_state=0, test_size=1.-train_size)
        for train_index, test_index in rs:
            pass
        Xtrain = X[:,:,train_index]; Xtest = X[:,:,test_index]
        Utrain = U[:,:,train_index]; Utest = U[:,:,test_index]
    elif train_size == 1.0:
        Xtrain = X; Xtest = X
        Utrain = U; Utest = U
    else:
        raise Exception('Train size must be in (0,1]')

    dad = DaDControl()
    dad.learn(Xtrain, Utrain, learner, iters, Xtest, Utest, verbose=False)
    print(' DaD (iters:{:d}). Initial Err: {:.4g}, Best: {:.4g}'.format(iters,
        dad.initial_test_err, dad.min_test_error))
    return dad