np.cumsum(input) 的长度比输入数组长——这怎么可能？

如何解决np.cumsum(input) 的长度比输入数组长——这怎么可能？

在 Colab 中运行以下代码。运行两个单独的实例（单独的文件）。在一种情况下，代码有效，而在另一种情况下，它不起作用。在它不起作用的情况下，函数 np.cumsum() 似乎返回的数组长度是输入数组的两倍，这会创建一个 ValueError：“操作数无法与形状一起广播 (2000,) (1000,)”。
无法弄清楚它为什么会发生，或者它是如何可能的。也无法在网上找到任何答案（甚至是同一问题的类似实例），因此非常感谢您的帮助！！！

'''

def b2_run_advanced_strategies_experiment(env_name='BanditTwoArmedUniform-v0'):
    results = {}
    experiments = [
        # baseline strategies 
        lambda env: pure_exploitation(env,N_Episodes),lambda env: pure_exploration(env,]
    for env_seed in tqdm(SEEDS,desc='All experiments'):
        env = gym.make(env_name,seed=env_seed) ; env.reset()
        true_Q = np.array(env.env.p_dist * env.env.r_dist)
        opt_V = np.max(true_Q)
        for seed in tqdm(SEEDS,desc='All environments',leave=False):
            for experiment in tqdm(experiments,desc='Experiments with seed {}'.format(seed),leave=False):
                env.seed(seed) ; np.random.seed(seed) ; random.seed(seed)
                name,Re,Qe,Ae = experiment(env)
                Ae = np.expand_dims(Ae,-1)                
                print("len of Re=",len(Re)) # RESULT GIVES 1000
                print("len of cumsum= ",len(np.cumsum(Re))) # RESULT GIVES 2000,HOW IS THAT POSSIBLE???
                
                episode_mean_rew = np.cumsum(Re) / (np.arange(len(Re)) + 1) # ERROR ON THIS LINE

                Q_selected = np.take_along_axis(
                    np.tile(true_Q,Ae.shape),Ae,axis=1).squeeze()
                regret = opt_V - Q_selected
                cum_regret = np.cumsum(regret)
                if name not in results.keys(): results[name] = {}
                if 'Re' not in results[name].keys(): results[name]['Re'] = []
                if 'Qe' not in results[name].keys(): results[name]['Qe'] = []
                if 'Ae' not in results[name].keys(): results[name]['Ae'] = []
                if 'cum_regret' not in results[name].keys(): 
                    results[name]['cum_regret'] = []
                if 'episode_mean_rew' not in results[name].keys(): 
                    results[name]['episode_mean_rew'] = []

                results[name]['Re'].append(Re)
                results[name]['Qe'].append(Qe)
                results[name]['Ae'].append(Ae)
                results[name]['cum_regret'].append(cum_regret)
                results[name]['episode_mean_rew'].append(episode_mean_rew)
    return results

b2_results_a = b2_run_advanced_strategies_experiment()

'''

解决方法

您的数组可能是多维的：

eye3 = np.eye(3)
print(len(eye3)) # 3 (3 rows)
print(len(np.cumsum(eye3))) # 9 (3 rows * 3 columns = 9 elements once flattened)
print(len(np.cumsum(eye3,axis=1))) # 3 (3 rows)