以下代码显示了我面临的问题：“”

def fakeDataGenerator(chanNum=31):

# This function generates the data I want to recover and it shows the characters of the data I am working on. It's continuous and differentiable.

    peaks = random.sample(range(chanNum), random.choice(range(3,10)))
    peaks.append(chanNum)
    peaks.sort()
    out = [random.choice(range(-5, 5))]
    delta = 1
    while len(out) < chanNum:
        if len(out) < peaks[0]:
            out.append(out[-1]+delta)
        elif len(out) == peaks[0]:
            delta *= -1
            peaks.pop(0)
    return out

originalData = torch.tensor(fakeDataGenerator(31)).reshape(1, 31).float()

encoder = torch.rand((31, 9)).float() #encoder here is something that messed the data up
code = torch.matmul(originalData, encoder) #here we get the code which is messed up by the encoder

decoder = torch.pinverse(encoder) #We can make use of the encoder matrix to decode the data.

# For example, here I apply pinverse to recover the data, but...

decoded = torch.matmul(code, decoder)
print(decoded - originalData)  #the result is no good.

我能否利用原始数据和编码器的特性更好地恢复原始数据？这个程序工作的环境不允许像神经网络这样的复杂模型。