修改后，反归一没有实现 ：def FNormalizeMultUseData(data,normalize): data = np.array(data) listlow = normalize[-1, 0] listhigh = normalize[-1, 1] delta = listhigh - listlow if delta != 0: for j in range(0,data.shape[0]): data[j,-1] = (data[j,-1] - listlow)/delta data[j,-1] = data[j,-1]*(normalize[-1,1]-normalize[-1,0])+normalize[-1,0] for i in range(0, data.shape[1]-1): listlow = normalize[i, 0] listhigh = normalize[i, 1] delta = listhigh - listlow if delta != 0: for j in range(0,data.shape[0]): data[j,i] = (data[j,i] - listlow)/delta return data

根据你提供的代码来看，确实没有实现反归一化。可以对该函数进行修改，加入反归一化的实现，如下所示：

def FNormalizeMultUseData(data, normalize):
    data = np.array(data)
    for i in range(data.shape[1] - 1):
        listlow = normalize[i, 0]
        listhigh = normalize[i, 1]
        delta = listhigh - listlow
        if delta != 0:
            for j in range(data.shape[0]):
                data[j, i] = (data[j, i] - listlow) / delta
    listlow = normalize[-1, 0]
    listhigh = normalize[-1, 1]
    delta = listhigh - listlow
    if delta != 0:
        for j in range(data.shape[0]):
            data[j, -1] = (data[j, -1] - listlow) / delta
    # 反归一化
    for i in range(data.shape[1] - 1):
        listlow = normalize[i, 0]
        listhigh = normalize[i, 1]
        delta = listhigh - listlow
        if delta != 0:
            for j in range(data.shape[0]):
                data[j, i] = data[j, i] * delta + listlow
    listlow = normalize[-1, 0]
    listhigh = normalize[-1, 1]
    delta = listhigh - listlow
    if delta != 0:
        for j in range(data.shape[0]):
            data[j, -1] = data[j, -1] * delta + listlow
    return data

这里的反归一化代码与归一化代码类似，只是将归一化的计算过程反过来进行计算。对于每一列数据，先计算出归一化时的差值delta，然后按照相应的比例进行反归一化处理。最后返回反归一化后的数据即可。