错误使用 / (line 65) Inverse model is noncausal because of delays.
时间: 2024-04-02 12:37:06 浏览: 19
这个错误通常是因为您正在尝试使用非因果模型进行计算,而非因果模型具有延迟。因此,在使用非因果模型进行计算时,您需要考虑这些延迟,并相应地调整您的计算。
您可以尝试使用因果模型或使用其他方法解决这个问题,例如对延迟进行补偿。如果您需要更多的帮助和指导,请提供更具体的上下文和代码,这样我才能更好地帮助您。
相关问题
a与b取反 a is the inverse of b
This statement is incorrect. The correct statement should be "a is the inverse of b" if and only if "b is the inverse of a." This is because the concept of "inverse" requires a mutual relationship between two elements, where each is the inverse of the other.
train_predict = scaler.inverse_transform(train_predict.reshape(-1, 1)) y_train = scaler.inverse_transform([y_train.reshape(-1, 1)]) train_predict = np.tile(train_predict, (1, 4)) y_train = np.tile(y_train, (1, 4)) y_train = scaler.inverse_transform(y_train.T).reshape(110, 4) test_predict = scaler.inverse_transform(test_predict) y_test = scaler.inverse_transform([y_test])报错Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 48, in <module> train_predict = scaler.inverse_transform(train_predict.reshape(-1, 1)) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (110,1) doesn't match the broadcast shape (110,4)
这个报错信息和之前的一样,都是因为在进行逆归一化操作时,输出的形状与广播形状不匹配导致的。具体来说,你在对训练数据进行逆归一化操作时,复制了4次train_predict和y_train,让它们的形状变成了(110,4)和(1,440),而逆归一化操作的结果形状是(110,1),所以就无法与广播形状(110,4)匹配了。
解决这个问题的方法就是在进行逆归一化操作之前,先将train_predict和y_train的形状改为(110,1)和(440,1),这样逆归一化操作的结果形状就能与广播形状匹配了。你可以使用numpy的reshape方法来改变形状,具体代码如下:
```
train_predict = scaler.inverse_transform(train_predict.reshape(-1, 1))
y_train = scaler.inverse_transform(y_train.reshape(-1, 1))
train_predict = np.tile(train_predict, (1, 4))
y_train = np.tile(y_train, (1, 4))
y_train = scaler.inverse_transform(y_train.T).reshape(110, 4)
test_predict = scaler.inverse_transform(test_predict)
y_test = scaler.inverse_transform([y_test])
```
这样修改后,就能够顺利地进行逆归一化操作了。