poly_features=np.power(features,np.arange(max_degree).reshape(1,-1))
时间: 2024-05-27 16:11:27 浏览: 14
This code creates a matrix of polynomial features from the input features. The variable "max_degree" specifies the highest degree of the polynomial features.
The function "np.power()" raises each element of the input features to the power of the corresponding element in the sequence of degrees from 0 to (max_degree-1). The resulting matrix has one row for each input feature and one column for each degree of the polynomial.
For example, if the input features are [x1, x2] and max_degree=3, the resulting matrix would be:
[[1, x1, x1^2, x1^3],
[1, x2, x2^2, x2^3]]
相关问题
poly_features=np.power(features,np.arange(max_degree).reshape(1,-1))什么意思
这行代码的意思是将features的每个元素分别取0到max_degree-1次幂,并将结果按行组成一个二维数组poly_features。其中,np.power函数是numpy库中的函数,可以计算数组元素的幂。reshape(1,-1)的作用是将一维数组转换为二维数组,其中第一个维度为1,第二个维度自动计算。
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。
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