yhat = torch.from_numpy(input_x)

As an AI language model, I don't have access to the context or the data used in the code. However, based on the syntax, it seems like this line of code is converting a NumPy array named "input_x" into a PyTorch tensor named "yhat". The torch.from_numpy() function is used to convert a NumPy array into a PyTorch tensor. This line of code suggests that the input data is being prepared for a PyTorch model.

相关问题

请分析这段代码yhat = torch.from_numpy(input_x)

这段代码将 NumPy 数组 input_x 转换为 PyTorch 张量 yhat。具体来说，它使用 PyTorch 中的 from_numpy() 函数将 input_x 转换为 PyTorch 张量。这个函数创建了一个新的张量，其数据是从 NumPy 数组中获取的。因此，yhat 将包含与 input_x 相同的数据，但是它是 PyTorch 张量，可以在 PyTorch 模型中使用。

inv_yhat = scaler.inverse_transform(inv_yhat)

This line of code is used to invert the scaling applied to the predicted values (inv_yhat). In machine learning models, it is common to apply feature scaling to the input data before training the model. This is done to normalize the data and improve the performance of the model. However, the predicted values are also scaled and need to be inverted to their original scale before they can be compared to the actual values.

In this case, the "scaler" object is a scaler that was used to normalize the data before training the model. The "inverse_transform" method of the scaler is used to invert the scaling applied to the predicted values (inv_yhat) and convert them back to their original scale. The resulting values are then stored in the "inv_yhat" variable.