Applies all basis functions to the input and concatenates the results. Args: x (Tensor): Input tensor of shape (..., feature_dim).

r"""Calculate the Area Under the Receiver Operating Characteristic Curve (ROC AUC)

r"""Calculate the F1 score, a weighted average of precision and recall

r"""Calculate the precision, the ability of the classifier not to label a sample as positive if it is negative

r"""Calculate the recall, the ability of the classifier to find all the positive samples

r"""Print the network structure and output the number of network parameters.

r"""Print the network structure and output the number of network parameters.

r"""Train the network with training data. Args: X (numpy ndarray): Training data. y (numpy ndarray): Label data.

r"""Train the network with training data. Args: X (numpy ndarray): Training data. y (numpy ndarray): Label data.

r"""The symbolic regression algorithm is performed to fit the data. Args: X: Input data. y: Label.

前向传播：对每个编译好的子表达式计算张量，再分别做线性变换，最后求和加偏置。

r"""Forward pass: single linear transformation after feature augmentation, plus bias.

Forward pass of the RNN. Args: input (Tensor): Input tensor. Shape depends on batch_first. hx (Tensor, optio

Forward pass of the RNN cell. Args: input (Tensor): Input tensor of shape (batch, input_size) or (input_size,).

Forward pass of the LSTM cell. Args: input (Tensor): Input tensor of shape (batch, input_size) or (input_size,).

Forward pass of the GRU cell. Args: input (Tensor): Input tensor of shape (batch, input_size) or (input_size,).

Return the set of all functions called in `code`.

获取本次实验的全局 token 统计(thinking/content/total)。

r"""Load a model from the specified path with the given filename. Args: path: The location of the trained model. file

r"""Method to count the number of nodes in a given tree structure. Args: x: A symbol tree structure Returns:

r"""Use a trained model to make predictions. Args: X (torch.Tensor): Data that needs to be predicted. Returns: