linear_regression

LinearRegression(*, output_size, learning_rate=0.001, loss=None, a=None, b=None)

Bases: SupervisedIncrementalLearner

Linear regression learner.

Initialize the learner.

Parameters:

Name	Type	Description	Default
output_size	int	The size of the output.	required
learning_rate	float	The learning rate.	0.001
loss	Module | None	The loss function.	None
a	Tensor | None	Initial weights. If None (the default), random weights are used.	None
b	Tensor | None	Initial bias. If None (the default), random bias is used.	None

Source code in src/flowcean/torch/linear_regression.py

def __init__(
    self,
    *,
    output_size: int,
    learning_rate: float = 1e-3,
    loss: nn.Module | None = None,
    a: Tensor | None = None,
    b: Tensor | None = None,
) -> None:
    """Initialize the learner.

    Args:
        output_size: The size of the output.
        learning_rate: The learning rate.
        loss: The loss function.
        a: Initial weights. If None (the default), random weights are used.
        b: Initial bias. If None (the default), random bias is used.
    """
    self.model = nn.LazyLinear(output_size)
    if a is not None:
        self.model.weight.data = a
    if b is not None:
        self.model.bias.data = b
    self.loss = loss or nn.MSELoss()
    self.optimizer = SGD(
        self.model.parameters(),
        lr=learning_rate,
    )