Mojo struct

LinearRegression

@memory_only
struct LinearRegression

A Gradient Descent based linear regression with mse as the loss function.

Aliases

• __del__is_trivial = False

Fields

• lr (Float32): Learning rate.
• n_iters (Int): The maximum number of iterations.
• reg_alpha (Float32): Constant that multiplies the regularization term.
• l1_ratio (Float32): The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
• tol (Float32): The stopping criterion based on loss.
• batch_size (Int): Batch size, with batch_size=1 corresponds to SGD, 1 < batch_size < n_samples corresponds to Mini-Batch Gradient Descent.
• random_state (Int): Used for shuffling the data.
• weights (Matrix): Weights per feature.
• bias (Float32): Bias term.

Implemented traits

AnyType, CV, ImplicitlyDestructible

Methods

__init__

fn __init__(out self, learning_rate: Float32 = 0.001, n_iters: Int = 1000, reg_alpha: Float32 = 0, l1_ratio: Float32 = -1, tol: Float32 = 0, batch_size: Int = 0, random_state: Int = -1)

Args:

• learning_rate (Float32)
• n_iters (Int)
• reg_alpha (Float32)
• l1_ratio (Float32)
• tol (Float32)
• batch_size (Int)
• random_state (Int)
• self (Self)

Returns:

Self

fn __init__(out self, params: Dict[String, String])

Args:

• params (Dict)
• self (Self)

Returns:

Self

Raises:

fit

fn fit(mut self, X: Matrix, y: Matrix)

Fit the model.

Args:

• self (Self)
• X (Matrix)
• y (Matrix)

Raises:

predict

fn predict(self, X: Matrix) -> Matrix

Predict regression values for X.

Args:

• self (Self)
• X (Matrix)

Returns:

Matrix: The predicted values.

Raises: