LinearRegression

class mlpractice.gradient_descent.LinearRegression(descent: mlpractice.gradient_descent.gradient_descent.BaseDescent, tolerance: float = 0.001, max_iter: int = 1000)

Linear regression class.

Parameters
descentBaseDescent

Descent class.

tolerancefloat

Stopping criterion for square of euclidean norm of weight difference.

max_iterint

Stopping criterion for iterations.

Attributes
descentBaseDescent

Descent class.

tolerancefloat

Stopping criterion for square of euclidean norm of weight difference.

max_iterint

Stopping criterion for iterations.

loss_historylist of float

Progress history.

Methods

calc_loss(X, Y)

Getting objects, calculating loss.

fit(X, Y)

Getting objects, fitting descent weights.

predict(X)

Getting objects, predicting targets.

calc_loss(X: numpy.ndarray, Y: numpy.ndarray) None

Getting objects, calculating loss.

Parameters
Xnp.ndarray

Features.

Ynp.ndarray

Targets.

fit(X: numpy.ndarray, Y: numpy.ndarray) mlpractice.gradient_descent.gradient_descent.LinearRegression

Getting objects, fitting descent weights.

Parameters
Xnp.ndarray

Features.

Ynp.ndarray

Targets.

Returns
selfLinearRegression

Current regression object.

predict(X: numpy.ndarray) numpy.ndarray

Getting objects, predicting targets.

Parameters
Xnp.ndarray

Features.

Returns
Ynp.ndarray

Predicted targets.