brainpy.algorithms.offline.LassoRegression#

class brainpy.algorithms.offline.LassoRegression(alpha=1.0, degree=2, add_bias=False, name=None, max_iter=1000, learning_rate=0.001, gradient_descent=True)[source]#

Lasso regression method for offline training.

Parameters
  • alpha (float) – Constant that multiplies the L1 term. Defaults to 1.0. alpha = 0 is equivalent to an ordinary least square.

  • max_iter (int) – The maximum number of iterations.

  • degree (int) – The degree of the polynomial that the independent variable X will be transformed to.

  • name (str) – The name of the algorithm.

__init__(alpha=1.0, degree=2, add_bias=False, name=None, max_iter=1000, learning_rate=0.001, gradient_descent=True)[source]#

Methods

__init__([alpha, degree, add_bias, name, ...])

call(identifier, targets, inputs[, outputs])

The training procedure.

gradient_descent_solve(targets, inputs[, ...])

init_weights(n_features, n_out)

Initialize weights randomly [-1/N, 1/N]

initialize(identifier, *args, **kwargs)

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

predict(W, X)

register_implicit_nodes(*nodes, **named_nodes)

register_implicit_vars(*variables, ...)

save_states(filename[, variables])

Save the model states.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

unique_name([name, type_])

Get the unique name for this object.

vars([method, level, include_self])

Collect all variables in this node and the children nodes.

Attributes

name

Name of the model.