brainpy.algorithms.offline.PolynomialRegression#

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

Methods

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

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.