Module Assign NameStore Str Assign NameStore Str ImportFrom alias alias ClassDef bases NameLoad body Expr Str Assign NameStore NameLoad Assign NameStore NameLoad FunctionDef arguments args NameParam NameParam defaults body Assign AttributeStore NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign AttributeStore NameLoad attr NameLoad If NameLoad body Assert CompareEq AttributeLoad NameLoad attr AttributeLoad AttributeLoad NameLoad attr attr Assert CompareEq AttributeLoad NameLoad attr AttributeLoad AttributeLoad NameLoad attr attr decorator_list FunctionDef arguments args NameParam NameParam defaults vararg kwarg body Expr Str Expr Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults Num vararg kwarg body Expr Str For NameStore Call NameLoad NameLoad body Expr Call AttributeLoad NameLoad attr NameLoad NameLoad decorator_list FunctionDef arguments args NameParam defaults body Expr Str Expr Call NameLoad decorator_list decorator_list
N/A N/A __author__ Tom Schaul, tom@idsia.ch N/A __version__ $Id$ pybrain.utilities Named abstractMethod Trainer N/A Named N/A N/A  A trainer determines how to change the adaptive parameters of a module.
    It requires access to a DataSet object (which provides input-target tuples).  N/A ds None N/A module None __init__ N/A N/A self module N/A N/A N/A N/A self module module N/A setData N/A N/A self dataset N/A N/A N/A Associate the given dataset with the trainer. N/A N/A self ds dataset N/A dataset N/A N/A N/A N/A dataset indim N/A N/A self module indim N/A N/A N/A dataset outdim N/A N/A self module outdim N/A trainOnDataset N/A N/A self dataset N/A args kwargs N/A N/A Set the dataset and train.

        Additional arguments are passed on to the train method. N/A N/A N/A self setData dataset N/A N/A N/A self trainEpochs args kwargs N/A trainEpochs N/A N/A self epochs N/A 1 args kwargs N/A N/A Train on the current dataset for the given number of `epochs`.

        Additional arguments are passed on to the train method. N/A dummy N/A range epochs N/A N/A N/A N/A self train args kwargs N/A train N/A N/A self N/A N/A N/A Train on the current dataset, for a single epoch. N/A N/A abstractMethod N/A N/A