def run(self, **kwargs):
r"""
Run the learning algorithm.
INPUT:
- ``self`` -- object on which the function is invoked.
- ``stopping_criterion`` -- ``StoppingCriterion`` object (default:
``FixedIterationsStoppingCriterion()``) to be used in order to decide
when learning should be stopped.
- ``selector`` -- iterator (default: ``sequential_selector``) yielding
the examples to be fed to the learning algorithm.
OUTPUT:
No output.
EXAMPLES:
See examples for concrete subclasses such as
``GradientPerceptronLearningAlgorithm`` in package
yaplf.algorithms.neural.
AUTHORS:
- Dario Malchiodi (2010-02-22)
"""
try:
self.stop_criterion = kwargs['stopping_criterion']
self.stop_criterion.reset()
except KeyError:
self.stop_criterion = FixedIterationsStoppingCriterion()
self.stop_criterion.register_learning_algorithm(self)
try:
# unit selection
selector = kwargs['selector']
except KeyError:
selector = sequential_selector
self.sample_selector = selector(self.sample)
class IterativeAlgorithm(LearningAlgorithm, Observable):
r"""
Base class for iterative learning algorithms. Besides the feature
inherited from ``LearningAlgorithm``, iterative learning algorithms are
observable (typically in the aim of generating graphics summarizing their
behaviour) and use an example selector and a stopping criterion.
INPUT:
- ``self`` -- object on which the function is invoked.
- ``sample`` -- list or tuple of ``Example`` object containing the train
set in input to the learning algorithm.
OUTPUT:
An ``IterativeLearningAlgorithm`` object.
EXAMPLES:
See examples for concrete subclasses such as
``GradientPerceptronLearningAlgorithm`` in package yaplf.algorithms.neural.
AUTHORS:
- Dario Malchiodi (2010-02-22)
"""
def __init__(self, sample):
r"""
See ``IterativeLearningAlgorithm`` for full documentation.
"""
LearningAlgorithm.__init__(self, sample)
Observable.__init__(self)
self.sample = sample
#This calms down pylint. The actual values will be set in run
self.stop_criterion = None
self.sample_selector = None
def run(self, **kwargs):
r"""
Run the learning algorithm.
INPUT:
- ``self`` -- object on which the function is invoked.
- ``stopping_criterion`` -- ``StoppingCriterion`` object (default:
``FixedIterationsStoppingCriterion()``) to be used in order to decide
when learning should be stopped.
- ``selector`` -- iterator (default: ``sequential_selector``) yielding
the examples to be fed to the learning algorithm.
OUTPUT:
No output.
EXAMPLES:
See examples for concrete subclasses such as
``GradientPerceptronLearningAlgorithm`` in package
yaplf.algorithms.neural.
AUTHORS:
- Dario Malchiodi (2010-02-22)
"""
try:
self.stop_criterion = kwargs['stopping_criterion']
self.stop_criterion.reset()
except KeyError:
self.stop_criterion = FixedIterationsStoppingCriterion()
self.stop_criterion.register_learning_algorithm(self)
try:
# unit selection
selector = kwargs['selector']
except KeyError:
selector = sequential_selector
self.sample_selector = selector(self.sample)
def get_current_iteration_value(self):
pass
def get_previous_iteration_value(self):
pass
