def __init__(self, sample, **kwargs):
r"""
See ``RosenblattPerceptronAlgorithm`` for full definition.
"""
PerceptronAlgorithm.__init__(self, sample, **kwargs)
IterativeAlgorithm.__init__(self, sample)
PerceptronAlgorithm.reset(self, **kwargs)
def __init__(self, sample, **kwargs):
r"""
See ``GradientPerceptronAlgorithm`` for full documentation.
"""
PerceptronAlgorithm.__init__(self, sample, **kwargs)
IterativeAlgorithm.__init__(self, sample)
# This calms down pylint
self.beta = None
self.reset(**kwargs)
def __init__(self, sample, n_clusters = 2, m = 2, **kwargs ):
IterativeAlgorithm.__init__(self, sample)
try:
self.initializer = kwargs['initializer']
except KeyError:
self.initializer = False
self.n_clusters = n_clusters
self.m = m
self.distance = 1.0
#self.reset(**kwargs)
self.memberships_old = None
self.reset(**kwargs)
def __init__(self, sample, dimensions=None, **kwargs):
r"""
See ``BackpropagationAlgorithm`` for full documentation.
"""
IterativeAlgorithm.__init__(self, sample)
# dimensions needs to be a named argument in order to be able to
# cross-validate on it. The default value will correspond to three
# layers, with the input and output one automatically sized in order
# to fit the provided data set, and the hidden one containing half of
# the biggest value between number of input and output units.
if dimensions is not None:
self.dimensions = dimensions
else:
n_in = len(sample[0].pattern)
n_out = len(sample[0].label)
self.dimensions = (n_in, int(max(n_in, n_out) / 2), n_out)
num_input = self.dimensions[0]
for example in sample:
if len(example.pattern) != num_input:
raise ValueError('Sample incompatible with number of units')
try:
self.activations = kwargs['activations']
if len(self.activations) != len(dimensions):
raise ValueError(\
'Activations incompatible with number of layers')
except KeyError:
self.activations = SigmoidActivationFunction()
except TypeError:
pass
# Raised by len if the argument is assigned a single activation
# this calms down pylint
self.threshold = None
self.reset(**kwargs)
