def __init__(self, alpha, threshold, sample, **kwargs):
r"""See ``SVMClassifier`` for full documentation.
"""
Classifier.__init__(self)
num_patterns = len(sample)
check_svm_classification_sample(sample)
self.dim = len(sample[0].pattern)
if len(alpha) != num_patterns:
raise ValueError('The supplied sample and multipliers vector do \
not have the same size')
self.sv_indices = [i for i in range(len(alpha)) if alpha[i] != 0]
self.support_vectors = [sample[i].pattern for i in self.sv_indices]
self.signed_alphas = [alpha[i] * sample[i].label
for i in self.sv_indices]
self.threshold = threshold
try:
self.kernel = kwargs['kernel']
except KeyError:
self.kernel = Kernel.get_default()
def __init__(self, dimensions, connections, **kwargs):
r"""
See ``MultilayerPerceptron`` for full documentation.
"""
Classifier.__init__(self)
self.dimensions = dimensions
self.connections = list(connections)
try:
self.thresholds = kwargs['thresholds']
self.has_thresholds = True
except KeyError:
self.thresholds = None
self.has_thresholds = False
try:
self.activations = kwargs['activations']
except KeyError:
self.activations = HeavisideActivationFunction()
MultilayerPerceptron.check_size(self.dimensions, self.connections,
self.thresholds, self.activations)
self.notify_observers()
def __init__(self, weights, **kwargs):
r"""
See :class:`Perceptron` for full documentation.
"""
Classifier.__init__(self)
self.__weights = self.__activation = None
try:
threshold = kwargs['threshold']
self.has_threshold = True
except KeyError:
self.has_threshold = False
threshold = (0,) * len(weights)
Perceptron.check_weights_and_threshold(weights, threshold)
self.set_weights_and_threshold(weights, threshold)
try:
self.activation = kwargs['activation']
#activation function
except KeyError:
self.activation = HeavisideActivationFunction()
