def __init__(self, smoothing, smooth_param = 0):
LinearClassifier.__init__(self)
self.trained = False
self.likelihood = 0
self.prior = 0
self.smooth = smoothing
self.smooth_param = smooth_param
def __init__(self):
LinearClassifier.__init__(self)
self.trained = False
self.likelihood = 0
self.prior = 0
self.smooth = True
self.smooth_param = 1
def __init__(self):
LinearClassifier.__init__(self)
self.trained = False
self.likelihood = 0
self.prior = 0
self.smooth = False
self.smooth_param = 1
def __init__(self,nr_epochs = 10, initial_step = 1.0, alpha = 1.0,regularizer = 1.0):
LinearClassifier.__init__(self)
self.trained = False
self.nr_epochs = nr_epochs
self.params_per_round = []
self.initial_step = initial_step
self.alpha = alpha
self.regularizer = regularizer
def __init__(self, labels, feature_generator, epochs=10, eta=1.):
LinearClassifier.__init__(self, labels, feature_generator)
self.parameters_for_epoch = []
self.n_epochs = epochs
self.n_features = feature_generator.n_features()
self.eta = eta
def __init__(self,
nr_epochs=10,
initial_step=1.0,
alpha=1.0,
regularizer=1.0):
LinearClassifier.__init__(self)
self.trained = False
self.nr_epochs = nr_epochs
self.params_per_round = []
self.initial_step = initial_step
self.alpha = alpha
self.regularizer = regularizer
