def __init__(self,
loss="hinge",
penalty='l2',
alpha=0.0001,
l1_ratio=0.15,
fit_intercept=True,
max_iter=1000,
tol=None,
shuffle=True,
verbose=0,
epsilon=DEFAULT_EPSILON,
n_jobs=1,
random_state=None,
learning_rate="optimal",
eta0=0.0,
power_t=0.5,
class_weight=None,
warm_start=False,
average=False,
n_iter=None):
_skSGDClassifier.__init__(self, loss, penalty, alpha, l1_ratio,
fit_intercept, max_iter, tol, shuffle,
verbose, epsilon, n_jobs, random_state,
learning_rate, eta0, power_t, class_weight,
warm_start, average, n_iter)
BaseWrapperClf.__init__(self)
def __init__(self,
C,
gamma,
learning_rate="optimal",
class_weight=None,
n_components=100,
random_state=None):
self.C = C
self.gamma = gamma
self.learning_rate = learning_rate
self.loss = 'hinge'
self.penalty = 'l2'
NystroemSet.__init__(self,
gamma=gamma,
n_components=n_components,
random_state=random_state)
SGDClassifier.__init__(self,
loss=self.loss,
penalty=self.penalty,
alpha=1 / C,
warm_start=True,
class_weight=class_weight)
def __init__(self, k='all'):
if k != None:
MutableSGDClassifier.K_features = k
SGDClassifier.__init__(self,alpha=.0001, n_iter=50,penalty="elasticnet")
