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from sklearn.linear_model import SGDClassifier
from Orange.base import SklLearner
from Orange.preprocess import Normalize
from Orange.regression.linear import LinearModel
__all__ = ["SGDClassificationLearner"]
class SGDClassificationLearner(SklLearner):
name = 'sgd'
__wraps__ = SGDClassifier
__returns__ = LinearModel
preprocessors = SklLearner.preprocessors + [Normalize()]
supports_weights = True
def __init__(self, loss='hinge', penalty='l2', alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=5,
tol=1e-3, shuffle=True, epsilon=0.1, random_state=None,
learning_rate='invscaling', eta0=0.01, power_t=0.25,
warm_start=False, average=False, preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
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