@@ -92,7 +92,7 @@ def _validate_params(self, set_max_iter=True):
9292 raise ValueError ("max_iter must be > zero. Got %f" % self .max_iter )
9393 if not (0.0 <= self .l1_ratio <= 1.0 ):
9494 raise ValueError ("l1_ratio must be in [0, 1]" )
95- if self .alpha < 0.0 :
95+ if not isinstance ( self , SGDOneClassSVM ) and self .alpha < 0.0 :
9696 raise ValueError ("alpha must be >= 0" )
9797 if self .learning_rate in ("constant" , "invscaling" ):
9898 if self .eta0 <= 0.0 :
@@ -1611,10 +1611,10 @@ def partial_fit(self, X, y=None, sample_weight=None):
16111611 self : returns an instance of self.
16121612 """
16131613
1614- self . alpha = self .nu / 2
1614+ alpha = self .nu / 2
16151615 self ._validate_params ()
16161616
1617- return self ._partial_fit (X , self . alpha , C = 1.0 , loss = self .loss ,
1617+ return self ._partial_fit (X , alpha , C = 1.0 , loss = self .loss ,
16181618 learning_rate = self .learning_rate ,
16191619 max_iter = 1 ,
16201620 sample_weight = sample_weight ,
@@ -1688,8 +1688,8 @@ def fit(self, X, y=None, coef_init=None, offset_init=None,
16881688 self : returns an instance of self.
16891689 """
16901690
1691- self . alpha = self .nu / 2
1692- self ._fit (X , alpha = self . alpha , C = 1.0 ,
1691+ alpha = self .nu / 2
1692+ self ._fit (X , alpha = alpha , C = 1.0 ,
16931693 loss = self .loss , learning_rate = self .learning_rate ,
16941694 coef_init = coef_init , offset_init = offset_init ,
16951695 sample_weight = sample_weight )
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