@@ -314,11 +314,11 @@ def _solve(self):
314314 covarF_inv = np .linalg .inv (covarF )
315315 j = 0
316316 for i in f :
317- l , bi = self ._compute_lambda (
317+ lam , bi = self ._compute_lambda (
318318 covarF_inv , covarFB , meanF , wB , j , [self .lB [i ], self .uB [i ]]
319319 )
320- if CLA ._infnone (l ) > CLA ._infnone (l_in ):
321- l_in , i_in , bi_in = l , i , bi
320+ if CLA ._infnone (lam ) > CLA ._infnone (l_in ):
321+ l_in , i_in , bi_in = lam , i , bi
322322 j += 1
323323 # 2) case b): Free one bounded weight
324324 l_out = None
@@ -327,18 +327,18 @@ def _solve(self):
327327 for i in b :
328328 covarF , covarFB , meanF , wB = self ._get_matrices (f + [i ])
329329 covarF_inv = np .linalg .inv (covarF )
330- l , bi = self ._compute_lambda (
330+ lam , bi = self ._compute_lambda (
331331 covarF_inv ,
332332 covarFB ,
333333 meanF ,
334334 wB ,
335335 meanF .shape [0 ] - 1 ,
336336 self .w [- 1 ][i ],
337337 )
338- if (self . ls [ - 1 ] is None or l < self . ls [ - 1 ]) and l > CLA . _infnone (
339- l_out
340- ):
341- l_out , i_out = l , i
338+ if (
339+ self . ls [ - 1 ] is None or lam < self . ls [ - 1 ]
340+ ) and lam > CLA . _infnone ( l_out ) :
341+ l_out , i_out = lam , i
342342 if (l_in is None or l_in < 0 ) and (l_out is None or l_out < 0 ):
343343 # 3) compute minimum variance solution
344344 self .ls .append (0 )
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