def _prepare_fit_binary(est, y, i):
 y_i = np.ones(y.shape, dtype=np.float64, order='C')
 y_i[(y != est.classes_[i])] = (-1.0)
 average_intercept = 0
 average_coef = None
 if (len(est.classes_) == 2):
  if (not est.average):
   coef = est.coef_.ravel()
   intercept = est.intercept_[0]
  else:
   coef = est.standard_coef_.ravel()
   intercept = est.standard_intercept_[0]
   average_coef = est.average_coef_.ravel()
   average_intercept = est.average_intercept_[0]
 elif (not est.average):
  coef = est.coef_[i]
  intercept = est.intercept_[i]
 else:
  coef = est.standard_coef_[i]
  intercept = est.standard_intercept_[i]
  average_coef = est.average_coef_[i]
  average_intercept = est.average_intercept_[i]
 return (y_i, coef, intercept, average_coef, average_intercept)

def _prepare_fit_binary ( est , y , i ) : y_i = np . ones ( y . shape , dtype = np . float64 , order = 'C' ) y_i [ ( y != est . classes_ [ i ] ) ] = ( - 1.0 ) average_intercept = 0 average_coef = None if ( len ( est . classes_ ) == 2 ) : if ( not est . average ) : coef = est . coef_ . ravel ( ) intercept = est . intercept_ [ 0 ] else : coef = est . standard_coef_ . ravel ( ) intercept = est . standard_intercept_ [ 0 ] average_coef = est . average_coef_ . ravel ( ) average_intercept = est . average_intercept_ [ 0 ] elif ( not est . average ) : coef = est . coef_ [ i ] intercept = est . intercept_ [ i ] else : coef = est . standard_coef_ [ i ] intercept = est . standard_intercept_ [ i ] average_coef = est . average_coef_ [ i ] average_intercept = est . average_intercept_ [ i ] return ( y_i , coef , intercept , average_coef , average_intercept )
initialization for fit_binary .