#NEWLINE# def __init__( self , loss , penalty = 'l2' , alpha = 0.0001 , C = 1.0 , l1_ratio = 0.15 , fit_intercept = True , n_iter = 5 , shuffle = True , verbose = 0 , epsilon = 0.1 , random_state = None , learning_rate = 'optimal' , eta0 = 0.0 , power_t = 0.5 , warm_start = False , average = False ) : #NEWLINE# #INDENT# self . loss = loss #NEWLINE# self . penalty = penalty #NEWLINE# self . learning_rate = learning_rate #NEWLINE# self . epsilon = epsilon #NEWLINE# self . alpha = alpha #NEWLINE# self . C = C #NEWLINE# self . l1_ratio = l1_ratio #NEWLINE# self . fit_intercept = fit_intercept #NEWLINE# self . n_iter = n_iter #NEWLINE# self . shuffle = shuffle #NEWLINE# self . random_state = random_state #NEWLINE# self . verbose = verbose #NEWLINE# self . eta0 = eta0 #NEWLINE# self . power_t = power_t #NEWLINE# self . warm_start = warm_start #NEWLINE# self . average = average #NEWLINE# self . _validate_params ( ) #NEWLINE# self . coef_ = None #NEWLINE# if ( self . average > 0 ) : #NEWLINE# #INDENT# self . standard_coef_ = None #NEWLINE# self . average_coef_ = None #NEWLINE# #UNINDENT# self . t_ = None