<s> import numpy as np <EOL> from sklearn . base import BaseEstimator , ClassifierMixin <EOL> from sklearn . metrics import accuracy_score <EOL> from sklearn . externals import six <EOL> from . _decode import DECODERS <EOL> from . _utils import atleast2d_or_csr , safe_sparse_dot , validate_lengths <EOL> class BaseSequenceClassifier ( BaseEstimator , ClassifierMixin ) : <EOL> def _get_decoder ( self ) : <EOL> try : <EOL> return DECODERS [ self . decode ] <EOL> except KeyError : <EOL> raise ValueError ( "<STR_LIT>" . format ( self . decode ) ) <EOL> def predict ( self , X , lengths = None ) : <EOL> """<STR_LIT>""" <EOL> X = atleast2d_or_csr ( X ) <EOL> scores = safe_sparse_dot ( X , self . coef_ . T ) <EOL> if hasattr ( self , "<STR_LIT>" ) : <EOL> n_classes = len ( self . classes_ ) <EOL> coef_t = self . coef_trans_ . T . reshape ( - <NUM_LIT:1> , self . coef_trans_ . shape [ - <NUM_LIT:1> ] ) <EOL> trans_scores = safe_sparse_dot ( X , coef_t . T ) <EOL> trans_scores = trans_scores . reshape ( - <NUM_LIT:1> , n_classes , n_classes ) <EOL> else : <EOL> trans_scores = None <EOL> decode = self . _get_decoder ( ) <EOL> if lengths is None : <EOL> y = decode ( scores , trans_scores , self . intercept_trans_ , <EOL> self . intercept_init_ , self . intercept_final_ ) <EOL> else : <EOL> start , end = validate_lengths ( X . shape [ <NUM_LIT:0> ] , lengths ) <EOL> y = [ decode ( scores [ start [ i ] : end [ i ] ] , trans_scores , <EOL> self . intercept_trans_ , self . intercept_init_ , <EOL> self . intercept_final_ ) <EOL> for i in six . moves . xrange ( len ( lengths ) ) ] <EOL> y = np . hstack ( y ) <EOL> return self . classes_ [ y ] <EOL> def score ( self , X , y , lengths = None ) : <EOL> """<STR_LIT>""" <EOL> return accuracy_score ( y , self . predict ( X , lengths ) ) </s>
