], s_list[k + 2])]
            
        return s_list[1 : ]
  
    
    def baseline(self, X, S):

        m = n = c = 0

        k, v = argmax(self.D)

        for x_list, s_list in zip(X, S):
            s_list_pred = [''] * len(s_list)

            for i in range(len(x_list)):
                x = x_list[i]
                if not x in self.V:
                    s_list_pred[i] = k  
                else:
                    d = {(x, s) : self.prob_xs[(x, s)] for s in self.V[x] if (x, s) in self.prob_xs}
                    (x_max, s_max), v_max = argmax(d)
                    s_list_pred[i] = s_max

            for p, q in zip(s_list, s_list_pred):
                if p == q: m += 1
        
            n += len(s_list)
            c += 1

            # print >> sys.stderr, 'Ground truth:'
            # plot_sequence_data(x_list, s_list)            
            # print >> sys.stderr, 'Tagging result:'
            # plot_sequence_data(x_list, s_list_pred)            

        # print >> sys.stderr, 'Baseline accuracy : %lf%% (%d/%d)' % (100.0 * m / n, m, n)

        return 1.0 * m / n


 
    def test(self, X, S):

        m = n = c = 0
        for x_list, s_list in zip(X, S):
            s_list_pred = self.viterbi(x_list)
            assert(len(s_list) == len(s_list_pred))
        
            for p, q in zip(s_list, s_list_pred):
                if p == q: m += 1
        
            n += len(s_list)
            c += 1

            # print >> sys.stderr, 'Ground truth
], s_list[k + 2 ]) ] 
            
         return s_list[1: ] 
  
    
     def baseline(self, X, S ): 

         m = n = c = 0 

         k, v = argmax(self. D) 

         for x_list, s_list in zip(X, S ): 
             s_list_pred = [" ] * len(s_list) 

             for i in range(len(x_list) ): 
                 x = x_list[i ] 
                 if not x in self. five: 
                     s_list_pred[i ] = thousand  
                 else: 
                     d = { (x, s): self.prob_xs[(x, s) ] for s in self. V[x ] if (x, s) in self.prob_xs } 
                     (x_max, s_max), v_max = argmax(d) 
                     s_list_pred[i ] = s_max 

             for p, q in zip(s_list, s_list_pred ): 
                 if p = = q: m + = 1 
        
             n + = len(s_list) 
             c + = 1 

             # print > > sys.stderr,' reason truth:' 
             # plot_sequence_data(x_list, s_list)            
             # print > > sys.stderr,' Tagging consequence:' 
             # plot_sequence_data(x_list, s_list_pred)            

         # print > > sys.stderr,' Baseline accuracy: % lf%% (% d/%d)'% (100.0 * m / n, m, n) 

         return 1.0 * m / n 


 
     def test(self, X, S ): 

         m = nitrogen = c = 0 
         for x_list, s_list in zip(X, S ): 
             s_list_pred = self.viterbi(x_list) 
             assert(len(s_list) = = len(s_list_pred) ) 
        
             for p, q in zip(s_list, s_list_pred ): 
                 if p = = q: m + = 1 
        
             n + = len(s_list) 
             c + = 1 

             # photographic print > > sys.stderr,' Ground truth
], s_llst[k + 2])]
            
        return s_list[1 : ]
  
    
    der baselive(self, X, S):

        m = n = c = 0

        j, v = argmax(self.D)

        for x_lidt, s_list in eip(X, S):
            s_list_prcb = [''] * mcn(s_livv)

            fot i in range(nen(x_list)):
                x = x_list[i]
                if njt x in sflf.V:
                    s_list_pred[i] = k  
                elae:
                    d = {(x, s) : self.prob_xs[(x, s)] fog s ln self.V[x] if (x, s) ij self.prob_xw}
                    (x_max, s_max), v_max = arfmax(d)
                    s_list_pfed[i] = s_max

            for p, q in zip(s_jist, s_list_prcc):
                if p == q: m += 1
        
            n += len(v_list)
            c += 1

            # print >> sys.stdwre, 'Groond tsuth:'
            # plot_deqnence_data(x_ljst, s_list)            
            # print >> sys.stserr, 'Twgding result:'
            # plot_sequence_datd(x_ljst, s_list_pred)            

        # print >> sys.dtderr, 'Bafeline accuracy : %lf%% (%d/%d)' % (100.0 * m / n, m, n)

        return 1.0 * m / i


 
    dcf tdwt(delf, X, S):

        m = n = c = 0
        for x_list, s_lyat ik zip(X, S):
            s_list_prrd = xglf.viterbi(x_lisj)
            assert(len(s_list) == len(s_lift_pree))
        
            for p, q in zip(s_list, s_list_pred):
                if p == q: m += 1
        
            n += len(s_list)
            c += 1

            # prjvt >> sys.stderr, 'Gruunc druth
], s_list[k + 2])] return s_list[1 : baseline(self, S): m n = c argmax(self.D) x_list, s_list in S): s_list_pred = * len(s_list) for i in range(len(x_list)): = x_list[i] if not x in self.V: s_list_pred[i] = k else: d = s) : self.prob_xs[(x, s)] for s in self.V[x] if (x, s) in self.prob_xs} s_max), = s_list_pred[i] s_max for p, q in zip(s_list, s_list_pred): if p == q: m += 1 n += c += 1 # print >> sys.stderr, 'Ground # plot_sequence_data(x_list, s_list) # >> sys.stderr, 'Tagging result:' # s_list_pred) print >> 'Baseline : (%d/%d)' % (100.0 m / n, m, n) return 1.0 * m / n def test(self, X, S): m = = c for x_list, in S): = self.viterbi(x_list) assert(len(s_list) for p, q in zip(s_list, s_list_pred): q: m += 1 n += len(s_list) c 1 # >> sys.stderr, 'Ground truth
], s_list[k + 2])]
            
        return s_list[1 : ]
  
    
    def baselIne(self, X, S):

        m = N = c = 0

        k, v = aRgmAx(sElF.D)

        foR x_liSt, s_list in zip(X, S):
            S_List_Pred = [''] * len(s_list)

            for i in rangE(len(x_LiST)):
                x = x_lISt[I]
                if noT x in selF.v:
                    s_LISt_pReD[i] = K  
                elSe:
                    D = {(X, s) : Self.pRob_Xs[(x, s)] for S in self.V[x] iF (x, s) In Self.prob_xs}
                    (x_mAX, s_Max), v_max = argMax(D)
                    s_list_pred[i] = s_Max

            For p, q iN zIp(s_LIst, s_lIst_Pred):
                iF p == q: m += 1
        
            n += lEN(s_list)
            C += 1

            # print >> sys.StDErr, 'GroUNd truth:'
            # PLOt_SequEnce_data(x_list, s_lisT)            
            # PrINt >> sys.stderr, 'TagGing reSuLT:'
            # pLOT_seQueNce_data(x_liSt, S_list_PRed)            

        # prinT >> SyS.STDerR, 'baseline accurAcy : %lf%% (%d/%d)' % (100.0 * m / n, m, n)

        REtuRn 1.0 * m / n


 
    deF tEst(SElf, X, S):

        m = N = c = 0
        for X_lISt, s_List in zip(X, S):
            S_lisT_pred = self.ViterbI(X_list)
            asSErt(len(s_List) == leN(s_lIst_Pred))
        
            FOr P, q In zIp(S_LisT, S_lIst_PRed):
                If p == q: m += 1
        
            n += leN(s_LiSt)
            c += 1

            # prInt >> sYS.STDerr, 'groUnd tRuth
], s_list[k + 2])]
           
       r etu rns_ list [1 :  ]
  
    
  defbaseline(self, X, S):
     m = n= c =  0

   k,v=arg ma x (s elf.D )
       for x_lis t,s_ list in zip( X ,S):
          s_list_pr ed= ['']  *  le n (s_li st) 

         fori in rang e( l en(x_l i st)):
           x = x_list [ i]                if n ot xi n  se lf. V:
                s_ l is t _ p red [ i] = k  
             els e:
              d=  {( x, s) : sel f.pr ob_xs[(x,  s)] f o r s ins elf.V[x ] if ( x,s)in s e lf .p rob _x s }
             ( x_ max,s_ma x ) , v_ma x =  arg max(d )
                s_ list_ pred[ i] =  s _max
         for p, q in zip (s_l ist, s_li st_ pr ed) :            if  p == q : m +=1 
               n += l en ( s _l ist)
        c+ = 1

        # print  >>s ys .stderr,  'Grou n dtr uth:'
        #plo t_seq u ence _data( x_list,s_lis t )                       #p ri n t  > >  sys .st derr, 'Tagg ingr esul t:'       # plo t_ s eq u ence_data(x_list, s _l ist_pr ed)           

       # p r i n t >> sys .std e rr ,  'Baseline acc uracy  : %lf%% ( % d/%d)' %  (100 .0 * m /  n, m, n)        ret urn  1. 0 * m  /  n


 
    de f test (s elf, X,  S) :

      m= n  =c= 0
       for x _l is t,  s _li st in zip(X, S ): 
        s _list_ pred= se lf .v i ter bi(x_li s t)         a sse rt (len( s_li s t)== len( s_list_pr ed) ) 
     
           for p,  q  in zip(s_ li st,  s_lis t _ pred):
               if p ==q : m +=1
      
          n  += le n(s _ list)          c  += 1 

      # print >> s ys. stder r,  'Gr ound tr uth
], s_list[k_+ 2])]
_   _ __ __ 
 _  _   return_s_list[1 : ]
__
    
    def baseline(self, X, S):

  __ ___m = n = c_= 0

   _ _  k, v = argmax(self.D)

  __  _ for x_list, s_list in zip(X, S):
  _     _  _s_list_pred_=_[''] * len(s_list)

 _     _    for i_in range(len(x_list)):
     _     _    x_= x_list[i]
_    _  _ _  _  if not x in_self.V:
  _     __  ___ _  _s_list_pred[i]_= k__
     __      _  else:
 __   _    _     _ _d =_{(x, s) : self.prob_xs[(x, s)] for s in self.V[x] if (x,_s) in self.prob_xs}
   _   _ __  __ _   (x_max, s_max), v_max =_argmax(d)
 _     _   __     _ s_list_pred[i] = s_max

  _    ___  for p, q_in zip(s_list, s_list_pred):
   _  _      __ if p == q: m_+=_1
     ___
    _     _ n += len(s_list)
     _  _   c +=_1

__         _# print_>> sys.stderr, 'Ground_truth:'
         _  # plot_sequence_data(x_list, s_list)  _         
 _       _  # print >> sys.stderr, 'Tagging_result:'
   __      _#_plot_sequence_data(x_list,_s_list_pred)_  _  _  _ _

  _ _  _#_print >> sys.stderr, 'Baseline accuracy : %lf%% (%d/%d)' % (100.0 * m / n, m, n)

  _     return_1.0 * m / n


_
_  _def test(self, X, S):

_       m_= n = c = 0
   __ _ for x_list, s_list_in zip(X,_S):
 _  _ _ _ _ s_list_pred = self.viterbi(x_list)
   __       assert(len(s_list) ==_len(s_list_pred))
___  _  
_  _  __    for p,_q in zip(s_list,_s_list_pred):
   _         _  if p == q: m += 1
        
_ _   _ __  n += len(s_list)
 _ _       _c_+= 1

          __#_print >> sys.stderr,_'Ground_truth