CloneSet142

Bio/MarkovModel.py

Bio/MarkovModel.py

        k = outputs[t-1] 
        for j in range(N): 
             # The probability of the state is the sum of the
             # transitions from all the states from time t-1.
             lprob = LOG0 
             for i in range(N): 
                  lp = matrix[i][t-1]+lp_transition[i][j]+lp_emission[i][k] 


                  lprob = _logadd(lprob,lp) 
             matrix[j][t] = lprob 
        

        k = outputs[t] 
        for i in range(N): 
             # The probability of the state is the sum of the
             # transitions from all the states from time t+1.
             lprob = LOG0 
             for j in range(N): 
                  lp = matrix[j][t+1]+lp_transition[i][j]+lp_emission[i][k] 


                  lprob = _logadd(lprob,lp) 
             matrix[i][t] = lprob 
        

  k = outputs[ [[#variable72eb4c80]]] 
  for [[#variable72eb4c00]]in range(N):
  
    # The probability of the state is the sum of the
    # transitions from all the states from time t+1.
    # transitions from all the states from time t-1.
    lprob = LOG0 
    for [[#variable72eb4ba0]]in range(N):
    
      lp = matrix[ [[#variable72eb4ba0]]][ [[#variable72eb4b40]]]+lp_transition[i][j]+lp_emission[i][k] 
      lprob = _logadd(lprob,lp) 
    matrix[ [[#variable72eb4c00]]][t] = lprob 
  
 

t 

t-1 

i 

j 

j 

i 

t+1 

t-1