CloneSet108


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CloneSet
Parameter
Count
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Similarity
Syntax Category
[Sequence Length]
19220.951stmt_list[4]
Clone AbstractionParameter Bindings
Clone Instance
(Click to see clone)
Line CountSource Line
Source File
119136
Bio/MarkovModel.py
219375
Bio/MarkovModel.py
Clone Instance
1
Line Count
19
Source Line
136
Source File
Bio/MarkovModel.py

    if pseudo_initial!=None: 
         pseudo_initial = asarray(pseudo_initial) 
         if pseudo_initial.shape!=(N, ): 
             raise ValueError("pseudo_initial not shape len(states)") 
         
    if pseudo_transition!=None: 
         pseudo_transition = asarray(pseudo_transition) 
         if pseudo_transition.shape!=(N,N):  
             raise ValueError("pseudo_transition not shape "+"len(states) X len(states)") 
         
    if pseudo_emission!=None: 
         pseudo_emission = asarray(pseudo_emission) 
         if pseudo_emission.shape!=(N,M):  
             raise ValueError("pseudo_emission not shape "+"len(states) X len(alphabet)") 


     # Training data is given as a list of members of the alphabet.
     # Replace those with indexes into the alphabet list for easier
     # computation.
         
    training_outputs = [ ] 


Clone Instance
2
Line Count
19
Source Line
375
Source File
Bio/MarkovModel.py

    if pseudo_initial!=None: 
         pseudo_initial = asarray(pseudo_initial) 
         if pseudo_initial.shape!=(N, ): 
             raise ValueError("pseudo_initial not shape len(states)") 
         
    if pseudo_transition!=None: 
         pseudo_transition = asarray(pseudo_transition) 
         if pseudo_transition.shape!=(N,N):  
             raise ValueError("pseudo_transition not shape "+"len(states) X len(states)") 
         
    if pseudo_emission!=None: 
         pseudo_emission = asarray(pseudo_emission) 
         if pseudo_emission.shape!=(N,M):  
             raise ValueError("pseudo_emission not shape "+"len(states) X len(alphabet)") 


     # Training data is given as a list of members of the alphabet.
     # Replace those with indexes into the alphabet list for easier
     # computation.
         
    training_states,training_outputs =  [ ],[ ] 


Clone AbstractionParameter Count: 2Parameter Bindings

if pseudo_initial!=None:
  pseudo_initial = asarray(pseudo_initial) 
  if pseudo_initial.shape!=(N, ):
  
    raise ValueError("pseudo_initial not shape len(states)") 
  
if pseudo_transition!=None:
  pseudo_transition = asarray(pseudo_transition) 
  if pseudo_transition.shape!=(N,N):
  
    raise ValueError("pseudo_transition not shape "+"len(states) X len(states)") 
  
if pseudo_emission!=None:
  pseudo_emission = asarray(pseudo_emission) 
  if pseudo_emission.shape!=(N,M):
  
    raise ValueError("pseudo_emission not shape "+"len(states) X len(alphabet)") 
    # Training data is given as a list of members of the alphabet.
    # Replace those with indexes into the alphabet list for easier
    # computation.
  
 [[#variable2f59ab00]]= [[#variable1a3dffc0]]
 

CloneAbstraction
Parameter Bindings
Parameter
Index
Clone
Instance
Parameter
Name
Value
11[[#2f59ab00]]
training_outputs 
12[[#2f59ab00]]
training_states,training_outputs 
21[[#1a3dffc0]]
[ ] 
22[[#1a3dffc0]]
[ ],[ ]