CloneSet108


Clone Mass	Clones in CloneSet	Parameter Count	Clone Similarity	Syntax Category [Sequence Length]
19	2	2	0.951	stmt_list[4]

Clone Abstraction

Parameter Bindings

Clone Instance (Click to see clone)	Line Count	Source Line	Source File
1	19	136	Bio/MarkovModel.py
2	19	375	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 Abstraction

Parameter Count: 2

Parameter 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
1	1	`[[#2f59ab00]]`	training_outputs
1	2	`[[#2f59ab00]]`	training_states,training_outputs
2	1	`[[#1a3dffc0]]`	[ ]
2	2	`[[#1a3dffc0]]`	[ ],[ ]