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Clone Mass | Clones in CloneSet | Parameter Count | Clone Similarity | Syntax Category [Sequence Length] |
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19 | 2 | 2 | 0.951 | stmt_list[4] |
Clone Abstraction | Parameter Bindings |
Clone Instance (Click to see clone) | Line Count | Source Line | Source File |
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1 | 19 | 136 | Bio/MarkovModel.py |
2 | 19 | 375 | Bio/MarkovModel.py |
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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 = [ ] |
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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 = [ ],[ ] |
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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 Index | Clone Instance | Parameter Name | Value |
---|---|---|---|
1 | 1 | [[#2f59ab00]] | training_outputs |
1 | 2 | [[#2f59ab00]] | training_states,training_outputs |
2 | 1 | [[#1a3dffc0]] | [ ] |
2 | 2 | [[#1a3dffc0]] | [ ],[ ] |