CloneSet99


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Clone
Mass
Clones in
CloneSet
Parameter
Count
Clone
Similarity
Syntax Category
[Sequence Length]
20210.998stmt_list[6]
Clone AbstractionParameter Bindings
Clone Instance
(Click to see clone)
Line CountSource Line
Source File
12174
Bio/NeuralNetwork/BackPropagation/Layer.py
220164
Bio/NeuralNetwork/BackPropagation/Layer.py
Clone Instance
1
Line Count
21
Source Line
74
Source File
Bio/NeuralNetwork/BackPropagation/Layer.py

        # set up the weights
        self.weights = { } 
        for own_node in self.nodes: 
             for other_node in self._next_layer.nodes: 
                  self.weights[(own_node,other_node)] =
                                          random.randrange( -2.0,2.0) 

          # set up the weight changes
             
        self.weight_changes = { } 
        for own_node in self.nodes: 
             for other_node in self._next_layer.nodes: 
                  self.weight_changes[(own_node,other_node)] =  0.0 

          # set up the calculated values for each node -- these will
          # actually just be set from inputs into the network.
             
        self.values = { } 
        for node in self.nodes: 
             # set the bias node -- always has a value of 1
             if node==0:  
                 self.values[0] = 1 
             else: 
                  self.values[node] = 0 
             


Clone Instance
2
Line Count
20
Source Line
164
Source File
Bio/NeuralNetwork/BackPropagation/Layer.py

        # set up the weights
        self.weights = { } 
        for own_node in self.nodes: 
             for other_node in self._next_layer.nodes: 
                  self.weights[(own_node,other_node)] =
                                          random.randrange( -2.0,2.0) 

          # set up the weight changes
             
        self.weight_changes = { } 
        for own_node in self.nodes: 
             for other_node in self._next_layer.nodes: 
                  self.weight_changes[(own_node,other_node)] =  0.0 

          # set up the calculated values for each node
             
        self.values = { } 
        for node in self.nodes: 
             # bias node
             if node==0:  
                 self.values[node] = 1 
             else: 
                  self.values[node] = 0 
             


Clone AbstractionParameter Count: 1Parameter Bindings

# set up the weights
self.weights = { } 
for own_node in self.nodes:
  for other_node in self._next_layer.nodes:
  
    self.weights[(own_node,other_node)] = random.randrange( -2.0,2.0) 
    # set up the weight changes
  
self.weight_changes = { } 
for own_node in self.nodes:
  for other_node in self._next_layer.nodes:
  
    self.weight_changes[(own_node,other_node)] = 0.0 
    # set up the calculated values for each node
    # set up the calculated values for each node -- these will
    # actually just be set from inputs into the network.
  
self.values = { } 
for node in self.nodes:
  # bias node
  # set the bias node -- always has a value of 1
  if node==0:
  
    self.values[ [[#variable78abc4c0]]] = 1 
  else:
  
    self.values[node] = 0 
  
 

CloneAbstraction
Parameter Bindings
Parameter
Index
Clone
Instance
Parameter
Name
Value
11[[#78abc4c0]]
node 
12[[#78abc4c0]]
0