CloneSet246


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Clone
Mass
Clones in
CloneSet
Parameter
Count
Clone
Similarity
Syntax Category
[Sequence Length]
8220.950compound_stmt
Clone AbstractionParameter Bindings
Clone Instance
(Click to see clone)
Line CountSource Line
Source File
18193
Bio/NeuralNetwork/BackPropagation/Layer.py
28283
Bio/NeuralNetwork/BackPropagation/Layer.py
Clone Instance
1
Line Count
8
Source Line
193
Source File
Bio/NeuralNetwork/BackPropagation/Layer.py

        # update each node in this network
        for update_node in self.nodes[1: ]:
                                          
            # sum up the weighted inputs from the previous network
            sum = 0.0 
            for node in previous_layer.nodes: 
                 sum+=(previous_layer.values[node]*previous_layer.weights[(node,update_node)]) 


            self.values[update_node] = self._activation(sum) 

        # propogate the update to the next layer


Clone Instance
2
Line Count
8
Source Line
283
Source File
Bio/NeuralNetwork/BackPropagation/Layer.py

        # update all of the nodes in this layer
        for update_node in self.nodes: 
             # sum up the contribution from all of the previous inputs
             sum = 0.0 
             for node in previous_layer.nodes: 
                  sum+=(previous_layer.values[node]*previous_layer.weights[(node,update_node)]) 


             self.values[update_node] = self._activation(sum) 


Clone AbstractionParameter Count: 2Parameter Bindings

# update all of the nodes in this layer
# update each node in this network
for update_node in [[#variable5d6e11a0]] [[#variable5d6e12c0]]:
  # sum up the contribution from all of the previous inputs
  # sum up the weighted inputs from the previous network
  sum = 0.0 
  for node in previous_layer.nodes:
  
    sum+=(previous_layer.values[node]*previous_layer.weights[(node,update_node)]) 
  self.values[update_node] = self._activation(sum) 
  # propogate the update to the next layer
 

CloneAbstraction
Parameter Bindings
Parameter
Index
Clone
Instance
Parameter
Name
Value
11[[#5d6e11a0]]
self 
12[[#5d6e11a0]]
self.nodes 
21[[#5d6e12c0]]
.nodes 
22[[#5d6e12c0]]
[1: ]