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a neural network
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initialize all the weights in the network to random numbers **Arguments** - minWeight: the minimum value a weight can take - maxWeight: the maximum value a weight can take |
Fully connects each layer in the network to the one above it **Note** this sets the connections, but does not assign weights |
build an unconnected network and set node counts **Arguments** - nodeCounts: a list containing the number of nodes to be in each layer. the ordering is: (nInput,nHidden1,nHidden2, ... , nHiddenN, nOutput) |
returns a list of input node indices |
returns a list of output node indices |
returns a list of hidden nodes in the specified layer |
returns the total number of nodes |
returns the number of hidden layers |
returns a particular node |
returns a list of all nodes |
classifies a given example and returns the results of the output layer. **Arguments** - example: the example to be classified **NOTE:** if the output layer is only one element long, a scalar (not a list) will be returned. This is why a lot of the other network code claims to only support single valued outputs. |
Constructor This constructs and initializes the network based upon the specified node counts. A fully connected network with random weights is constructed. **Arguments** - nodeCounts: a list containing the number of nodes to be in each layer. the ordering is: (nInput,nHidden1,nHidden2, ... , nHiddenN, nOutput) - nodeConnections: I don't know why this is here, but it's optional. ;-) - actFunc: the activation function to be used here. Must support the API of _ActFuncs.ActFunc_. - actFuncParms: a tuple of extra arguments to be passed to the activation function constructor. - weightBounds: a float which provides the boundary on the random initial weights |
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