Class Network

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