class NeuralNet: public Graph

This class implements a generic (acyclic, non-recurrent) neural network. It is a graph of neurons and weights, relying on the methods of the Neuron and Weight classes to calculate the output of the network and gradients for use in BP-based systems.
For the network to calculate gradients correctly, these steps must be performed:

1. Set the inputs to the networks
2. Calling N the function of the network outputs whose gradient we wish to calculate, set the 'delta' values to the partial derivatives dN/d(out), for each output neuron.
3. Call gradient() for each weight.

Attributes:

std::vector<InputTerminal*> VI

The input terminals. Note that each of them must be present also in the V set (inherited from Graph) to avoid memory leaks.

std::vector<Neuron*> VO

The output neurons. Note that each of them must be present also in the V set (inherited from Graph) to avoid memory leaks.

Methods:

void setInput(const std::vector<double> &inV) throw (length_error)

Sets the value of each input terminal to the given value (inV[0] to VI[0] and so on), throwing an exception if the two vectors have different sizes.

void value(std::vector<double> *outV) throw (length_error)

Sets the given vector to the output values from the network (VO[0] to (*outV)[0] and so on), throwing an exception if the two vectors have different sizes.

void setDelta(const std::vector<double> &deltaV) throw (length_error)

Sets the 'delta' value of each output neuron to the given value (deltaV[0] to VO[0] and so on), throwing an exception if the two vectors have different sizes.

Streamers:

ostream& operator<<(ostream &o,const NeuralNet &n)

Writes a representation of the network to the given stream. Uses writeNodes<>() and writeLinks<>().

istream& operator>>(istream &i,NeuralNet &n)

Reads back a representation of a network from the given stream. Uses readNodes<>() and readLinks<>(), with NeuronInserter, ITInserter and LinkInserter.