In this file you can describe your network.

*********************
* Network structure *
*********************

Without this section your NeuralNetwork object will have
NO neurons (unless you modify source code) and is therefore
quite useless!

Currently it is possible to create only a fully connected
multilayer feedforward network. To do that, just answer
some easy questions below.

First layer in network is input layer. It consists of simple
buffer nodes that do NOT compute anything, but only hold
input values. How many input nodes your network must have?

  input_layer: 2

Other layers consist of computing neurons. How many nodes
should be in computational layers starting from the input
layer side of the network? For adding a layer just add a
line with the keyword "comp_layer:". The last computational
layer will also act as the output layer. Layers without
any neurons will be ignored.

  comp_layer: 4
  comp_layer: 4
  comp_layer: 1


**********************
* Neuron parameters  *
**********************

Work of an artificial neural network depends on neuron parameters.
If you have no idea what they should be, then you may set them
to default values (which are given in help texts before
each parameter). Or just leave them empty (delete the
numbers after each parameter identifier), then they will be
automatically set to default values. Actually it is even allowed
to delete this section "Neuron parameters" completely from this
file and leave only the "Network structure" section. Then
everything will be set to default as well. But make a backup
of this file first ;)

Currently it is possible to use only one type of neuron.
Its activation function is sigmoidal (logistic function
1 / (1 + exp(-av)) where "a" is slope parameter and "v"
is internal activity level).


- Parameter: treshold. Type: double. Default value: 0 - -
Treshold (or bias) value is used for lowering or increasing
the sum (internal activity level) before applying the activation
function.

  treshold: 0

- Parameter: slope_parameter. Type: double. Default value: 0.5 - -
It is the slope parameter of the sigmoid function. When slope
parameter approaches infinity, sigmoid function becomes simply a
treshold function (do not confuse with treshold parameter, which was
described above).

  slope_parameter: 0.5


********************
* Additional info  *
********************

It is also possible in this file to set max_weight parameter
(normally you should do it in training configuration file) and
all weights of connections. However, in normal circumstances
you shouldn't do it here. If you want to set all weights manually,
then first generate a network with needed structure, then edit this
generated file according to your needs. You can then use the
generated and edited file as a config file for creating
a new network object.

Note: you will see that generated configuration file contains
max_weight parameter. In most cases it is not used during the
normal work of neural network, but some rare applications may
use it (like my neural network visualizer). Therefore when you
change the weights manually, consider if it is also necessary
to change max_weight value.