public void AddConnection(string connection)
{
int botId;
Match match = constAssignment.Match(connection);
if (match.Success) {
int binValue = int.Parse(match.Groups[1].Captures[0].ToString());
botId = int.Parse(match.Groups[2].Captures[0].ToString());
var binInput = new BinInput(binValue);
GetBot(botId).AssignInput(binInput);
return;
}
match = botAssignment.Match(connection);
botId = int.Parse(match.Groups[1].Captures[0].ToString());
bool lowIsOutput = match.Groups[2].Captures[0].ToString() == "output";
int lowOutputId = int.Parse(match.Groups[3].Captures[0].ToString());
var lowBotInput = new BotInput(GetBot(botId), BotInput.OutputType.LOW);
bool highIsOutput = match.Groups[4].Captures[0].ToString() == "output";
int highOutputId = int.Parse(match.Groups[5].Captures[0].ToString());
var highBotInput = new BotInput(GetBot(botId), BotInput.OutputType.HIGH);
if (lowIsOutput) {
outputBins.Add(lowOutputId, lowBotInput);
} else {
GetBot(lowOutputId).AssignInput(lowBotInput);
}
if (highIsOutput) {
outputBins.Add(highOutputId, highBotInput);
} else {
GetBot(highOutputId).AssignInput(highBotInput);
}
}
/// <summary>
/// Initializes a neural network before use.
/// </summary>
/// <param name="inputSize">Amount of inputs for the neural network.</param>
/// /// <param name="outputSize">Amount of output for the neural network.</param>
/// /// <param name="hiddenLayers">Amount of hidden layers for the neural network.</param>
public NeuralNetwork(int inputSize, int outputSize, int[] hiddenLayers, float mutateChance, int memorySlotSize, BinInput input)
{
inputFunc = input;
//debugging
if (inputSize <= 0 || outputSize <= 0)
{
Debug.LogError("In or output size is zero!");
return;
}
this.mutateChance = mutateChance;
int nLength, wLength;
#region Neurons
neurons = new float[hiddenLayers.Length + 2][];
//init hidden layer neurons
for (int layer = 0; layer < hiddenLayers.Length; layer++)
{
neurons[layer + 1] = new float[hiddenLayers[layer]];
}
//init input neurons
neurons[0] = new float[inputSize];
/*init output neurons
* reusing old variable for different purpose*/
nLength = neurons.Length - 1;
neurons[nLength] = new float[outputSize];
#endregion
#region Weights
//init weights (and set node + weight values)
weights = new float[nLength][][];
for (int layer = 0; layer < nLength; layer++)
{
weights[layer] = new float[neurons[layer].Length][];
}
//get each connection
for (int layer = 0; layer < nLength; layer++)
{
for (int neuron = 0; neuron < neurons[layer].Length; neuron++)
{
//create weights
weights[layer][neuron] = new float[neurons[layer + 1].Length];
//randomize base values for weights
wLength = weights[layer][neuron].Length;
for (int weigth = 0; weigth < wLength; weigth++)
{
weights[layer][neuron][weigth] = Ran();
}
}
}
#endregion
memoryPrefab = new float[memorySlotSize][];
for (int memorySlot = 0; memorySlot < memoryPrefab.Length; memorySlot++)
{
memoryPrefab[memorySlot] = new float[hiddenLayers[0]];
}
RanMemory();
}
