public NeuralOutput Think(SensoryInput input)
{
for (var layer = 0; layer < Network.Layers.Length; ++layer)
{
for (var neuron = 0; neuron < Network.Layers[layer].Neurons.Length; neuron++)
{
if (layer == 0)
{
Network.Layers[layer].Neurons[neuron].Value = input.Values[neuron];
}
else
{
Network.Layers[layer].Neurons[neuron].Value = 0;
for (var dentrite = 0; dentrite < Network.Layers[layer - 1].Neurons.Length; ++dentrite)
{
Network.Layers[layer].Neurons[neuron].Value = Network.Layers[layer].Neurons[neuron].Value +
Network.Layers[layer - 1].Neurons[dentrite]
.Value *
Network.Layers[layer].Neurons[neuron].Dendrite
[dentrite].Weight;
}
Network.Layers[layer].Neurons[neuron].Value =
MathUtilityHelper.HyperbolicTangent(Network.Layers[layer].Neurons[neuron].Value);
}
}
}
NeuralOutput output;
output.Values = new double[Network.Layers[Network.Layers.Length - 1].Neurons.Length];
for (var neuron = 0; neuron < output.Values.Length; ++neuron)
{
output.Values[neuron] = Network.Layers[Network.Layers.Length - 1].Neurons[neuron].Value;
// Mathf.Clamp((float) Network.Layers[Network.Layers.Length - 1].Neurons[neuron].Value, -1, 1);
}
return(output);
}
public NeuralOutput Think(SensoryInput input)
{
for (var layer = 0; layer < Network.Layers.Length; ++layer)
{
for (var neuron = 0; neuron < Network.Layers[layer].Neurons.Length; neuron++)
{
if (layer == 0)
Network.Layers[layer].Neurons[neuron].Value = input.Values[neuron];
else
{
Network.Layers[layer].Neurons[neuron].Value = 0;
for (var dentrite = 0; dentrite < Network.Layers[layer - 1].Neurons.Length; ++dentrite)
{
Network.Layers[layer].Neurons[neuron].Value = Network.Layers[layer].Neurons[neuron].Value +
Network.Layers[layer - 1].Neurons[dentrite]
.Value*
Network.Layers[layer].Neurons[neuron].Dendrite
[dentrite].Weight;
}
Network.Layers[layer].Neurons[neuron].Value =
MathUtilityHelper.HyperbolicTangent(Network.Layers[layer].Neurons[neuron].Value);
}
}
}
NeuralOutput output;
output.Values = new double[Network.Layers[Network.Layers.Length - 1].Neurons.Length];
for (var neuron = 0; neuron < output.Values.Length; ++neuron)
{
output.Values[neuron] = Network.Layers[Network.Layers.Length - 1].Neurons[neuron].Value;
// Mathf.Clamp((float) Network.Layers[Network.Layers.Length - 1].Neurons[neuron].Value, -1, 1);
}
return output;
}
