public void Mutate()
{
#if false
int ran = NeuralNet.RandomGenerator.Next(1, 8);
if (ran == 1)
{
return;
}
else if (ran > 4)
{
InputSynapses.ForEach(syn =>
{
if (NeuralNet.RandomGenerator.Next(1, 2) == 1)
{
syn.Weight *= 1 + (NeuralNet.RandomGenerator.NextDouble() - 0.5) / 10;
}
});
}
else if (ran == 2)
{
InputSynapses.ForEach(syn =>
{
if (NeuralNet.RandomGenerator.Next(1, 5) == 1)
{
syn.Weight *= -1;
}
});
}
else if (ran == 3)
{
Bias *= 1 + (NeuralNet.RandomGenerator.NextDouble() - 0.5) / 5;
}
else if (ran == 4)
{
Bias *= -1;
}
#else
InputSynapses.ForEach(syn =>
{
var ran = NeuralNet.RandomGenerator.NextDouble();
if (ran < Constants.Con.mutate_percent_of_synapses_uniform)
{
syn.Weight += NeuralNet.RandomGenerator.NextGaussian(0, Constants.Con.mutation_power_synapse);
}
else if (ran < Constants.Con.mutate_percent_of_synapses_uniform + Constants.Con.mutate_percent_of_synapses_new_values)
{
syn.Weight = NeuralNet.RandomGenerator.NextGaussian(0, Constants.Con.init_stdDev_synapse);
}
syn.Weight = Math.Max(-30, Math.Min(30, syn.Weight));
});
{//bias
var ran = NeuralNet.RandomGenerator.NextDouble();
if (ran < Constants.Con.mutate_percent_of_biases_uniform)
{
Bias += NeuralNet.RandomGenerator.NextGaussian(0, Constants.Con.mutation_power_bias);
}
else if (ran < Constants.Con.mutate_percent_of_synapses_uniform + Constants.Con.mutate_percent_of_biases_new_values)
{
Bias = NeuralNet.RandomGenerator.NextGaussian(0, Constants.Con.init_stdDev_bias);
}
Bias = Math.Max(-30, Math.Min(30, Bias));
}
#endif
}
//public void UpdateWeights(double learnRate, double momentum)
//{
// var prevDelta = BiasDelta;
// BiasDelta = learnRate * Gradient;
// Bias += BiasDelta + momentum * prevDelta;
// foreach (var synapse in InputSynapses)
// {
// prevDelta = synapse.WeightDelta;
// synapse.WeightDelta = learnRate * Gradient * synapse.InputNeuron.Value;
// synapse.Weight += synapse.WeightDelta + momentum * prevDelta;
// }
//}
#endregion
public void ResetNeuron()
{
Initialization();
InputSynapses.ForEach(synapse => synapse.Initialization());
}
