/// <summary>
/// Constructs a new network using a fixed mixing ration between two parents.
/// </summary>
/// <param name="parent1">The fist parent network.</param>
/// <param name="parent2">The second parent network.</param>
/// <param name="mixingRatio">The probability with which the first(second) child will have genes from the first(second) parent.</param>
/// <param name="ch1">When this method returns contains the first child network.</param>
/// <param name="ch2">When this method returns contains the second child network.</param>
private void UniformCrossover(NeuralNetwork parent1, NeuralNetwork parent2, float mixingRatio, out NeuralNetwork ch1, out NeuralNetwork ch2)
{
DenseLayer[] parent1Layers = parent1.GetLayers();
DenseLayer[] parent2Layers = parent2.GetLayers();
var child1Layers = new DenseLayer[parent1Layers.Length];
var child2Layers = new DenseLayer[parent1Layers.Length];
for (int k = 0; k < child1Layers.Length; k++)
{
DenseLayer parent1Layer = parent1Layers[k];
DenseLayer parent2Layer = parent2Layers[k];
var child1Weights = new double[parent1Layer.InputNeuronsCount + 1, parent1Layer.OutputNeuronsCount];
var child2Weights = new double[parent1Layer.InputNeuronsCount + 1, parent1Layer.OutputNeuronsCount];
for (int i = 0; i < child1Weights.GetLength(0); i++)
{
for (int j = 0; j < child1Weights.GetLength(1); j++)
{
if (random.NextDouble() < mixingRatio)
{
child1Weights[i, j] = parent1Layer[i, j];
child2Weights[i, j] = parent2Layer[i, j];
}
else
{
child1Weights[i, j] = parent2Layer[i, j];
child2Weights[i, j] = parent1Layer[i, j];
}
}
}
child1Layers[k] = new DenseLayer(child1Weights, parent1Layer.GetActivationFunction());
child2Layers[k] = new DenseLayer(child2Weights, parent1Layer.GetActivationFunction());
}
ch1 = new NeuralNetwork(child1Layers);
ch2 = new NeuralNetwork(child2Layers);
}
