public void MutateTest()
{
var neuralNetParameters = new NeuralNetParameters()
{
ActivationFunctionType = EActivationFunctionType.Tanh,
InputLayerSize = 2,
HiddenLayerSize = 3,
OutputLayerSize = 2,
NumberOfHiddenLayers = 1
};
var neuralNet = new NeuralNet(neuralNetParameters);
var input = new double[] { 1.0, -1.0 };
neuralNet.Randomize();
var result1 = neuralNet.Think(input);
neuralNet.Mutate(1.0);
var result2 = neuralNet.Think(input);
Assert.AreEqual(2, result1.Length);
Assert.AreEqual(2, result2.Length);
Assert.AreNotEqual(result1[0], result2[0]);
Assert.AreNotEqual(result1[1], result2[1]);
}
public void CrossoverIdenticalNeuralNetsTest()
{
var neuralNetParameters = new NeuralNetParameters()
{
ActivationFunctionType = EActivationFunctionType.Tanh,
InputLayerSize = 2,
HiddenLayerSize = 3,
OutputLayerSize = 2,
NumberOfHiddenLayers = 1
};
var neuralNet1 = new NeuralNet(neuralNetParameters);
neuralNet1.Randomize();
var neuralNet2 = neuralNet1.GetClone();
var child = neuralNet1.Crossover(neuralNet2);
var input = new double[] { 1.0, -1.0 };
var neuralNet1Result = neuralNet1.Think(input);
var neuralNet2Result = neuralNet2.Think(input);
var childResult = child.Think(input);
Assert.AreEqual(2, childResult.Length);
Assert.AreEqual(neuralNet1Result[0], childResult[0]);
Assert.AreEqual(neuralNet1Result[1], childResult[1]);
Assert.AreEqual(neuralNet2Result[0], childResult[0]);
Assert.AreEqual(neuralNet2Result[1], childResult[1]);
}
public static NeuralNet GetRandomMultiLayerNeuralNet(NeuralNetParameters parameters)
{
var multiLayerNeuralNet = GetMultiLayerNeuralNet(parameters);
multiLayerNeuralNet.Randomize();
return(multiLayerNeuralNet);
}
public static NeuralNet GetMultiLayerNeuralNet(NeuralNetParameters parameters)
{
return(new NeuralNet(parameters));
}
