Exemplo n.º 1
0
        public XorEvolver(IFitnessTester fitnessTester)
        {
            this.fitnessTester = fitnessTester;
            IEnumerable <NeuralNetwork> networks = Helpers.Repeat(
                () => new NeuralNetwork(NeuralDna.Random(9), 2, 2, 1),
                PopulationSize);

            this.population = networks.ToDictionary(nn => nn, nn => this.fitnessTester.Fitness(nn));
        }
Exemplo n.º 2
0
        public void ProcessGeneration()
        {
            List <NeuralNetwork> newNetworks = new List <NeuralNetwork>();

            for (int i = 0; i < this.population.Count(); i++)
            {
                NeuralNetwork father = this.population.Keys.Random(nn => this.population[nn]);
                NeuralNetwork mother = this.population.Keys.Except(new[] { father }).Random(nn => this.population[nn]);

                NeuralDna childDna = father.Dna.Crossover(mother.Dna);
                childDna.Mutate(MutationRate);

                NeuralNetwork child = new NeuralNetwork(childDna, father.NumInputs, father.LayerNums);

                newNetworks.Add(child);
            }

            this.population = newNetworks.ToDictionary(nn => nn, nn => this.fitnessTester.Fitness(nn));
        }
Exemplo n.º 3
0
        public NeuralNetwork(NeuralDna dna, int numInputs, params int[] layerNums)
        {
            this.dna       = dna;
            this.numInputs = numInputs;
            this.layerNums = layerNums;

            Queue <double>     genes  = new Queue <double>(dna.Genes);
            List <NeuronLayer> layers = new List <NeuronLayer>();

            int previousLayer = numInputs;

            foreach (int layerNum in layerNums)
            {
                layers.Add(new NeuronLayer(genes, layerNum, previousLayer));
                previousLayer = layerNum;
            }

            if (genes.Any())
            {
                throw new ArgumentException();
            }

            this.layers = layers;
        }