private void readFile()
{
string[] lines = File.ReadAllLines("USPopulation.txt");
if (lines.Length != LAST_YEAR - FIRST_YEAR + 1)
{
MessageBox.Show("Wrong number of lines in input file");
Application.Current.Shutdown();
}
int year = FIRST_YEAR;
foreach (string line in lines)
{
try
{
years.Add(year, int.Parse(line));
dataList.Add(new KeyValuePair <int, int>(year, int.Parse(line)));
PopulationList.Add("The population in " + year + " is " + line);
}
catch (Exception)
{
MessageBox.Show("Incorrect data in input file");
Application.Current.Shutdown();
}
year++;
}
}
public GeneticAlgorithm(int[] shape, int populationSize, Random rnd, double minWeightValue, double maxWeightValue, double[] inputExample)
{
PopulationSize = populationSize;
Rnd = rnd;
MinWeightValue = minWeightValue;
MaxWeightValue = maxWeightValue;
InputExample = inputExample;
Shape = shape;
for (int i = 0; i < populationSize; i++)
{
PopulationList.Add(new NeuronNet(shape, Rnd));
}
foreach (NeuronNet neuronNet in PopulationList) //randomize individuals
{
neuronNet.SetInputLayer(inputExample);
neuronNet.RandomizeWeights(MinWeightValue, MaxWeightValue);
}
}
/// <summary>
///
/// </summary>
/// <param name="Worker"></param>
private void AddWorkerToList(Worker worker)
{
// Vérifie si le nombre de slots de populaton est suffisant
// avant de créer l'unité
try
{
if (CheckFreeSlotInPopulation(worker))
{
PopulationList.Add(worker);
PopulationChanged(this, new PopulationChangedEventArgs {
ActualPopulation = PopulationList.Sum(x => x.PopulationSlots), Unit = worker
});
}
}
catch (NotEnoughUnitSlotsAvailableException)
{
throw;
}
}
public void Add(T element)
{
PopulationList.Add(element);
}
private void NearGenesCrossWithOffspring(NeuronNet individualA, NeuronNet individualB)
{
NeuronNet offspring = new NeuronNet(Shape, Rnd);
double extraRange = 0.1;
offspring.SetInputLayer(InputExample);
if (individualA.Layers.Count != individualB.Layers.Count)
{
throw new Exception("Different layer size. Can not cross");
}
for (int i = 0; i < individualA.Layers.Count; i++)
{
if (individualA.Layers[i].Neurons.Count != individualB.Layers[i].Neurons.Count)
{
throw new Exception("Different neuron size. Can not cross");
}
for (int j = 0; j < individualA.Layers[i].Neurons.Count; j++)
{
double auxBiasA = individualA.Layers[i].Neurons[j].Bias;
double auxBiasB = individualB.Layers[i].Neurons[j].Bias;
double maximumBias;
double minimumBias;
if (auxBiasA > auxBiasB)
{
maximumBias = auxBiasA + (auxBiasA * extraRange);
minimumBias = auxBiasB - (auxBiasA * extraRange);
}
else
{
maximumBias = auxBiasB + (auxBiasA * extraRange);
minimumBias = auxBiasA - (auxBiasA * extraRange);
}
offspring.Layers[i].Neurons[j].Bias = Rnd.NextDouble() * (maximumBias - minimumBias) + minimumBias;
if (individualA.Layers[i].Neurons[j].Dendrites.Count != individualB.Layers[i].Neurons[j].Dendrites.Count)
{
throw new Exception("Different dendrite count. Can not cross");
}
for (int k = 0; k < individualA.Layers[i].Neurons[j].Dendrites.Count; k++)
{
double auxDendriteWeightA = individualA.Layers[i].Neurons[j].Dendrites[k].Weight;
double auxDendriteWeightB = individualB.Layers[i].Neurons[j].Dendrites[k].Weight;
double maximum;
double minimum;
if (auxDendriteWeightA > auxDendriteWeightB)
{
maximum = auxDendriteWeightA + (auxDendriteWeightA * extraRange);
minimum = auxDendriteWeightB - (auxDendriteWeightA * extraRange);
}
else
{
maximum = auxDendriteWeightB + (auxDendriteWeightA * extraRange);
minimum = auxDendriteWeightA - (auxDendriteWeightA * extraRange);
}
offspring.Layers[i].Neurons[j].Dendrites[k].Weight = Rnd.NextDouble() * (maximum - minimum) + minimum;
}
}
}
PopulationList.Add(offspring);
}