本文整理汇总了C#中Population.GetTop方法的典型用法代码示例。如果您正苦于以下问题：C# Population.GetTop方法的具体用法？C# Population.GetTop怎么用？C# Population.GetTop使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Population的用法示例。
在下文中一共展示了Population.GetTop方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: EvaluateProbe
//.........这里部分代码省略.........
listaRecomendacao[j] = tupla;
}
list_of_predictions.Add(listaRecomendacao);
}
//Usar o melhor
double maiorMapping = 0;
int idMaiorMapping = 0;
//Testar cada individual
for (int k = 0; k < list_of_predictions.Count; k++)
{
int[] prediction_probe = (from t in list_of_predictions[k] select t.Item1).ToArray();
double resultado = PrecisionAndRecall.AP(prediction_probe, correct_items);
if (resultado > maiorMapping)
{
maiorMapping = resultado;
idMaiorMapping = k;
}
}
//Set global so Fitness itens can see.
list_prediction_probes = list_of_predictions;
correct_items_global = correct_items;
//Algortimo Genetico
/* // Crossover = 80%
// Mutation = 5%
// Population size = 100
// Generations = 2000
// Genome size = 2
GA ga = new GA(0.8, 0.05, 40, 400, list_prediction_probes.Count);
ga.FitnessFunction = new GAFunction(Fitness);
//ga.FitnessFile = @"H:\fitness.csv";
ga.Elitism = true;
ga.Go();
double[] values;
double fitness;
ga.GetBest(out values, out fitness);*/
//create the GA using an initialised population and user defined Fitness Function
const double crossoverProbability = 0.85;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create a Population of random chromosomes of length 44
var population = new Population(40, list_of_predictions.Count * 10, false, false);
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.DoublePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, CalculateFitness);
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(Terminate);
var best = population.GetTop(1)[0];
double rangeConst = 1 / (System.Math.Pow(2, 10) - 1);
ga_weights[original] = new List<double>();
for (int i = 0; i < list_prediction_probes.Count; i++)
{
string str = best.ToBinaryString((i * 10), 10);
Int64 convertInt32 = Convert.ToInt32(str, 2);
double x = (convertInt32 * rangeConst);
ga_weights[original].Add(x);
}
best_alg[original] = idMaiorMapping;
num_users++;
if (num_users % 10 == 0)
Console.Error.Write(".");
if (num_users % 100 == 0)
Console.Error.WriteLine("");
}
}示例2: Invoke
public void Invoke(Population currentPopulation,
ref Population newPopulation, FitnessFunction fitnesFunctionDelegate)
{
//take top 3
var num = 3;
var min = System.Math.Min (num, currentPopulation.Solutions.Count);
var best = currentPopulation.GetTop (min);
var cutoff = best [min-1].Fitness;
var genecount = best [0].Genes.Count;
try
{
var config_vars = (ConfigVars)best[rand.Next(0,min-1)].Genes [rand.Next(0,genecount-1)].ObjectValue;
var index = rand.Next(0, config_vars.vars.Count-1);
var key = config_vars.vars. ElementAt(index).Key;
newPopulation.Solutions.Clear();
foreach (var chromosome in currentPopulation.Solutions) {
if (chromosome.Fitness < cutoff) {
foreach (var gene in chromosome.Genes) {
var target_config_vars = (ConfigVars)gene.ObjectValue;
target_config_vars.vars [key] = config_vars.vars [key];
}
}
newPopulation.Solutions.Add (chromosome);
}
_invoked++;
}
catch(Exception e) {
Console.WriteLine ("OOPS! " + e.Message + " " + e.StackTrace);
}
}