private object InnerGet(Type expectType, bool autoResolving, params object[] lastMappingValues)
{
if (expectType == null)
{
throw new ArgumentNullException(nameof(expectType));
}
using (GA.Lock(this.UUID + expectType.AssemblyQualifiedName))
{
var callSite = GetCallSite(this, expectType);
if (callSite == null && autoResolving)
{
callSite = this.AutoResolving(expectType, null);
if (callSite == null)
{
return(null);
}
CacheType.TryAdd(expectType, new List <ICallSite>(1)
{
callSite
});
}
return(callSite?.Invoke(lastMappingValues));
}
}
public void Remove(Type expectType, string name, bool promote = false)
{
if (expectType == null)
{
throw new ArgumentNullException(nameof(expectType));
}
if (string.IsNullOrWhiteSpace(name))
{
throw new ArgumentNullException(nameof(name));
}
ConcurrentDictionary <string, ICallSite> dict;
using (GA.Lock(expectType))
{
if (CacheTypeName.TryGetValue(expectType, out dict))
{
ICallSite callSite;
dict.TryRemove(name, out callSite);
}
}
if (this._hasParent && promote)
{
this.Parent.Remove(expectType, name, promote);
}
}
private static void SumTo50()
{
Console.WriteLine("Sum to 50:...");
GA ga = new GA(
20, // Number of individuals
3, // Number of genes in the genotype
individual => // Fitness function
Math.Abs(50 - individual.Genotype.Sum(val => val))
);
ga.RunEpoch(
500, // Number of generations to run for
null, // Action to perform for each generation
() => // Action to perform once fitness has improved
{
Console.WriteLine(
"Gen {2}: Fit={1}, Genotype={0}",
string.Join(
" ",
ga.BestIndividual.Genotype.Select(val => val.ToString("0.00"))),
ga.BestIndividual.Fitness.ToString("0.00"),
ga.CurrentEpochGeneration);
});
Console.WriteLine("Sum to 5: done!");
Console.WriteLine("");
}
private static void Find12345()
{
Console.WriteLine("Find 1 2 3 4 5:...");
GA ga = new GA(
200, // Number of individuals
5, // Number of genes in the genotype
individual => // Fitness function
Math.Abs(individual.Genotype[0] - 1) +
Math.Abs(individual.Genotype[1] - 2) +
Math.Abs(individual.Genotype[2] - 3) +
Math.Abs(individual.Genotype[3] - 4) +
Math.Abs(individual.Genotype[4] - 5));
ga.RunEpoch(500, null, () =>
{
Console.WriteLine(
"Gen {2}: Fit={1}, Genotype={0}",
string.Join(
" ",
ga.BestIndividual.Genotype.Select(
val => val.ToString("0.00"))),
ga.BestIndividual.Fitness.ToString("0.00"),
ga.CurrentEpochGeneration);
});
Console.WriteLine("Find 1 2 3 4 5: done!");
Console.WriteLine("");
}
static void CurrentDomain_UnhandledException(object sender, UnhandledExceptionEventArgs e)
{
if (e.ExceptionObject is Exception)
{
GA.OnGlobalError(sender, e.ExceptionObject as Exception);
}
else
{
WriteUnhandledException("应用崩溃了：{0}", e.ExceptionObject);
}
}
static void CurrentDomain_UnhandledException(object sender, UnhandledExceptionEventArgs e)
{
//System.Runtime.CompilerServices.RuntimeWrappedException
if (e.ExceptionObject is Exception)
{
GA.OnGlobalError(sender, e.ExceptionObject as Exception);
}
else
{
WriteUnhandledException("应用崩溃了：{0}", e.ExceptionObject);
}
}
public void FindProjectionMatrix()
{
List<WorldToScreenCase> cases =
new List<WorldToScreenCase>
{
new WorldToScreenCase(688, 349, 0,0 ,0), // 1
new WorldToScreenCase(454, 155, 52.5,0,0), // 2
new WorldToScreenCase(353, 70, 105,0,0), // 3
new WorldToScreenCase(127, 169, 52.5, 34,0), // 4
new WorldToScreenCase(155, 411, 0, 34,0), // 5
new WorldToScreenCase(220, 364, 0, 34 - 7.32*0.5, 2.44), // 6
new WorldToScreenCase(80, 378, 0, 34+7.32*0.5,2.44), // 7
new WorldToScreenCase(92, 57, 105, 34 + 7.32*0.5, 2.44), // 8
new WorldToScreenCase(145, 56, 105, 34-7.32*0.5,2.44), // 9
};
GA ga = new GA(
5000, // Number of individuals
16, // Number of genes in the genotype
individual => // Fitness function
{
double errorSum = 0;
foreach (WorldToScreenCase test in cases)
{
Point3D testScreen = ProjectCase(test.World, individual.Genotype);
double sqrError = (testScreen - test.Screen).LengthSquared;
errorSum += sqrError;
}
return errorSum;
});
ga.RunEpoch(100000,
() =>
{
ga.BreakEpochRun = ga.BestIndividual.Fitness <= 1.0 || stopButton.Enabled == false;
Application.DoEvents();
},
() =>
{
generationLabel.Text = string.Format(
"Gen {2}: Fit={1}, Genotype={0}",
string.Join(
" ",
ga.BestIndividual.Genotype.Select(val => val.ToString("0.00"))),
ga.BestIndividual.Fitness.ToString("0.00"),
ga.CurrentEpochGeneration);
DrawCasesToBitmap(cases, ga.BestIndividual.Genotype);
});
}
static void Main(string[] args)
{
GA Ga;
TravellingSalesman1dObjective Objective;
Galib.Mutation.IntegerSwapMutator Mutator;
IntegerChromosomeSpecifier ChromosomeSpecifier;
//
// Create the objective.
//
int[] cities = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
Objective = new TravellingSalesman1dObjective(cities);
//
// Create the mutator
//
Mutator = new Galib.Mutation.IntegerSwapMutator();
//
// Create the chromosome specifier.
//
ChromosomeSpecifier = new IntegerChromosomeSpecifier();
ChromosomeSpecifier.Length = numberOfCities;
ChromosomeSpecifier.MinValue = 0;
ChromosomeSpecifier.MaxValue = numberOfCities;
ChromosomeSpecifier.PositionDependent = true;
//
// Create the recombinator
//
Galib.Recombination.PartiallyMatchedRecombinator Recombinator;
Recombinator = new Galib.Recombination.PartiallyMatchedRecombinator();
Ga = new GA();
Ga.PopulationSize = 100;
Ga.Mutator = Mutator;
Ga.ChromosomeSpecifier = ChromosomeSpecifier;
Ga.Objective = Objective;
Ga.InvertedObjective = true;
Ga.Recombinator = Recombinator;
Ga.MaxGenerations = 500;
Ga.Run();
Console.WriteLine("Best Individual:");
Console.WriteLine( Ga.Population.BestChromosome );
Console.WriteLine("Finished. Hit return.");
Console.ReadLine();
}
/// <summary>
/// 如果结果出现异常则抛出错误，否则不做任何处理。
/// </summary>
/// <typeparam name="TResult">结果的数据类型。</typeparam>
/// <param name="result"><see cref="System.Result"/> 的派生类实例。</param>
public static TResult ThrowIfFailded <TResult>(this TResult result)
where TResult : Result
{
if (result == null)
{
throw new ArgumentNullException("result");
}
if (result.IsFailed)
{
GA.OnGlobalError(result, result.Exception);
throw result.Exception;
}
return(result);
}
private static void OnceInvoke(object state)
{
AsyncJob tme = state as AsyncJob;
if (tme.WaitForNextTask())
{
return;
}
try
{
tme.RunTask();
}
catch (Exception ex)
{
GA.OnGlobalError(tme, ex);
throw;
}
}
/// <summary>
/// 写入未捕获的异常。该异常不记录到日志管理器，而是独立出一个 LogError{yyyy-MM-dd}.txt 文件。
/// </summary>
/// <param name="message">复合格式的错误消息。</param>
/// <param name="args">一个对象数组，其中包含零个或多个要设置格式的对象。</param>
public static void WriteUnhandledException(string message, params object[] args)
{
var path = GA.IsWebRuntime ? System.Web.Webx.MapUrl("~/LogError" + DateTime.Now.ToString("yyyy-MM-dd") + ".txt") : GA.FullPath("LogError" + DateTime.Now.ToString("yyyy-MM-dd") + ".txt");
System.IO.File.AppendAllText(path, DateTime.Now + " " + string.Format(message, args));
}
private void ShowTestResult(GA ga, WorldToScreenCase test)
{
Point3D p = ProjectCase(test.World, ga.BestIndividual.Genotype);
}
public static extern HWND GetAncestor(HWND hwnd, GA gaFlags);
public static extern IntPtr GetAncestor(IntPtr hWnd, GA gaFlags);
/// <summary> /// 创建一个模拟对象。 /// </summary> /// <typeparam name="TModel">对象的数据类型。</typeparam> /// <returns>要一个模拟的对象。</returns> protected virtual TModel CreateMock <TModel>() => GA.CreateMockModel <TModel>();
private void bw_DoWork(object sender, DoWorkEventArgs e)
{
BackgroundWorker worker = sender as BackgroundWorker;
GaTask task = (GaTask)e.Argument;
try
{
task.ProgressPercent = 0;
task.State = TaskState.Launched;
task.LaunchTime = DateTime.Now;
IList<IIndividual<Wrapper<LearningObject>>> individualsList = new List<IIndividual<Wrapper<LearningObject>>>();
//Individuals initializing
for (int i = 0; i < task.GaConfigs.PopulationSize; i++)
{
//Wrap source LOs to pass to GA
//List<Wrapper<LearningObject>> wrappedListOfLo = new List<Wrapper<LearningObject>>(); = task.SourceLearningObjects.Select(sourceLearningObject => new Wrapper<LearningObject>(GAUtils.GetProbability(0.5), sourceLearningObject)).ToList();
List<Wrapper<LearningObject>> wrappedListOfLo = new List<Wrapper<LearningObject>>();
foreach (var sourceLearningObject in task.SourceLearningObjects)
{
wrappedListOfLo.Add(new Wrapper<LearningObject>(GAUtils.GetProbability(0.5), sourceLearningObject));
}
wrappedListOfLo = GAUtils.Shuffle(wrappedListOfLo).ToList();
individualsList.Add(new Individual<Wrapper<LearningObject>>(wrappedListOfLo));
}
var population = new Population<Wrapper<LearningObject>>(individualsList, IndividualEvaluateFunc, MutationFunc, CrossoverFunc, SelectionFunc);
IGeneticAlgorithm<Wrapper<LearningObject>> ga = new GA<Wrapper<LearningObject>>(population);
IGAconfiguration configuration = new GAConfiguration(task.GaConfigs.GAIterationsNumber,
task.GaConfigs.MutationProbability, task.GaConfigs.MutationPeriod, task.GaConfigs.CrossoverProbability,
task.GaConfigs.ElitismPercentage);
ga.Run(StopCondition, configuration, task.Id, worker);
if (worker != null && worker.CancellationPending)
{
task.State = TaskState.Cancelled;
e.Result = task;
}
else
{
task.State = TaskState.Finished;
}
if (task.State == TaskState.Finished)
{
if (worker != null) worker.ReportProgress(100, task.Id);
}
//Put result data to task
task.AverageFFvalues = ga.Statistics.AverageFitnessFunctionValues;
task.BestFfValues = ga.Statistics.BestFitnessFunctionValues;
task.WorstFfValues = ga.Statistics.WorstFitnessFunctionValues;
List<LearningObject> resultList = new List<LearningObject>();
foreach (var wrapper in ga.BestIndividual.Chromosome.Genes)
{
if (wrapper.Used)
{
resultList.Add(wrapper.Value);
}
}
task.ResultLearningObjects = resultList;
e.Result = task;
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
task.State = TaskState.Error;
task.ErrorMessage = ex.Message;
e.Result = task;
}
}
public static void FindProjectionMatrix()
{
List<WorldToScreenCase> cases =
new List<WorldToScreenCase>
{
// New image
/*new WorldToScreenCase(690, 350, 0, 0),
new WorldToScreenCase(455, 155, 105*0.5, 0),
new WorldToScreenCase(353, 70, 105, 0),
new WorldToScreenCase(128, 169, 105*0.5, 68*0.5),
new WorldToScreenCase(153, 412, 0, 68*0.5),*/
// Old Image
new WorldToScreenCase(165,178 , 0, 0),
new WorldToScreenCase(377,120 , 52.5, 0),
new WorldToScreenCase(513,85 , 105, 0),
new WorldToScreenCase(473,157 , 52.5, 34),
new WorldToScreenCase(611,208 , 52.5, 68),
new WorldToScreenCase(735,136 , 105, 68),
};
GA ga = new GA(
2000, // Number of individuals
16, // Number of genes in the genotype
individual => // Fitness function
{
double errorSum = 0;
foreach (WorldToScreenCase test in cases)
{
Point3D testScreen = ProjectCase(test.World, individual.Genotype);
double sqrError = (testScreen - test.Screen).LengthSquared;
errorSum += sqrError;
}
return errorSum;
});
ga.RunEpoch(10000,
() =>
{
ga.BreakEpochRun = ga.BestIndividual.Fitness <= 1.0 || Console.KeyAvailable;
},
() =>
{
Console.WriteLine(
"Gen {2}: Fit={1}, Genotype={0}",
string.Join(
" ",
ga.BestIndividual.Genotype.Take(5).Select(
val => val.ToString("0.00"))),
ga.BestIndividual.Fitness.ToString("0.00"),
ga.CurrentEpochGeneration);
});
if (Console.KeyAvailable)
{
Console.ReadKey();
}
Console.WriteLine("Results for training set:");
foreach (WorldToScreenCase test in cases)
{
ShowTestResult(ga, test);
}
Console.WriteLine("");
Console.WriteLine("Additional tests:");
ShowTestResult(ga, new WorldToScreenCase(120, 73, 105, 34));
Console.WriteLine("Find ProjectionMatrix: done!");
Console.WriteLine("");
}
private static void ShowTestResult(GA ga, WorldToScreenCase test)
{
Point3D p = ProjectCase(test.World, ga.BestIndividual.Genotype);
Console.WriteLine(
" World ({0:0.00}, {1:0.00}) => \n" +
" Wanted:({2:0.00}, {3:0.00}) \n" +
" Received:({4:0.00}, {5:0.00})",
test.World.X,
test.World.Y,
test.Screen.X,
test.Screen.Y,
p.X,
p.Y);
}
private void Hoskin(int n, double L, double H, bool cum, double startPoint, double volatiles)
{
int m = n;// (int)Math.Pow(n, 2);
double[] phi=new double[m];
double[] cov = new double[m];
double[] psi = new double[m];
double[] output = new double[m];
double[] output2 = new double[m];
while (true)
{
double v = 1;
output[0]=snorm();
phi[0]=0;
for (int i = 0; i < m; i++)
cov[i]=covariance(i, H);
/* simulation */
for (int i = 1; i < m; i++)
{
phi[i - 1] = cov[i];
for (int j = 0; j < i - 1; j++)
{
psi[j] = phi[j];
phi[i - 1] -= psi[j] * cov[i - j - 1];
}
phi[i - 1] /= v;
for (int j = 0; j < i - 1; j++)
{
phi[j] = psi[j] - phi[i - 1] * psi[i - j - 2];
}
v *= (1 - phi[i - 1] * phi[i - 1]);
output[i] = 0;
for (int j = 0; j < i; j++)
{
output[i] += phi[j] * output[i - j - 1];
}
output[i] += Math.Sqrt(v) * snorm();
}
/* rescale to obtain a sample of size 2^(*n) on [0,L] */
double scaling = Math.Pow(L / m, H);
for (int i = 0; i < m; i++)
{
output2[i] = scaling * (output[i]);
if (cum && i > 0)
{
output2[i] += output[i - 1];
}
}
//Start the stopwatch
Stopwatch s = new Stopwatch();
s.Start();
//Set up the Model
List<double>outputv=new List<double>();
List<double>outputv2=new List<double>();
for (int i = 0; i < m; i++)
{
outputv.Add(output[i]);
outputv2.Add(output2[i]);
}
Model model = new Model(startPoint, H, outputv, outputv2, startPoint + 0.01000, m, volatiles);
//Set up the GA and run it
Int32 threads = 10;
GA ga = new GA(model,threads, 0.8, 0.0005, 1000);
ga.Go();
//===============================================================================================
//In this example we have set the model, progress reporter delegate (which is optional), number
//of threads, crossover rate, mutation rate, population size, number of generations
//===============================================================================================
//Get the results
s.Stop();
Console.WriteLine();
Console.WriteLine("Optimisation run finished in " + s.ElapsedMilliseconds.ToString() + " ms.");
double[] bestGenes; double bestFitness;
ga.GetBest(out bestGenes, out bestFitness);
if (bestFitness > 0.1)
{
double old_price = startPoint;//clo->stock_price;
double old_volatilities = volatiles; //bestGenes[0];//clo->variance;
double mean_reversion_rate = bestGenes[0] / 10000000;
double mean_reversion_level = bestGenes[1] / 1000000;
double dt = bestGenes[2] / 10000000;
double variance_variance = bestGenes[3] / 10000000;
double risk_free_rate = bestGenes[4] / 10000000;
for (int i = 0; i < n; i++)
{
double new_volatilities = old_volatilities +
mean_reversion_rate * (mean_reversion_level - old_volatilities) * dt +
variance_variance * output[i];
double new_price = old_price +
risk_free_rate * old_price * dt +
new_volatilities * old_price * output2[i]; //These are already scaled via hoskin */
Ticks.Add(new_price);
old_price = new_price;
old_volatilities = new_volatilities;
}
break;
}
}
/**/
/* for (int i = 0; i < n; i++)
{
/* double new_volatilities = old_volatilities +
mean_reversion_rate * (mean_reversion_level - old_volatilities) * dt +
variance_variance * output[i];
double new_price = old_price +
risk_free_rate * old_price * dt +
new_volatilities * old_price * output2[i]; //These are already scaled via hoskin */
/*double new_volatilities = old_volatilities + variance_variance*output[i];
double new_price = old_price + new_volatilities * output2[i];
Ticks.Add(new_price);
old_price = new_price;
old_volatilities = new_volatilities;
}*/
}
static void Main()
{
IGAconfiguration configuration = new GAConfiguration(100, 0.9, null, 0.6, 0.3);
IList<IIndividual<Wrapper<MyValuableClass>>> individualsList = new List<IIndividual<Wrapper<MyValuableClass>>>();
//Individuals initializing
for (int i = 0; i < 1000; i++)
{
//Wrap source LOs to pass to GA
List<Wrapper<MyValuableClass>> wrappedList = new List<Wrapper<MyValuableClass>>();
for (int j = 0; j < 5; j++)
{
wrappedList.Add(new Wrapper<MyValuableClass>(new MyValuableClass(GAUtils.GetRandom(0, 100))));
}
individualsList.Add(new Individual<Wrapper<MyValuableClass>>(wrappedList));
}
Console.WriteLine("Population creating");
var population = new Population<Wrapper<MyValuableClass>>(individualsList, IndividualEvaluateFunc, MutationFunc, CrossoverFunc, SelectionFunc);
IGeneticAlgorithm<Wrapper<MyValuableClass>> ga = new GA<Wrapper<MyValuableClass>>(population);
ObjectId id = new ObjectId();
Console.WriteLine("GA launching");
BackgroundWorker bgBackgroundWorker = new BackgroundWorker();
ga.Run(StopCondition, configuration, id, bgBackgroundWorker, WriteToCOnsole);
foreach (var val in ga.Statistics.AverageFitnessFunctionValues)
{
Console.WriteLine(val);
}
string fileName = "data.txt";
using (StreamWriter outfile = new StreamWriter(fileName))
{
foreach (var averageFitnessFunctionValue in ga.Statistics.AverageFitnessFunctionValues)
{
outfile.WriteLine(averageFitnessFunctionValue);
}
}
//ga.Run(statistics => statistics.CurrentIteration > configuration.IterationsNumber, configuration, id, bgBackgroundWorker, WriteToCOnsole);
}
private void Optimize()
{
SortedList<int, int> Result = new SortedList<int, int>();
lblScanCount.Text = "ScanCount:---";
lblScanMaxCount.Text = "ScanMaxCount:---";
for (nudAWin.Value = 500; nudAWin.Value > 0; nudAWin.Value--)
{
Simulate(gPairList,20, 30, 100, 0, (double)nudAWin.Value);
int AWCount = 0;
int ScanCount = 0;
int ScanMaxCount = 0;
Analyze(gPairList,out AWCount, out ScanCount, out ScanMaxCount);
//lblAWC.Text = "AWCount: " + AWCount.ToString();
Result.Add((int)nudAWin.Value, AWCount);
}
KeyValuePair<int, int> Best = Result.First(x => (x.Value == Result.Values.Min()));
nudAWin.Value = Best.Key;
lblAWC.Text = "AWCount: " + Best.Value.ToString();
Random rand = new Random();
GA ga = new GA(500, 3,
individual =>
{
List<Pair> PairList = gPairList.ToList(); ;
Simulate(PairList,Math.Abs(individual.Genotype[0] * 100),
Math.Abs(individual.Genotype[1] * 100),
Math.Abs(individual.Genotype[2] * 100),
0/* Math.Abs(individual.Genotype[3] * 100)*/,
(double)nudAWin.Value);
int AWCount = 0;
int ScanCount = 0;
int ScanMaxCount = 0;
Analyze(PairList,out AWCount, out ScanCount, out ScanMaxCount);
return ((Math.Pow(AWCount - ScanMaxCount, 2) / 4) + Math.Pow(AWCount - ScanCount, 2) + Math.Abs(individual.Genotype[2]) * 10 + Math.Abs(individual.Genotype[1]) * 100 + Math.Abs(individual.Genotype[0]) * 100 + (Math.Abs(individual.Genotype[1]) < Math.Abs(individual.Genotype[0]) ? 100 : 0));
},
individual =>
{
individual.Genotype[0] = 2 * rand.NextDouble();
individual.Genotype[1] = 2 * rand.NextDouble();
individual.Genotype[2] = 2 * rand.NextDouble();
});
backgroundWorker1.RunWorkerAsync(ga);
/* nudScanTime.Value=(decimal)Math.Abs(ga.BestIndividual.Genotype[0]*100);
nudMaskTime.Value = (decimal)Math.Abs(ga.BestIndividual.Genotype[1]*100);
nudThresold.Value = (decimal)Math.Abs(ga.BestIndividual.Genotype[2]*100);
nudRet.Value = 0;//(decimal)Math.Abs(ga.BestIndividual.Genotype[3] * 100);
*/
//double ScanTime = (double);
//double MaskTime = (double)
//double Thresold = (double)
}
private void btn_Run_Click(object sender, System.EventArgs e)
{
try
{
if (Points.Count < 2)
{
MessageBox.Show("No Enough cities");
return;
}
this.m_chro = null;
Refresh();
//�¼ӵIJ���
points.Clear();
temps.Clear();
DrawPoints();
GALib.Initializer newItializer = new GALib.Initializer(this.Initializer);
GALib.Mutate mutater = new GALib.Mutate(this. ChromoseCompraror);
GALib.Fitness fitmethod = new GALib.Fitness(this.Fitness);
GALib.CrossOver CrossMethod = new GALib.CrossOver(this.CrossOver);
GALib.GA GAAlgo = new GA(newItializer,fitmethod,mutater,CrossMethod);
GAAlgo.Generations = long.Parse(this.num_Gnr.Value.ToString());
GAAlgo.PopulationSize = ushort.Parse(this.num_PopSiz.Value.ToString());
GAAlgo.Mutation = double.Parse(this.num_Mutation.Value.ToString());
GAAlgo.CrossOver = double.Parse(this.numCO.Value.ToString());
if(Tool_MainBar.Buttons[1].Pushed)
{
GAAlgo.EnableLogging = true;
GAAlgo.LogFilePath = this.openFileDialog.FileName;
}
GAAlgo.Initialize();
while (!GAAlgo.IsDone())
GAAlgo.CreateNextGeneration();
m_chro = GAAlgo.GetBestChromosome();
DrawCitiesPath();
}
catch(System.FormatException exp)
{
MessageBox.Show("Please check your Input Parameters "+exp);
}
}
public GenomePair(string id, GA.Genome gen)
{
Id = id;
Genome = gen;
}
