//
//
//
public string GetDeviceInfo()
{
string dev;
//if (PlayerPrefs.HasKey(LoginManager.LOCAL_DEVICE_NO))
//{
// dev = PlayerPrefs.GetString(LoginManager.LOCAL_DEVICE_NO);
// return dev;
//}
dev = GA.GetDeviceInfo();
if (dev == null)
{
dev = SystemInfo.deviceUniqueIdentifier;
}
#if UNITY_IPHONE
//dev = _GetIDFA();
if (dev.Length >= 36)
{
dev = dev.Substring(0, 36);
}
//处理ios 6.0 出现的idfa获取为0的问题
if (dev == "00000000-0000-0000-0000-000000000000")
{
dev = SystemInfo.deviceUniqueIdentifier;
}
#endif
//PlayerPrefs.SetString(LoginManager.LOCAL_DEVICE_NO, dev);
return(dev);
}
public void Test2()
{
var now = new DateTime(2015, 2, 1);
StreamTextWriterFactory writerFactory = new StreamTextWriterFactory(new DayLogPathFactory());
TextLogger logger = new TextLogger(writerFactory, new LogDescriptor());
writerFactory.NowGetter = () => now;
logger.Write(new LogItem()
{
Time = now,
Message = "测试内容。",
Type = LogType.Info
});
System.Threading.Thread.Sleep(1001);
var path = GA.FullPath(LogFolder, "2015年02月", "01.log");
try
{
Assert.Equal(now.ToString("HH:mm:ss.ffff") + " [消息] 测试内容。\r\n", GA.IO.ShareReadAllText(path, Encoding.UTF8));
}
catch (UnauthorizedAccessException)
{
//....
}
}
// use a wind shield prop
public void UseWindShield()
{
if (GetComponent <WindController> ().IsWindMask()) // used one, do nothing
{
return;
}
if (CurThrowObject == null) // throwing, do nothing
{
return;
}
if (GetComponent <GoldController> ().WindShield <= 0) // 道具不够，显示商店，让玩家购买
{
GetComponent <GoldController>().ShowShopCanvas();
this.PauseGame();
return;
}
GetComponent <WindController> ().ChangeWindMask(true); // wind mask
GetComponent <GoldController> ().WindShield--; // change prop num
var ani = GameCanvas.transform.Find("UIWindSheildButton/-1").gameObject;
ani.SetActive(true);
GetComponent <AnimatorScript>().AddRising(ani, 0.5f, 1, new AnimatorDelegate(this.PropsCallback));
TouchHandler.PlayClickAudio(); // 播放按钮声音
// umeng
GA.Use("WindShield", 1, 15);
}
// use a basket prop
public void UseBasket()
{
if (CurThrowObject == null || CurThrowObject.tag == "Basket") // have a basket, do nothing
{
return;
}
if (GetComponent <GoldController> ().Basket <= 0) // 道具不够，显示商店，让玩家购买
{
GetComponent <GoldController>().ShowShopCanvas();
this.PauseGame();
return;
}
Destroy(CurThrowObject); // destroy current ball
this.GenerateBasket(Role.transform.position.x + 0.48f); // generate a new basket
GetComponent <GoldController> ().Basket--; // change prop num
var ani = GameCanvas.transform.Find("UIBasketButton/-1").gameObject;
ani.SetActive(true);
GetComponent <AnimatorScript>().AddRising(ani, 0.5f, 1, new AnimatorDelegate(this.PropsCallback));
TouchHandler.PlayClickAudio(); // 播放按钮声音
int guide = PlayerPrefs.GetInt(GameConsts.GUIDE_LOCAL, 0); // 需要做新手引导
if (guide == 0)
{
this.ShowGuideTxt(true);
}
// umeng
GA.Use("Basket", 1, 15);
}
void Start()
{
DontDestroyOnLoad(this.gameObject);
called = GameObject.Find("GAcarrier").GetComponent <GA> (); //manggil GA
//loading text
starttime = Time.time;
}
protected void Page_Load(object sender, EventArgs e)
{
if (!IsPostBack)
{
if (Session["GoodID"] == null)
{
Response.Redirect("GA_GoodExam.aspx");
}
else
{
string lGoodID = Session["GoodID"].ToString();
GA Gadmin = new GA();
GAController gadim = new GAController();
List <Good> GoodInfo = new List <Good>();
List <ImgInfo> GoodPictureInfo = new List <ImgInfo>();
GoodInfo = gadim.GoodInfo(Gadmin, lGoodID);
GoodPictureInfo = gadim.GoodPictureInfo(Gadmin, lGoodID);
tbxGoodName.Text = GoodInfo[0].GoodName;
tbxGoodPrice.Text = GoodInfo[0].GoodPrice;
tbxGoodIncontory.Text = GoodInfo[0].GoodIncentory.ToString();
tbxGoodSales.Text = GoodInfo[0].SalesVolume.ToString();
tbxGoodImgTitle.Text = GoodPictureInfo[0].ImgTitle;
tbxGoodImgUrl.Text = GoodPictureInfo[0].ImgAddress;
tbxGoodIncontory.Enabled = false;
tbxGoodSales.Enabled = false;
}
}
}
/// <summary>
/// Call each time a step finished
/// </summary>
/// <param name="ga"></param>
/// <param name="step"></param>
private void Ga_StepFinish(GA ga, int step)
{
if (IsDisposed)
{
return;
}
//Console.WriteLine("Step " + step);
Invoke((Action) delegate()
{
progressBar1.Value = 100 * step / ga.Generations;
label_progress.Text = string.Format("{0}/{1} {2}%", step, ga.Generations, progressBar1.Value);
// open a preview window show result
if (optResultsWindow == null || optResultsWindow.IsDisposed)
{
optResultsWindow = new OptimizationResultsWindow();
optResultsWindow.OnIndividualSelected += OptResultsWindow_OnIndividualSelected;
optResultsWindow.OnApplyIndividualClick += OptResultsWindow_OnApplyIndividualClick;
optResultsWindow.Show();
}
optResultsWindow.visualizeNonDominatedSet(ga.NonDominatedSet);
});
}
protected async Task MyOnNavigatedTo()
{
try
{
if (IsMainPage() &&
(AppConfig.AppUpdate == UpdateVersion.NotSet || AppConfig.AppUpdate == UpdateVersion.V1_4) &&
!popUpNewVersion.IsOpen)
{
this.IsEnabled = false;
ApplicationBar.IsVisible = false;
PopUpNewVersion();
popUpNewVersion.IsOpen = true;
}
}
catch (Exception ex)
{
GA.LogException(ex);
ClearPopUpError();
}
var updatedFeeds = await _feedManager.LoadAsync();
if (updatedFeeds != null && updatedFeeds.Count > 0)
{
var message = FeedHelper.BuildUpdateStatus(updatedFeeds);
var count = updatedFeeds.Count > AppConfig.MAX_NEW_UPDATE_MESSENGER_WAIT
? AppConfig.MAX_NEW_UPDATE_MESSENGER_WAIT
: updatedFeeds.Count;
Messenger.ShowToast(message, miliSecondsUntilHidden: count * 2000);
}
}
// Use this for initialization
void Start()
{
// GA.StartWithAppKeyAndReportPolicyAndChannelId ("5abaee4db27b0a487f000095", Analytics.ReportPolicy.BATCH, "App Store");
GA.Start();
GA.StartLevel("02");
print("我开启了友盟统计");
}
private void Start()
{
Application.runInBackground = true;
var sampleArea = GameObject.Find("SampleArea");
Area = sampleArea == null
? Camera.main.rect
: sampleArea.GetComponent <RectTransform>().rect;
var generationTextGO = GameObject.Find("CurrentInfo/Background/GenerationText");
if (generationTextGO != null)
{
var fitnessTextGO = GameObject.Find("CurrentInfo/Background/FitnessText");
m_generationText = generationTextGO.GetComponent <Text>();
m_fitnessText = fitnessTextGO.GetComponent <Text>();
m_previousGenerationText = GameObject.Find("PreviousInfo/Background/GenerationText").GetComponent <Text>();
m_previousFitnessText = GameObject.Find("PreviousInfo/Background/FitnessText").GetComponent <Text>();
m_previousGenerationText.text = string.Empty;
m_previousFitnessText.text = string.Empty;
}
if (m_generationText != null)
{
m_generationText.text = string.Empty;
m_fitnessText.text = string.Empty;
}
GA = CreateGA();
GA.GenerationRan += delegate {
m_previousBestFitness = GA.BestChromosome.Fitness.Value;
m_previousAverageFitness = GA.Population.CurrentGeneration.Chromosomes.Average(c => c.Fitness.Value);
Debug.Log($"Generation: {GA.GenerationsNumber} - Best: ${m_previousBestFitness} - Average: ${m_previousAverageFitness} - Population : {GA.Population.MaxSize} ");
if (ChromosomesCleanupEnabled)
{
foreach (var c in GA.Population.CurrentGeneration.Chromosomes)
{
c.Fitness = null;
}
}
};
StartSample();
m_gaThread = new Thread(() =>
{
try
{
Thread.Sleep(1000);
Debug.Log("Starting GA");
GA.Start();
}
catch (Exception ex)
{
Debug.LogError($"GA thread error: {ex.Message}");
}
});
m_gaThread.Start();
}
// Use this for initialization
void Start()
{
//JSONNode N = new Json
//N[1] = "Hello world";
//N[1] = "string";
//Debug.Log("JSON: " + N.ToString());
//设置Umeng Appkey
GA.StartWithAppKeyAndChannelId("551bc899fd98c55326000032", "App Store");
//调试时开启日志
GA.SetLogEnabled(true);
GA.SetLogEncryptEnabled(true);
GA.ProfileSignIn("fkdafjadklfjdklf");
GA.ProfileSignIn("jfkdajfdakfj", "app strore");
print("GA.ProfileSignOff();");
GA.ProfileSignOff();
}
/// <summary>
/// Main program.
/// </summary>
static void Main(string[] args)
{
// Get the selected fitness type.
IFitness myFitness = GetFitnessMethod();
// Genetic algorithm setup.
_ga = new GA(_crossoverRate, _mutationRate, 100, 10000000, _genomeSize);
// Get the target fitness for this method.
_targetFitness = myFitness.TargetFitness;
// Run the genetic algorithm and get the best brain.
string program = GAManager.Run(_ga, fitnessFunction, OnGeneration);
// Display the final program.
Console.WriteLine(program);
Console.WriteLine();
// Compile to executable.
BrainPlus.Compile(program, "output.exe", myFitness);
// Run the result for the user.
string result = myFitness.RunProgram(program);
Console.WriteLine(result);
Console.ReadKey();
}
public static void Main()
{
// Crossover = 80%
// Mutation = 5%
// Population size = 100
// Generations = 2000
// Genome size = 2
GA ga = new GA(0.8,0.05,100,2000,2);
ga.FitnessFunction = new GAFunction(theActualFunction);
//ga.FitnessFile = @"H:\fitness.csv";
ga.Elitism = true;
ga.Go();
double[] values;
double fitness;
ga.GetBest(out values, out fitness);
System.Console.WriteLine("Best ({0}):", fitness);
for (int i = 0 ; i < values.Length ; i++)
System.Console.WriteLine("{0} ", values[i]);
ga.GetWorst(out values, out fitness);
System.Console.WriteLine("\nWorst ({0}):", fitness);
for (int i = 0 ; i < values.Length ; i++)
System.Console.WriteLine("{0} ", values[i]);
}
void Start()
{
GA.Start();
//调试时开启日志 发布时设置为false
GA.SetLogEnabled(true);
}
public void SetLevel(int level)
{
if (FilterPlatform())
{
GA.SetUserLevel(level);
}
}
public void SetAccount()
{
if (FilterPlatform())
{
GA.ProfileSignIn(GA.GetDeviceInfo());
}
}
public void GAPay(double amount, GA.PaySource source, string item)
{
if (FilterPlatform())
{
GA.Pay(amount, source, item, 1, amount);
}
}
public void Run()
{
if (Graph.IsEulerian())
{
Console.WriteLine("Graf jest eulerowski, wiec algorytm genetyczny nie jest potrzebny");
}
else
{
var latestFitness = int.MinValue;
GA.GenerationRan += (sender, e) =>
{
var bestInd = GA.BestChromosome as CPChromosome;
var bestFitness = (int)-bestInd.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
Console.WriteLine("Generation {0}: {1}", GA.GenerationsNumber, bestFitness);
}
};
GA.Start();
var bestChromosome = GA.BestChromosome as CPChromosome;
var phenotype = bestChromosome.GetValues();
Graph.AddEdgesToGraph(phenotype);
}
PrintShortestPath();
}
public void Run()
{
GA Ga;
Ga = new GA();
Ga.EncodingType = EncodingType.Integer;
Ga.MinIntValue = -5000;
Ga.MaxIntValue = 5000;
Ga.ChromosomeLength = Coefficients.Length;
Ga.PopulationSize = Coefficients.Length * 10;
Ga.Objective = new ObjectiveDelegate( this.LinearDiophantineObjective );
Ga.ObjectiveType = ObjectiveType.MinimizeObjective;
Ga.Terminate += new TerminateEventHandler( new ObjectiveThresholdTerminator(0).Terminate );
Ga.Terminate += new TerminateEventHandler( new EvolutionTimeTerminator( new TimeSpan(0,0,20) ).Terminate );
Ga.NewPopulation += new NewPopulationEventHandler( OnNewPopulation_ShowSummary );
Ga.FitnessScaling = FitnessScaling.LinearRanked;
Ga.Run();
Console.WriteLine("Best Individual: (obj={0})", Ga.BestObjective);
PopulationSummary ps = new PopulationSummary( Ga, Ga.Population );
Console.WriteLine(ps.BestChromosome);
Console.WriteLine("Finished. Hit return.");
Console.ReadLine();
}
public static void Main()
{
// Crossover = 80%
// Mutation = 5%
// Population size = 100
// Generations = 2000
// Genome size = 2
GA ga = new GA(0.8,0.05,100,2000,2);
ga.FitnessFunction = new GAFunction(theActualFunction);
//ga.FitnessFile = @"H:\fitness.csv";
ga.Elitism = true;
ga.Go();
double[] values;
double fitness;
ga.GetBest(out values, out fitness);
System.Console.WriteLine("Best ({0}):", fitness);
for (int i = 0 ; i < values.Length ; i++)
System.Console.WriteLine("{0} ", values[i]);
ga.GetWorst(out values, out fitness);
System.Console.WriteLine("\nWorst ({0}):", fitness);
for (int i = 0 ; i < values.Length ; i++)
System.Console.WriteLine("{0} ", values[i]);
}
protected GA _ga; // Shared genetic algorithm instance
public FitnessBase(GA ga, int maxIterationCount)
{
_ga = ga;
_maxIterationCount = maxIterationCount;
Output = "";
Program = "";
}
private IList <CheckersMove> GetNewMoves()
{
InitializeGA();
var termination = new GenerationNumberTermination(GA.GenerationsNumber + m_generationNumber);
GA.Termination = termination;
Debug.Log("Running GA...");
if (GA.State == GeneticAlgorithmState.NotStarted)
{
GA.Start();
}
else
{
GA.Resume();
}
Debug.Log("Fitness: " + GA.BestChromosome.Fitness);
Debug.Log("Generations: " + GA.GenerationsNumber);
if (GA.BestChromosome.Fitness <= 0)
{
Debug.LogError("Lower than zero.");
}
if (GA.BestChromosome.Fitness == 0)
{
HudController.IsGameOver = true;
}
return((GA.BestChromosome as CheckersChromosome).Moves);
}
protected GA _ga; // Shared genetic algorithm instance
public FitnessBase(GA ga, int maxIterationCount)
{
_ga = ga;
_maxIterationCount = maxIterationCount;
Output = "";
Program = "";
}
protected void btnUpdate_Click(object sender, EventArgs e)
{
Good lGood = new Good();
ImgInfo lGoodinfo = new ImgInfo();
lGood.GoodName = tbxGoodName.Text;
lGood.GoodPrice = tbxGoodPrice.Text;
lGood.GoodID = Convert.ToInt32(Session["GoodID"]);
lGoodinfo.ImgAddress = tbxGoodImgUrl.Text;
lGoodinfo.ImgTitle = tbxGoodImgTitle.Text;
lGoodinfo.GoodID = Convert.ToInt32(Session["GoodID"]);
GA Gadmin = new GA();
GAController gadmin = new GAController();
if (tbxGoodImgTitle.Text == "" || tbxGoodImgUrl.Text == "" || tbxGoodName.Text == "" || tbxGoodPrice.Text == "")
{
lbShow.Text = "信息不能为空";
}
else
{
if (gadmin.UpdateGoodinfo(Gadmin, lGoodinfo, lGood))
{
lbShow.Text = "修改成功";
}
else
{
lbShow.Text = "修改失败";
}
}
}
public WWW RequestGameInfo(SubmitSuccessHandler successEvent, SubmitErrorHandler errorEvent)
{
string game_key = GA.SettingsGA.GameKey;
string requestInfo = "game_key=" + game_key + "&keys=area%7Cevent_id%7Cbuild";
requestInfo = requestInfo.Replace(" ", "%20");
//Get the url with the request type
string url = GetURL(Requests[RequestType.GA_GetHeatmapGameInfo]);
url += "/?" + requestInfo;
WWW www = null;
#if !UNITY_WP8 && !UNITY_METRO
//Set the authorization header to contain an MD5 hash of the JSON array string + the private key
Hashtable headers = new Hashtable();
headers.Add("Authorization", GA.API.Submit.CreateMD5Hash(requestInfo + GA.SettingsGA.ApiKey));
//Try to send the data
www = new WWW(url, new byte[] { 0 }, headers);
#else
//Set the authorization header to contain an MD5 hash of the JSON array string + the private key
Dictionary <string, string> headers = new Dictionary <string, string>();
headers.Add("Authorization", GA.API.Submit.CreateMD5Hash(requestInfo + GA.SettingsGA.ApiKey));
//Try to send the data
www = new WWW(url, new byte[] { 0 }, headers);
#endif
GA.RunCoroutine(Request(www, RequestType.GA_GetHeatmapGameInfo, successEvent, errorEvent), () => www.isDone);
return(www);
}
public List <AllocOutput> GetAllocationsGA(AllocInput allocInput)
{
List <AllocOutput> allocations = new List <AllocOutput>();
GA GAlgo = new GA(allocInput.Tasks, allocInput.Processors, allocInput.Coefficients, allocInput.RefFrequency, allocInput.MaxDuration);
GAlgo.Train();
Population pop = GAlgo.GetLastGeneration();
var uniqueCorrectAllocs = GAlgo.GetCorrectAllocs().GroupBy(elem => elem.GetUniqueId()).Select(group => group.First());
foreach (var alloc in uniqueCorrectAllocs)
{
Dictionary <string, List <string> > processors = new Dictionary <string, List <string> >();
foreach (var proc in alloc.Processors)
{
processors.Add(proc.Value.Id, proc.Value.Tasks);
}
AllocOutput allocOutput = new AllocOutput((allocations.Count + 1).ToString(), alloc.ProgramRuntime, alloc.EnergyConsumed, processors);
allocations.Add(allocOutput);
}
return(allocations);
}
public void Start()
{
random = new Random();
// Initialize ga instance
ga = new GA <char>(populationSize, targetString.Length, random, GetRandomCharacter, FitnessFunction, elitism, mutationRate);
}
/// <summary>
/// Submit any queued up data immediately. Useful when you want to control when data is submitted.
/// </summary>
public static void ForceSubmit()
{
GA_SpecialEvents.SubmitAverageFPS();
//If we have internet connection then add any archived data to the submit queue
if (GA.SettingsGA.ArchiveData && GA.SettingsGA.InternetConnectivity)
{
List <GA_Submit.Item> archivedItems = GA.API.Archive.GetArchivedData();
if (archivedItems != null && archivedItems.Count > 0)
{
foreach (GA_Submit.Item item in archivedItems)
{
GA_Queue.AddItem(item.Parameters, item.Type, false);
}
if (GA.SettingsGA.DebugMode)
{
GA.Log("GA: Network connection detected. Adding archived data to next submit queue.");
}
}
}
//If we have something to submit and we have not stopped submitting completely then we start submitting data
if (_queue.Count > 0 && !_submittingData && !_endsubmit)
{
_submittingData = true;
GA.Log("GameAnalytics: Queue submit started");
GA.API.Submit.SubmitQueue(_queue, Submitted, SubmitError, false, string.Empty, string.Empty);
}
}
public static IntPtr GetAncestor(IHandle hwnd, GA flags)
{
IntPtr result = GetAncestor(hwnd.Handle, flags);
GC.KeepAlive(hwnd);
return(result);
}
void Start()
{
GA.StartWithAppKeyAndChannelId("59892f08310c9307b60023d0", "umeng");
GA.SetLogEnabled(Debug.isDebugBuild);
}
protected void btnDownPage_Click(object sender, EventArgs e)
{
int lCurrentPage = Convert.ToInt32(ViewState["CurrentPage"]);
for (int i = 0; i < cblistUpdate.Items.Count; i++)//读取CheckBoxList 选中的值,保存起来
{
if (cblistUpdate.Items[i].Selected)
{
info[(lCurrentPage * 10) + i] = cblistUpdate.Items[i].Value;
}
}
lCurrentPage++;
if (lCurrentPage + 1 == Convert.ToInt32(ViewState["PageCount"]))
{
btnDownPage.Enabled = false;
}
GA Gadmin = new GA();
GAController gadmin = new GAController();
FirstClassDm firstClassName = new FirstClassDm();
firstClassName.FirstClassDmName = "服装";
string lfirstClassDmID = Gadmin.FcNameGetID(firstClassName);
List <ImgInfo> Furniture = new List <ImgInfo>();
Furniture = gadmin.HomeNextPage(Gadmin, lCurrentPage);
cblistUpdate.DataSource = Furniture;
cblistUpdate.DataTextField = "ImgTitle";
cblistUpdate.DataValueField = "GoodID";
cblistUpdate.DataBind();
dlistPictureShow.DataSource = Furniture;
dlistPictureShow.DataBind();
btnUpPage.Enabled = true;
ViewState["CurrentPage"] = lCurrentPage;
lbPage.Text = "第" + (lCurrentPage + 1) + "页/共 " + ViewState["PageCount"].ToString() + "页";
}
protected void btnSubmit_Click(object sender, EventArgs e)
{
int lCurrentPage = Convert.ToInt32(ViewState["CurrentPage"]);
for (int i = 0; i < cblistUpdate.Items.Count; i++)//读取CheckBoxList 选中的值,保存起来
{
if (cblistUpdate.Items[i].Selected)
{
info[(lCurrentPage * 10) + i] = cblistUpdate.Items[i].Value;
}
}
GA Gadmin = new GA();
GAController gadmin = new GAController();
if (gadmin.UpdateInterface(Gadmin, info, 1))
{
lbPrompt.Text = "修改成功";
}
if (gadmin.UpdatePictureShow(Gadmin, 1).Count > 1)
{
drpdownlist.DataSource = gadmin.UpdatePictureShow(Gadmin, 1);
drpdownlist.DataTextField = "ImgTitle";
drpdownlist.DataValueField = "GoodID";
drpdownlist.DataBind();
drpdownlist.Items.Insert(0, new ListItem("请选择替换商品"));
}
}
private void button2_Click(object sender, EventArgs e)
{
if (seed == null) return;
int sampleSize = (int)SampSize.Value;
int mutate = (int)MapDivrg.Value;
List<List<List<Stat>>> stats = new List<List<List<Stat>>>();
for (int j = 0; j < sampleSize; j++)
{
stats.Add(new List<List<Stat>>());
for (int i = 0; i < mutate; i++)
{
Population pop = new Population(seed);
landscape map = new landscape(seed.home);
map.mutate(i + 1);
pop.setLandscape(map);
GA tempGA = new GA(pop);
stats[j].Add(tempGA.evolve());
}
}
string output = "";
for (int i = 0; i < stats[0].Count; i++){
for (int j = 0; j < sampleSize; j++)
{
output += stats[j][i].Count + ",";
}
output += Environment.NewLine;
}
fit.Text = output;
}
public LogicalXorFitness(GA ga, int maxIterationCount, string appendFunctions = null)
: base(ga, maxIterationCount, appendFunctions)
{
if (_targetFitness == 0)
{
_targetFitness = _trainingResults.Length * 256;
}
}
public LogicalXorFitness(GA ga, int maxIterationCount, string appendFunctions = null)
: base(ga, maxIterationCount, appendFunctions)
{
if (_targetFitness == 0)
{
_targetFitness = _trainingResults.Length * 256;
}
}
public MultiplyFitness(GA ga, int maxIterationCount, int maxTrainingCount = 4)
: base(ga, maxIterationCount)
{
_trainingCount = maxTrainingCount;
if (_targetFitness == 0)
{
_targetFitness = _trainingCount * 256;
}
}
public StringOptimizedFitness(GA ga, int maxIterationCount, string targetString)
: base(ga, maxIterationCount)
{
_targetString = targetString;
if (_targetFitness == 0)
{
_targetFitness = _targetString.Length * 256;
}
}
public LessThanEqualToFitness(GA ga, int maxIterationCount, int maxTrainingCount = 5)
: base(ga, maxIterationCount)
{
_trainingCount = maxTrainingCount;
if (_targetFitness == 0)
{
_targetFitness = _trainingCount * 256;
}
}
public AddToCharFitness(GA ga, int maxIterationCount, int maxTrainingCount = 5)
: base(ga, maxIterationCount)
{
_trainingCount = maxTrainingCount;
if (_targetFitness == 0)
{
_targetFitness = _trainingCount * 256;
}
}
public TimesThreeFitness(GA ga, int maxIterationCount, int maxTrainingCount = 3, string appendFunctions = null)
: base(ga, maxIterationCount, appendFunctions)
{
_trainingCount = maxTrainingCount;
if (_targetFitness == 0)
{
_targetFitness = _trainingCount * 256;
_functionCount = CommonManager.GetFunctionCount(appendFunctions);
}
}
private void Test() {
var e = new UserEvent ();
var ga = new GA ();
ga.GOID = "tank1";
e.sponsorId = "0";
e.targetIdList = World.GetInstance ().GetAllGOIds ();
e.type = UserEvent.EventType.GA;
e.rawContent = ga as object;
base.BroadCastEvent (e);
}
public StringStrictFitness(GA ga, int maxIterationCount, string targetString, string appendFunctions = null)
: base(ga, maxIterationCount, appendFunctions)
{
_targetString = targetString;
if (_targetFitness == 0)
{
_targetFitness = _targetString.Length * 256;
_targetFitness += 10;
}
}
void Test() {
var e = new UserEvent ();
var ga = new GA ();
ga.AssetName = "cube";
ga.GOID = "cube1";
e.rawContent = ga as object;
e.sponsorId = "0";
e.targetIdList = World.GetInstance ().GetAllGOIds ();
e.type = UserEvent.EventType.GA;
EventCollection.OnEventTrigger (e);
}
public ReverseStringFitness(GA ga, int maxIterationCount, int maxTrainingCount = 5)
: base(ga, maxIterationCount)
{
_trainingCount = maxTrainingCount;
if (_targetFitness == 0)
{
for (int i=0; i<_trainingCount; i++)
{
_targetFitness += (i + 1) * 256;
}
}
}
public FibonacciFitness(GA ga, int maxIterationCount, int maxDigits = 4, int maxTrainingCount = 3, string appendFunctions = null)
: base(ga, maxIterationCount, appendFunctions)
{
_maxDigits = maxDigits;
_trainingCount = maxTrainingCount;
if (_targetFitness == 0)
{
_targetFitness = _trainingCount * 256 * _maxDigits;
_functionCount = CommonManager.GetFunctionCount(appendFunctions);
}
}
public BottlesOfBeerFitness(GA ga, int maxIterationCount, string appendFunctions = null)
: base(ga, maxIterationCount, appendFunctions)
{
if (_targetFitness == 0)
{
// Number of numeric values in training example * 256 + number of characters in target string * 256 * number of numeric values (since string is next to each number).
foreach (byte[] trainingExample in _trainingExamples)
{
_targetFitness += (trainingExample.Length * 256) + (_targetString.Length * 256 * trainingExample.Length);
}
}
}
public IfThenFitness(GA ga, int maxIterationCount, int maxTrainingCount = 3)
: base(ga, maxIterationCount)
{
_trainingCount = maxTrainingCount;
if (_targetFitness == 0)
{
for (int i = 0; i < maxTrainingCount; i++)
{
_targetFitness += 256 * _trainingStrings[i].Length;
}
}
}
public HelloUserFitness(GA ga, int maxIterationCount, string targetString, int maxTrainingCount = 4)
: base(ga, maxIterationCount)
{
_targetString = targetString;
_trainingCount = maxTrainingCount;
if (_targetFitness == 0)
{
for (int i=0; i<_trainingCount; i++)
{
_targetFitness += (_targetString.Length + (i + 1)) * 256;
}
}
}
public CountDownFitness(GA ga, int maxIterationCount, int startingNumber)
: base(ga, maxIterationCount)
{
_startingNumber = startingNumber;
if (_targetFitness == 0)
{
for (int i = 1; i <= startingNumber; i++)
{
_targetFitness += 256 * (i.ToString().Length + 1); // +1 for newline
_targetFitness += i.ToString().Length + 1; // match length
}
}
}
public GuidingFunctionFitness(GA ga, int maxIterationCount, string appendFunctions = null)
: base(ga, maxIterationCount, appendFunctions)
{
if (_targetFitness == 0)
{
for (int i = 0; i < _trainingExamples.Length; i++)
{
_trainingResults[i] = _trainingExamples[i].Replace(">", "").Replace("<", "");
_targetFitness += _trainingResults[i].Length * 256;
_targetFitness += 10; // length fitness
}
}
}
public CountdownFitness(GA ga, int maxIterationCount, int maxTrainingCount = 4)
: base(ga, maxIterationCount)
{
_trainingCount = maxTrainingCount;
if (_targetFitness == 0)
{
// A * 256 => A = Number of numeric values in training example, 256 = 256 characters per byte.
_targetFitness += 6 * 256;
_targetFitness += 4 * 256;
_targetFitness += 3 * 256;
_targetFitness += 7 * 256;
}
}
private void run_Click_1(object sender, EventArgs e)
{
int time;
landscape env = new landscape((int)CityCount.Value);
Population pop = new Population((int)setPopSize.Value, env);
ga = new GA(pop);
ga.mutationFactor = (int)setMutFact.Value;
seed = ga.pop;
time = ga.evolve().Count;
fit.Text = "" + time +'\n';
}
public NumberGuessFitness(GA ga, int maxIterationCount, int maxTrainingCount = 1)
: base(ga, maxIterationCount)
{
_trainingCount = maxTrainingCount;
if (_targetFitness == 0)
{
for (int i = 0; i < maxTrainingCount; i++)
{
_targetFitness += _trainingLength * 256;
}
//_targetFitness += 7 * 256; // ou Win! (minus the Y which is already accounted for in the above loop).
}
}
public XmlToJsonFitness(GA ga, int maxIterationCount, string appendFunctions = null)
: base(ga, maxIterationCount, appendFunctions)
{
if (_targetFitness == 0)
{
for (int i = 0; i < _trainingExamples.Length; i++)
{
XmlDocument doc = new XmlDocument();
doc.LoadXml(_trainingExamples[i]);
string json = JsonConvert.SerializeXmlNode(doc);
_trainingResults[i] = json;
_targetFitness += json.Length * 256;
}
}
}
private void button1_Click(object sender, EventArgs e)
{
lblStatus.Text = "Procurando melhor solução...";
Application.DoEvents();
// Instancianto GA
GA ga = new GA(
Convert.ToInt32(txtTipoA.Text),
Convert.ToInt32(txtTipoB.Text),
Convert.ToInt32(txtTipoC.Text)
);
// Chamando a função que desenha as cargas
pictureBox1.Image = GUI.DesenhaCarga(ga.FindSolution());
lblStatus.Text = "Melhor solução encontrada.";
}
public CsvSplitFitness(GA ga, int maxIterationCount, int length, string appendFunctions = null)
: base(ga, maxIterationCount, appendFunctions)
{
_length = length;
if (_targetFitness == 0)
{
for (int i = 0; i < _trainingExamples.Length; i++)
{
string csvString = string.Join(",", SplitInParts(_trainingExamples[i], _length));
_trainingResults[i] = csvString;
_targetFitness += csvString.Length * 256;
}
}
}
public World(ref Canvas aCanvas)
{
mCanvas = aCanvas;
worldView = new WorldView();
createFishes();
createBaits();
int numGenes = mFishes[0].Brain.GetWeights().Count;
mGA= new GA(Config.NumFishes, numGenes,
Config.NumElites,
Config.MutationRate,Config.CrossoverRate, Config.Perturbation);
//mCanvas.Children.Add(worldView);
mCanvas.Children.Insert(0, worldView);
}
/// <summary>
/// Event handler that is called upon each generation. We use this opportunity to display some status info and save the current genetic algorithm in case of crashes etc.
/// </summary>
private static void OnGeneration(GA ga)
{
if (_bestStatus.Iteration++ > 1000)
{
_bestStatus.Iteration = 0;
Console.WriteLine("Best Fitness: " + _bestStatus.TrueFitness + "/" + _targetParams.TargetFitness + " " + Math.Round(_bestStatus.TrueFitness / _targetParams.TargetFitness * 100, 2) + "%, Ticks: " + _bestStatus.Ticks + ", Running: " + Math.Round((DateTime.Now - _startTime).TotalMinutes) + "m, Size: " + _genomeSize + ", Best Output: " + _bestStatus.Output + ", Changed: " + _bestStatus.LastChangeDate.ToString() + ", Program: " + _bestStatus.Program);
ga.Save("my-genetic-algorithm.dat");
}
if (_expandAmount > 0 && ga.GAParams.CurrentGeneration > 0 && ga.GAParams.CurrentGeneration % _expandRate == 0 && _genomeSize < _maxGenomeSize)
{
_genomeSize += _expandAmount;
ga.GAParams.GenomeSize = _genomeSize;
_bestStatus.Fitness = 0; // Update display of best program, since genome has changed and we have a better/worse new best fitness.
}
}
static void Main()
{
//
// Create the genetic algorithm component, and set the required
// properties.
//
GA ga = new GA();
ga.EncodingType = EncodingType.Binary;
ga.ChromosomeLength = 10;
ga.PopulationSize = 50;
ga.MaxGenerations = 10;
//
// Set the objective function for the run, which provides feedback
// to the algorithm as to the relative merit of candidate solutions.
//
ga.Objective = new ObjectiveDelegate( BinaryAlternateObjective );
//
// Set a "NewPopulation" event handler so that we get a callback on
// each new generation. During the callback, we print out the
// generation's statistics and the best chromosome found so far.
//
ga.NewPopulation += new NewPopulationEventHandler( OnNewPopulation_ShowSummary );
//
// Run the algorithm. It will stop after ga.MaxGenerations have elapsed.
//
ga.Run();
//
// Output the best individual that was found during the run.
//
Console.WriteLine("Best Individual:");
PopulationSummary ps = new PopulationSummary( ga, ga.Population );
Console.WriteLine(ps.BestChromosome);
Console.WriteLine("Finished. Hit return.");
Console.ReadLine();
}
static void Main()
{
//
// Describe the coding of our problem as an array of 10 integers
// in the range [-10,10].
//
GA ga = new GA();
ga.ChromosomeLength = 10;
ga.PopulationSize = 100;
ga.MaxGenerations = 100;
ga.EncodingType = EncodingType.Integer;
ga.MinIntValue = -10;
ga.MaxIntValue = 10;
ga.Objective = new ObjectiveDelegate( IntegerMaximizationObjective );
ga.MutationOperator = MutationOperator.GeneSpecific;
//
// Set a "NewPopulation" event handler so that we get a callback on
// each new generation. During the callback, we print out the
// generation's statistics and the best chromosome found so far.
//
ga.NewPopulation += new NewPopulationEventHandler( OnNewPopulation_ShowSummary );
//
// Let it run until default termination criteria are met, or
// MaxGenerations has elapsed.
//
ga.Run();
//
// Output the best individual that was found during the run.
//
Console.WriteLine("Best Individual:");
PopulationSummary ps = new PopulationSummary( ga, ga.Population );
Console.WriteLine( ps.BestChromosome );
Console.WriteLine("Finished. Hit return.");
Console.ReadLine();
}
