public BuildShelterActivity(Person actor, NeedEntry needEntry, EngineSettings settings, ConsoleHelper console)
: base(actor, needEntry, settings, console)
{
// TODO
//WoodFelling = new FellWoodActivity ();
//TimberMilling = new MillTimberActivity ();
}
public BaseActivity(Person actor, NeedEntry needEntry, EngineSettings settings, ConsoleHelper console)
{
Actor = actor;
NeedEntry = needEntry;
Settings = settings;
Console = console;
InitializeItemsProduced ();
InitializeItemsConsumed ();
}
static void Main(string[] args)
{
//STEP 1: Download dataset
DownloadDataset(originalDataDirectoryPath);
//Step 2: Prepare data by adding second column with value total number of features.
PrepareDataset(originalDataPath, preparedDataPath);
MLContext mlContext = new MLContext();
//STEP 3: Common data loading configuration
var fullDataView = mlContext.Data.LoadFromTextFile <UrlData>(path: Path.Combine(preparedDataPath, "*"),
hasHeader: false,
allowSparse: true);
//Step 4: Divide the whole dataset into 80% training and 20% testing data.
TrainTestData trainTestData = mlContext.Data.TrainTestSplit(fullDataView, testFraction: 0.2, seed: 1);
IDataView trainDataView = trainTestData.TrainSet;
IDataView testDataView = trainTestData.TestSet;
//Step 5: Map label value from string to bool
var UrlLabelMap = new Dictionary <string, bool>();
UrlLabelMap["+1"] = true; //Malicious url
UrlLabelMap["-1"] = false; //Benign
var dataProcessingPipeLine = mlContext.Transforms.Conversion.MapValue("LabelKey", UrlLabelMap, "LabelColumn");
ConsoleHelper.PeekDataViewInConsole(mlContext, trainDataView, dataProcessingPipeLine, 2);
//Step 6: Append trainer to pipeline
var trainingPipeLine = dataProcessingPipeLine.Append(
mlContext.BinaryClassification.Trainers.FieldAwareFactorizationMachine(labelColumnName: "LabelKey", featureColumnName: "FeatureVector"));
//Step 7: Train the model
Console.WriteLine("====Training the model=====");
var mlModel = trainingPipeLine.Fit(trainDataView);
Console.WriteLine("====Completed Training the model=====");
Console.WriteLine("");
//Step 8: Evaluate the model
Console.WriteLine("====Evaluating the model=====");
var predictions = mlModel.Transform(testDataView);
var metrics = mlContext.BinaryClassification.Evaluate(data: predictions, labelColumnName: "LabelKey", scoreColumnName: "Score");
ConsoleHelper.PrintBinaryClassificationMetrics(mlModel.ToString(), metrics);
// Try a single prediction
Console.WriteLine("====Predicting sample data=====");
var predEngine = mlContext.Model.CreatePredictionEngine <UrlData, UrlPrediction>(mlModel);
// Create sample data to do a single prediction with it
var sampleDatas = CreateSingleDataSample(mlContext, trainDataView);
foreach (var sampleData in sampleDatas)
{
UrlPrediction predictionResult = predEngine.Predict(sampleData);
Console.WriteLine($"Single Prediction --> Actual value: {sampleData.LabelColumn} | Predicted value: {predictionResult.Prediction}");
}
Console.WriteLine("====End of Process..Press any key to exit====");
Console.ReadLine();
}
static async Task Main()
{
Console.Title = "Samples.ServiceControl.RabbitMQAdapter.Shipping";
#region ShippingConfiguration
var endpointConfiguration = new EndpointConfiguration("Samples.ServiceControl.RabbitMQAdapter.Shipping");
var transport = endpointConfiguration.UseTransport <RabbitMQTransport>();
transport.ConnectionString("host=localhost");
transport.DelayedDelivery().DisableTimeoutManager();
transport.UseDirectRoutingTopology();
endpointConfiguration.SendFailedMessagesTo("adapter_error");
endpointConfiguration.AuditProcessedMessagesTo("adapter_audit");
endpointConfiguration.SendHeartbeatTo("adapter_Particular.ServiceControl");
#endregion
endpointConfiguration.UsePersistence <InMemoryPersistence>();
var chaos = new ChaosGenerator();
endpointConfiguration.RegisterComponents(
registration: components =>
{
components.ConfigureComponent(() => chaos, DependencyLifecycle.SingleInstance);
});
var recoverability = endpointConfiguration.Recoverability();
recoverability.Immediate(
customizations: immediate =>
{
immediate.NumberOfRetries(0);
});
recoverability.Delayed(
customizations: delayed =>
{
delayed.NumberOfRetries(0);
});
recoverability.DisableLegacyRetriesSatellite();
endpointConfiguration.EnableInstallers();
var endpointInstance = await Endpoint.Start(endpointConfiguration)
.ConfigureAwait(false);
Console.WriteLine("Press enter to exit");
Console.WriteLine("Press 'f' to toggle simulating of message processing failure");
while (true)
{
var key = Console.ReadKey();
Console.WriteLine();
if (key.Key == ConsoleKey.Enter)
{
break;
}
var lowerInvariant = char.ToLowerInvariant(key.KeyChar);
if (lowerInvariant == 'f')
{
chaos.IsFailing = !chaos.IsFailing;
Console.WriteLine($"Failure simulation is now turned {(chaos.IsFailing ? "on" : "off")}");
ConsoleHelper.ToggleTitle();
}
}
await endpointInstance.Stop()
.ConfigureAwait(false);
}
private void Afficher()
{
ConsoleHelper.AfficherEntete("Afficher les Participants");
this.liste = MethodesParticipant.GetParticipants();
ConsoleHelper.AfficherListe(this.liste, strategieAffichageParticipants);
}
private static void Client_OnPrivateMessage(PrivateMessage msg)
{
ConsoleHelper.WriteLine("收到私信：{0}", msg.Content);
}
public DrinkWaterNeedIdentifier(EngineSettings settings, ConsoleHelper console)
: base(ActivityVerb.Drink, ItemType.Water, PersonVitalType.Thirst, settings, console)
{
}
public PersonEnergyEffect(EngineSettings settings, ConsoleHelper console)
: base(settings, console)
{
}
public override int Execute(string[] args)
{
// sanity check
if (args.Length != 2)
{
throw new ArgumentException("incorrect number of arguments");
}
Progress.Message = "Searching";
string filePattern = args[1];
string root = Directory.GetCurrentDirectory();
IO.CheckRootAndPattern(ref root, ref filePattern);
List <string> files = IO.GetAllFiles(root, filePattern);
ConsoleHelper.DebugWriteLine("pattern: " + filePattern);
ConsoleHelper.DebugWriteLine("root: " + root);
if (files.Count == 0)
{
throw new FileNotFoundException("unable to find any files that match pattern: \"" + filePattern + "\" in root: \"" + root + "\"");
}
int counter = 0;
foreach (string file in files)
{
try
{
Progress.Message = "ReIndenting " + IO.GetFilename(file);
switch (IO.GetFileExtension(file).ToLower())
{
case "vb":
counter++;
var lines = IO.ReadFileLines(file);
var newlines = new List <string>();
int level = 0;
bool linecontinue = false;
//bool levelup = false;
bool meta = false;
bool metaContinue = false;
int linenumber = 0;
int lastIndentLevel = 0;
foreach (string line in lines)
{
linenumber++;
string newline = line.TrimWhiteSpace();
// clean string
string temp = StringHelper.Remove(line, new List <string> {
"Private", "Protected", "Public", "Shared", "Overridable", "Overrides", "Overloads", "Friend", "ReadOnly", "WriteOnly", "Partial", "Shadows", "Default", "NotInheritable"
});
temp = temp.TrimWhiteSpace();
// trim meta tags
meta = false;
if (temp.StartsWith("<"))
{
if (temp.Contains(">"))
{
meta = true;
temp = temp.Substring(temp.IndexOf('>') + 1);
}
else if (temp.EndsWith("_"))
{
temp = temp.Substring(temp.IndexOf('_'));
metaContinue = false;
}
else
{
ConsoleHelper.DebugWriteLine("invalid meta tag in code : " + file);
}
}
else if (metaContinue)
{
if (temp.Contains(">"))
{
meta = true;
metaContinue = false;
temp = temp.Substring(temp.IndexOf('>') + 1);
}
else
{
temp = "";
}
}
// trim comments
if (temp.Contains("'"))
{
temp = temp.Substring(0, temp.IndexOf('\''));
}
temp = temp.TrimWhiteSpace();
string firstword = StringHelper.FirstWord(temp);
string lastword = StringHelper.LastWord(temp);
if (!string.IsNullOrEmpty(firstword) && firstword[0] != '\'')
{
// ******************** Level Down Before Print *************************** //
// single level
if (StringHelper.StartsWith(temp, new List <string>()
{
"Loop", "Next", "End", "End If", "ElseIf", "#End If", "#ElseIf", "#Else", "Catch", "Finally", "End Try", "End Select", "End Sub", "End Function", "End Enum", "Case"
}) ||
(firstword == "Else" && !temp.StartsWith("Else :")))
{
level--;
}
// double level
if (StringHelper.StartsWith(temp, new List <string>()
{
"End Select"
}))
{
level--;
}
// ******************** correction for line continuation *************************** //
int correction = level;
//correction -= ((linecontinue) ? 1 : 0);
//correction -= ((linecontinue & !levelup) ? 1 : 0);
if (level + correction < 0)
{
if (temp != "#End Region")
{
ConsoleHelper.DebugWriteLine("invalid vb code : " + IO.GetFilename(file));
//ConsoleHelper.DebugWriteLine("file: " + file);
ConsoleHelper.DebugWriteLine("temp: " + temp);
ConsoleHelper.DebugWriteLine("line: " + linenumber.ToString());
}
}
// ******************** Print Line *************************** //
lastIndentLevel = correction;
if (correction < 0)
{
correction = 0;
}
newlines.Add(new String('\t', correction) + (linecontinue ? new String(' ', 2) : "") + newline);
linecontinue = StringHelper.EndsWith(temp, new List <string>()
{
"_"
}) & !meta;
// ******************** Level Up after line *************************** //
if (StringHelper.EndsWith(temp, new List <string>()
{
"Then", "Else", "#ElseIf", "#Else"
}) ||
StringHelper.StartsWith(temp, new List <string>()
{
"Namespace", "Class", "Structure", "Function", "Property", "Operator", "Enum", "Sub", "Module", "SyncLock", "Select Case", "Case", "For", "For Each", "Do", "Do While", "While", "Try", "Catch", "Finally", "With", "Custom Event"
}) ||
temp.StartsWith("AddHandler(") || temp.StartsWith("RemoveHandler(") || temp.StartsWith("RaiseEvent(") ||
(firstword == "Get") || (firstword == "Set"))
{
level++;
//levelup = true;
}
else
{
//if (!linecontinue) levelup = false;
}
// double level
if (StringHelper.StartsWith(temp, new List <string>()
{
"Select Case"
}))
{
level++;
}
}
else
{
if (newline.StartsWith("'"))
{
if (level < 0)
{
level = 0;
}
newlines.Add(new String('\t', level) + newline);
}
else
{
newlines.Add(newline);
}
}
}
if (lastIndentLevel != 0)
{
IO.WriteFileLines(file + ".error", newlines);
throw new Exception("failed to indent " + IO.GetFilename(file));
}
IO.WriteFileLines(file, newlines);
break;
default:
break;
}
}
catch (Exception ex)
{
ConsoleHelper.WriteException(ex);
}
}
ConsoleHelper.WriteLine(counter.ToString() + " File" + ((counter != 1) ? "s" : "") + " Checked", ConsoleColor.DarkYellow);
return(ConsoleHelper.EXIT_OK);
}
public override void Execute(List <string> _params, CommandSenderInfo _senderInfo)
{
try
{
if (_params.Count < 1 && _params.Count > 3)
{
SdtdConsole.Instance.Output(string.Format("Wrong number of arguments, expected 1 to 3, found {0}", _params.Count));
return;
}
if (_params[0].ToLower().Equals("off"))
{
if (Bounties.IsEnabled)
{
Bounties.IsEnabled = false;
LoadConfig.WriteXml();
SdtdConsole.Instance.Output(string.Format("Bounties has been set to off"));
return;
}
else
{
SdtdConsole.Instance.Output(string.Format("Bounties is already off"));
return;
}
}
else if (_params[0].ToLower().Equals("on"))
{
if (!Bounties.IsEnabled)
{
Bounties.IsEnabled = true;
LoadConfig.WriteXml();
SdtdConsole.Instance.Output(string.Format("Bounties has been set to on"));
return;
}
else
{
SdtdConsole.Instance.Output(string.Format("Bounties is already on"));
return;
}
}
else if (_params[0].ToLower().Equals("edit"))
{
if (_params.Count != 3)
{
SdtdConsole.Instance.Output(string.Format("Wrong number of arguments, expected 3, found {0}", _params.Count));
return;
}
int _value;
if (!int.TryParse(_params[2], out _value))
{
SdtdConsole.Instance.Output(string.Format("Must input a valid interger: {0}", _params[2]));
return;
}
ClientInfo _cInfo = ConsoleHelper.ParseParamIdOrName(_params[1]);
if (_cInfo != null)
{
Bounties.ConsoleEdit(_cInfo.playerId, _value);
}
else
{
SdtdConsole.Instance.Output(string.Format("No player found online with id or name: {0}. Checking steam id", _params[1]));
if (_params[1].Length != 17)
{
SdtdConsole.Instance.Output(string.Format("Can not edit: Invalid steam id {0}", _params[1]));
return;
}
Bounties.ConsoleEdit(_params[1], _value);
}
}
else if (_params[0].ToLower().Equals("remove"))
{
if (_params.Count != 2)
{
SdtdConsole.Instance.Output(string.Format("Wrong number of arguments, expected 2, found {0}", _params.Count));
return;
}
ClientInfo _cInfo = ConsoleHelper.ParseParamIdOrName(_params[1]);
if (_cInfo != null)
{
Bounties.ConsoleRemoveBounty(_cInfo.playerId);
}
else
{
SdtdConsole.Instance.Output(string.Format("No player found online with id or name: {0}. Checking steam id", _params[1]));
if (_params[1].Length != 17)
{
SdtdConsole.Instance.Output(string.Format("Can not remove: Invalid steam id {0}", _params[1]));
return;
}
Bounties.ConsoleRemoveBounty(_params[1]);
}
}
else if (_params[0].ToLower().Equals("list"))
{
if (_params.Count != 1)
{
SdtdConsole.Instance.Output(string.Format("Wrong number of arguments, expected 1, found {0}", _params.Count));
return;
}
Bounties.ConsoleBountyList();
}
else
{
SdtdConsole.Instance.Output(string.Format("Invalid argument {0}", _params[0]));
}
}
catch (Exception e)
{
Log.Out(string.Format("[SERVERTOOLS] Error in BountiesConsole.Execute: {0}", e.Message));
}
}
public ThirstEffect(EngineSettings settings, ConsoleHelper console)
: base(settings, console)
{
}
public CollectWaterActivity(Person actor, NeedEntry needEntry, EngineSettings settings, ConsoleHelper console)
: base(actor, needEntry, settings, console)
{
}
public ActivityCreator(EngineSettings settings, ConsoleHelper console)
{
Settings = settings;
Console = console;
}
/// <summary>
/// Displays a menu of options after each post
/// until the user exits the loop.
/// </summary>
public void LoopDisplay()
{
if (Posts.Count == 0)
{
VisiblePostIndex = 0;
}
else
{
for (VisiblePostIndex = 0; VisiblePostIndex <= Posts.Count; VisiblePostIndex++)
{
ExitLoop = false;
//Console.Clear();
RepeatFinalPost();
if (VisiblePost < Posts.Count - 1)
{
ShowPost();
}
ShowRedAlert();
ShowBlueAlert();
ResetAlertValues();
Console.WriteLine();
string[] choices = new string[]
{
" [1] Like ", " [3] Remove this post", " [5] Comment \n",
" [2] Unlike ", "[4] Remove all posts ", " [6] Next post ",
"[7] Main Menu"
};
int choice = ConsoleHelper.SelectChoice2(choices);
switch (choice)
{
case 1:
LikePost(Posts[VisiblePostIndex]);
VisiblePostIndex--;
break;
case 2:
UnlikePost(Posts[VisiblePostIndex]);
VisiblePostIndex--;
break;
case 3:
RemovePost();
VisiblePostIndex--;
break;
case 4:
RemoveAllPosts();
break;
case 5:
AddComment(Posts[VisiblePostIndex]);
VisiblePostIndex--;
break;
case 6:
ShowNextPost();
break;
case 7:
Console.Clear();
ExitLoop = true;
break;
default:
break;
}
if (ExitLoop && NoPosts)
{
VisiblePost = 0;
BlueAlert = "\n -- All posts removed --\n";
Console.WriteLine();
break;
}
else
{
if (ExitLoop)
{
VisiblePost = 0;
Console.WriteLine();
break;
}
}
}
}
}
public static void Main(String[] args)
{
ConsoleHelper.SetConsoleFont(5);
Calculating();
}
public StarvationEffect(EngineSettings settings, ConsoleHelper console)
: base(settings, console)
{
}
public BaseActivity(Person actor, NeedEntry needEntry, EngineSettings settings, ConsoleHelper console)
{
Actor = actor;
NeedEntry = needEntry;
Settings = settings;
Console = console;
InitializeItemsProduced();
InitializeItemsConsumed();
}
public override void Execute(List <string> _params, CommandSenderInfo _senderInfo)
{
try
{
if (_params[0].ToLower().Equals("add"))
{
if (_params.Count != 2)
{
SdtdConsole.Instance.Output(string.Format("Wrong number of arguments, expected 2, found {0}.", _params.Count));
return;
}
ClientInfo _cInfo = ConsoleHelper.ParseParamIdOrName(_params[1]);
if (_cInfo != null)
{
bool _highPingImmune = PersistentContainer.Instance.Players[_cInfo.playerId].HighPingImmune;
if (_highPingImmune)
{
SdtdConsole.Instance.Output(string.Format("Can not add Id. {0} is already in the Ping Immunity list.", _params[1]));
}
else
{
PersistentContainer.Instance.Players[_cInfo.playerId].HighPingImmune = true;
PersistentContainer.Instance.Save();
SdtdConsole.Instance.Output(string.Format("Added Id {0} to the Ping Immunity list.", _params[1]));
}
}
else
{
if (_params[1].Length != 17)
{
SdtdConsole.Instance.Output(string.Format("Player must be online or when offline use their steam Id which must be 17 numbers in length. Can not add Id: {0}", _params[1]));
return;
}
else
{
PersistentPlayer p = PersistentContainer.Instance.Players[_params[1]];
{
if (p != null)
{
bool _highPingImmune = PersistentContainer.Instance.Players[_params[1]].HighPingImmune;
if (_highPingImmune)
{
SdtdConsole.Instance.Output(string.Format("Can not add Id. {0} is already in the Ping Immunity list.", _params[1]));
}
else
{
PersistentContainer.Instance.Players[_params[1]].HighPingImmune = true;
PersistentContainer.Instance.Save();
SdtdConsole.Instance.Output(string.Format("Added Id {0} to the Ping Immunity list.", _params[1]));
}
}
}
}
}
}
else if (_params[0].ToLower().Equals("remove"))
{
if (_params.Count != 2)
{
SdtdConsole.Instance.Output(string.Format("Wrong number of arguments, expected 2, found {0}", _params.Count));
return;
}
if (_params[1].Length != 17)
{
SdtdConsole.Instance.Output(string.Format("Player must be online or when offline use their steam Id which must be 17 numbers in length. Can not add Id: {0}", _params[1]));
return;
}
else
{
PersistentPlayer p = PersistentContainer.Instance.Players[_params[1]];
{
if (p != null)
{
bool _highPingImmune = PersistentContainer.Instance.Players[_params[1]].HighPingImmune;
if (_highPingImmune)
{
SdtdConsole.Instance.Output(string.Format("Can not remove Id. {0} is not in the Ping Immunity list.", _params[1]));
}
else
{
PersistentContainer.Instance.Players[_params[1]].HighPingImmune = true;
PersistentContainer.Instance.Save();
SdtdConsole.Instance.Output(string.Format("Removed Id {0} from the Ping Immunity list.", _params[1]));
}
}
}
}
}
else if (_params[0].ToLower().Equals("list"))
{
if (_params.Count != 1)
{
SdtdConsole.Instance.Output(string.Format("Wrong number of arguments, expected 1, found {0}.", _params.Count));
return;
}
else
{
if (HighPingKicker.Dict.Count == 0)
{
SdtdConsole.Instance.Output("There are no players on the Ping Immunity list.");
return;
}
else
{
SdtdConsole.Instance.Output("High Ping Immunity List");
foreach (KeyValuePair <string, string> _c in HighPingKicker.Dict)
{
SdtdConsole.Instance.Output(string.Format("Player Id: {0} with name {1}", _c.Key, _c.Value));
}
}
}
}
else
{
SdtdConsole.Instance.Output(string.Format("Invalid argument {0}.", _params[0]));
}
}
catch (Exception e)
{
Log.Out(string.Format("[SERVERTOOLS] Error in PingImmunityCommandConsole.Run: {0}.", e));
}
}
public void Execute(CommandContext context)
{
File.Delete(context.AppConfigFilePath);
ConsoleHelper.PrintMsg("配置重置成功！");
}
public HungerEffect(EngineSettings settings, ConsoleHelper console)
: base(settings, console)
{
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Geometric_distribution">Geometric distribution</a>
public void Run()
{
// 1. Initialize the new instance of the Geometric distribution class with parameter P = 0.2
var geometric = new Geometric(0.2);
Console.WriteLine(@"1. Initialize the new instance of the Geometric distribution class with parameter P = {0}", geometric.P);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", geometric);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '3'", geometric.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability mass at location '3'", geometric.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability mass at location '3'", geometric.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", geometric.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", geometric.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", geometric.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", geometric.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", geometric.Median.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", geometric.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", geometric.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", geometric.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", geometric.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the Geometric distribution
Console.WriteLine(@"3. Generate 10 samples of the Geometric distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(geometric.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the Geometric(0.2, 20) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the Geometric(0.2, 20) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = geometric.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the Geometric(0.5) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the Geometric(0.5) distribution and display histogram");
geometric.P = 0.5;
for (var i = 0; i < data.Length; i++)
{
data[i] = geometric.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the Geometric(0.8) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the Geometric(0.8) distribution and display histogram");
geometric.P = 0.8;
for (var i = 0; i < data.Length; i++)
{
data[i] = geometric.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}
private static void Client_OnGroupMessage(GroupMessage msg)
{
ConsoleHelper.WriteLine("收到群组信息：{0}", msg.Content);
}
/// <summary>
/// socket 传输字节解码
/// 格式为：1+4+8+4+x+4+x+x
/// </summary>
/// <param name="data"></param>
/// <param name="onDecode"></param>
bool Decode(byte[] data, Action <RSocketMsg[], uint> onDecode)
{
uint offset = 0;
try
{
if (data != null && data.Length >= offset + MIN)
{
var list = new List <RSocketMsg>();
while (data.Length >= offset + MIN)
{
var total = BitConverter.ToUInt32(data, (int)offset + 1);
if (data.Length >= offset + total + 1)
{
var qm = new RSocketMsg((RSocketMsgType)data[offset]);
offset += 5;
qm.Total = total;
qm.SequenceNumber = BitConverter.ToInt64(data, (int)offset);
offset += 8;
qm.SLen = BitConverter.ToUInt32(data, (int)offset);
offset += 4;
if (qm.SLen > 0)
{
var narr = new byte[qm.SLen];
Buffer.BlockCopy(data, (int)offset, narr, 0, narr.Length);
qm.ServiceName = Encoding.UTF8.GetString(narr);
}
offset += qm.SLen;
qm.MLen = BitConverter.ToUInt32(data, (int)offset);
offset += 4;
if (qm.MLen > 0)
{
var tarr = new byte[qm.MLen];
Buffer.BlockCopy(data, (int)offset, tarr, 0, tarr.Length);
qm.MethodName = Encoding.UTF8.GetString(tarr);
}
offset += qm.MLen;
var dlen = qm.Total - 8 - 4 - 4 - qm.SLen - 4 - qm.MLen;
if (dlen > 0)
{
var darr = new byte[dlen];
Buffer.BlockCopy(data, (int)offset, darr, 0, (int)dlen);
qm.Data = darr;
offset += dlen;
}
list.Add(qm);
}
else
{
break;
}
}
if (list.Count > 0)
{
onDecode?.Invoke(list.ToArray(), offset);
return(true);
}
}
}
catch (Exception ex)
{
ConsoleHelper.WriteLine($"RCoder.Decode error:{ex.Message} stack:{ex.StackTrace} data:{data.Length} offset:{offset}");
}
onDecode?.Invoke(null, 0);
return(false);
}
private static void Client_OnChannelMessage(ChannelMessage msg)
{
ConsoleHelper.WriteLine("收到频道消息：{0}", msg.Content);
}
public void BuildAndTrain(IEnumerable <ImageData> imageSet)
{
ConsoleWriteHeader("Read model");
Console.WriteLine($"Model location: {inputTensorFlowModelFilePath}");
Console.WriteLine($"Training file: {dataLocation}");
// 1. Load images information (filenames and labels) in IDataView
//Load the initial single full Image-Set
//
IDataView fullImagesDataset = mlContext.Data.LoadFromEnumerable(imageSet);
IDataView shuffledFullImagesDataset = mlContext.Data.ShuffleRows(fullImagesDataset);
// Split the data 90:10 into train and test sets, train and evaluate.
TrainTestData trainTestData = mlContext.Data.TrainTestSplit(shuffledFullImagesDataset, testFraction: 0.10);
IDataView trainDataView = trainTestData.TrainSet;
IDataView testDataView = trainTestData.TestSet;
// 2. Load images in-memory while applying image transformations
// Input and output column names have to coincide with the input and output tensor names of the TensorFlow model
// You can check out those tensor names by opening the Tensorflow .pb model with a visual tool like Netron: https://github.com/lutzroeder/netron
// TF .pb model --> input node --> INPUTS --> input --> id: "input"
// TF .pb model --> Softmax node --> INPUTS --> logits --> id: "softmax2_pre_activation" (Inceptionv1) or "InceptionV3/Predictions/Reshape" (Inception v3)
var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: LabelAsKey, inputColumnName: "Label")
.Append(mlContext.Transforms.LoadImages(outputColumnName: "image_object", imageFolder: imagesFolder, inputColumnName: nameof(DataModels.ImageData.ImagePath)))
.Append(mlContext.Transforms.ResizeImages(outputColumnName: "image_object_resized",
imageWidth: ImageSettingsForTFModel.imageWidth, imageHeight: ImageSettingsForTFModel.imageHeight,
inputColumnName: "image_object"))
.Append(mlContext.Transforms.ExtractPixels(outputColumnName: "input", inputColumnName: "image_object_resized",
interleavePixelColors: ImageSettingsForTFModel.channelsLast,
offsetImage: ImageSettingsForTFModel.mean,
scaleImage: ImageSettingsForTFModel.scale)) //for Inception v3 needs scaleImage: set to 1/255f. Not needed for InceptionV1.
.Append(mlContext.Model.LoadTensorFlowModel(inputTensorFlowModelFilePath).
ScoreTensorFlowModel(outputColumnNames: new[] { "InceptionV3/Predictions/Reshape" },
inputColumnNames: new[] { "input" },
addBatchDimensionInput: false)); // (For Inception v1 --> addBatchDimensionInput: true) (For Inception v3 --> addBatchDimensionInput: false)
// 3. Set the training algorithm and convert back the key to the categorical values
var trainer = mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(labelColumnName: LabelAsKey, featureColumnName: "InceptionV3/Predictions/Reshape"); //"softmax2_pre_activation" for Inception v1
var trainingPipeline = dataProcessPipeline.Append(trainer)
.Append(mlContext.Transforms.Conversion.MapKeyToValue(PredictedLabelValue, "PredictedLabel"));
// 4. Train the model
// Measuring training time
var watch = System.Diagnostics.Stopwatch.StartNew();
ConsoleWriteHeader("Training the ML.NET classification model");
ITransformer model = trainingPipeline.Fit(trainDataView);
watch.Stop();
long elapsedMs = watch.ElapsedMilliseconds;
Console.WriteLine("Training with transfer learning took: " + (elapsedMs / 1000).ToString() + " seconds");
// 5. Make bulk predictions and calculate quality metrics
ConsoleWriteHeader("Create Predictions and Evaluate the model quality");
IDataView predictionsDataView = model.Transform(testDataView);
// This is an optional step, but it's useful for debugging issues
var loadedModelOutputColumnNames = predictionsDataView.Schema
.Where(col => !col.IsHidden).Select(col => col.Name);
// 5.1 Show the predictions
ConsoleWriteHeader("*** Showing all the predictions ***");
List <ImagePredictionEx> predictions = mlContext.Data.CreateEnumerable <ImagePredictionEx>(predictionsDataView, false, true).ToList();
predictions.ForEach(pred => ConsoleWriteImagePrediction(pred.ImagePath, pred.Label, pred.PredictedLabelValue, pred.Score.Max()));
// 5.2 Show the performance metrics for the multi-class classification
var classificationContext = mlContext.MulticlassClassification;
ConsoleWriteHeader("Classification metrics");
var metrics = classificationContext.Evaluate(predictionsDataView, labelColumnName: LabelAsKey, predictedLabelColumnName: "PredictedLabel");
ConsoleHelper.PrintMultiClassClassificationMetrics(trainer.ToString(), metrics);
// 6. Save the model to assets/outputs
ConsoleWriteHeader("Save model to local file");
ModelHelpers.DeleteAssets(outputMlNetModelFilePath);
mlContext.Model.Save(model, predictionsDataView.Schema, outputMlNetModelFilePath);
Console.WriteLine($"Model saved: {outputMlNetModelFilePath}");
}
private void _receiver_OnError(string ID, Exception ex)
{
ConsoleHelper.WriteLine("接收文件过程发生异常，ID:{0} err:{1}", ID, ex.Message);
}
private static void Server_OnMessage(string id, WSProtocal data)
{
ConsoleHelper.WriteLine("WSServer 收到{0}的消息：{1}", ConsoleColor.Green, id, Encoding.UTF8.GetString(data.Content));
_server.Reply(id, data);
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Pareto_distribution">Pareto distribution</a>
public void Run()
{
// 1. Initialize the new instance of the Pareto distribution class with parameters Shape = 3, Scale = 1
var pareto = new Pareto(1, 3);
Console.WriteLine(@"1. Initialize the new instance of the Pareto distribution class with parameters Shape = {0}, Scale = {1}", pareto.Shape, pareto.Scale);
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", pareto);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", pareto.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability density at location '0.3'", pareto.Density(0.3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability density at location '0.3'", pareto.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", pareto.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", pareto.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", pareto.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", pareto.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", pareto.Median.ToString(" #0.00000;-#0.00000"));
// Mode
Console.WriteLine(@"{0} - Mode", pareto.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", pareto.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", pareto.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
Console.WriteLine(@"{0} - Skewness", pareto.Skewness.ToString(" #0.00000;-#0.00000"));
Console.WriteLine();
// 3. Generate 10 samples of the Pareto distribution
Console.WriteLine(@"3. Generate 10 samples of the Pareto distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(pareto.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the Pareto(1, 3) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the Pareto(1, 3) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = pareto.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the Pareto(1, 1) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the Pareto(1, 1) distribution and display histogram");
pareto.Shape = 1;
for (var i = 0; i < data.Length; i++)
{
data[i] = pareto.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 6. Generate 100000 samples of the Pareto(10, 5) distribution and display histogram
Console.WriteLine(@"6. Generate 100000 samples of the Pareto(10, 50) distribution and display histogram");
pareto.Shape = 50;
pareto.Scale = 10;
for (var i = 0; i < data.Length; i++)
{
data[i] = pareto.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}
private static void Client_OnError(string ID, Exception ex)
{
ConsoleHelper.WriteLine("WSClient 出现异常：{0}", ConsoleColor.DarkGray, ex.Message);
}
public static void BuildAndTrainModel(string DataSetLocation, string ModelPath, MyTrainerStrategy selectedStrategy)
{
// Create MLContext to be shared across the model creation workflow objects
// Set a random seed for repeatable/deterministic results across multiple trainings.
var mlContext = new MLContext(seed: 1);
// STEP 1: Common data loading configuration
var trainingDataView = mlContext.Data.LoadFromTextFile<GitHubIssue>(DataSetLocation, hasHeader: true, separatorChar:'\t', allowSparse: false);
// STEP 2: Common data process configuration with pipeline data transformations
var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey(outputColumnName: "Label",inputColumnName:nameof(GitHubIssue.Area))
.Append(mlContext.Transforms.Text.FeaturizeText(outputColumnName: "TitleFeaturized",inputColumnName:nameof(GitHubIssue.Title)))
.Append(mlContext.Transforms.Text.FeaturizeText(outputColumnName: "DescriptionFeaturized", inputColumnName: nameof(GitHubIssue.Description)))
.Append(mlContext.Transforms.Concatenate(outputColumnName:"Features", "TitleFeaturized", "DescriptionFeaturized"))
.AppendCacheCheckpoint(mlContext);
// Use in-memory cache for small/medium datasets to lower training time.
// Do NOT use it (remove .AppendCacheCheckpoint()) when handling very large datasets.
// (OPTIONAL) Peek data (such as 2 records) in training DataView after applying the ProcessPipeline's transformations into "Features"
Common.ConsoleHelper.PeekDataViewInConsole(mlContext, trainingDataView, dataProcessPipeline, 2);
// STEP 3: Create the selected training algorithm/trainer
IEstimator<ITransformer> trainer = null;
switch(selectedStrategy)
{
case MyTrainerStrategy.SdcaMultiClassTrainer:
trainer = mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features");
break;
case MyTrainerStrategy.OVAAveragedPerceptronTrainer:
{
// Create a binary classification trainer.
var averagedPerceptronBinaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron("Label", "Features",numberOfIterations: 10);
// Compose an OVA (One-Versus-All) trainer with the BinaryTrainer.
// In this strategy, a binary classification algorithm is used to train one classifier for each class, "
// which distinguishes that class from all other classes. Prediction is then performed by running these binary classifiers, "
// and choosing the prediction with the highest confidence score.
trainer = mlContext.MulticlassClassification.Trainers.OneVersusAll(averagedPerceptronBinaryTrainer);
break;
}
default:
break;
}
//Set the trainer/algorithm and map label to value (original readable state)
var trainingPipeline = dataProcessPipeline.Append(trainer)
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
// STEP 4: Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
// in order to evaluate and get the model's accuracy metrics
Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
var crossValidationResults= mlContext.MulticlassClassification.CrossValidate(data:trainingDataView, estimator:trainingPipeline, numberOfFolds: 6, labelColumnName:"Label");
ConsoleHelper.PrintMulticlassClassificationFoldsAverageMetrics(trainer.ToString(), crossValidationResults);
// STEP 5: Train the model fitting to the DataSet
Console.WriteLine("=============== Training the model ===============");
var trainedModel = trainingPipeline.Fit(trainingDataView);
// (OPTIONAL) Try/test a single prediction with the "just-trained model" (Before saving the model)
GitHubIssue issue = new GitHubIssue() { ID = "Any-ID", Title = "WebSockets communication is slow in my machine", Description = "The WebSockets communication used under the covers by SignalR looks like is going slow in my development machine.." };
// Create prediction engine related to the loaded trained model
var predEngine = mlContext.Model.CreatePredictionEngine<GitHubIssue, GitHubIssuePrediction>(trainedModel);
//Score
var prediction = predEngine.Predict(issue);
Console.WriteLine($"=============== Single Prediction just-trained-model - Result: {prediction.Area} ===============");
//
// STEP 6: Save/persist the trained model to a .ZIP file
Console.WriteLine("=============== Saving the model to a file ===============");
mlContext.Model.Save(trainedModel, trainingDataView.Schema, ModelPath);
Common.ConsoleHelper.ConsoleWriteHeader("Training process finalized");
}
private static void Client_OnMessage(WSProtocal data)
{
ConsoleHelper.WriteLine("WSClient 收到的消息：{0}", ConsoleColor.DarkGray, Encoding.UTF8.GetString(data.Content));
}
private static void Main(string[] args)
{
//Create the MLContext to share across components for deterministic results
MLContext mlContext = new MLContext(seed: 1); //Seed set to any number so you have a deterministic environment
// STEP 1: Common data loading configuration
IDataView fullData = mlContext.Data.ReadFromTextFile(path: DataPath,
columns: new[]
{
new TextLoader.Column(DefaultColumnNames.Label, DataKind.R4, 0),
new TextLoader.Column(nameof(IrisData.SepalLength), DataKind.R4, 1),
new TextLoader.Column(nameof(IrisData.SepalWidth), DataKind.R4, 2),
new TextLoader.Column(nameof(IrisData.PetalLength), DataKind.R4, 3),
new TextLoader.Column(nameof(IrisData.PetalWidth), DataKind.R4, 4),
},
hasHeader: true,
separatorChar: '\t');
//Split dataset in two parts: TrainingDataset (80%) and TestDataset (20%)
(IDataView trainingDataView, IDataView testingDataView) = mlContext.Clustering.TrainTestSplit(fullData, testFraction: 0.2);
//STEP 2: Process data transformations in pipeline
var dataProcessPipeline = mlContext.Transforms.Concatenate(DefaultColumnNames.Features, nameof(IrisData.SepalLength), nameof(IrisData.SepalWidth), nameof(IrisData.PetalLength), nameof(IrisData.PetalWidth));
// (Optional) Peek data in training DataView after applying the ProcessPipeline's transformations
Common.ConsoleHelper.PeekDataViewInConsole(mlContext, trainingDataView, dataProcessPipeline, 10);
Common.ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, DefaultColumnNames.Features, trainingDataView, dataProcessPipeline, 10);
// STEP 3: Create and train the model
var trainer = mlContext.Clustering.Trainers.KMeans(featureColumn: DefaultColumnNames.Features, clustersCount: 3);
var trainingPipeline = dataProcessPipeline.Append(trainer);
var trainedModel = trainingPipeline.Fit(trainingDataView);
// STEP4: Evaluate accuracy of the model
IDataView predictions = trainedModel.Transform(testingDataView);
var metrics = mlContext.Clustering.Evaluate(predictions, score: DefaultColumnNames.Score, features: DefaultColumnNames.Features);
ConsoleHelper.PrintClusteringMetrics(trainer.ToString(), metrics);
// STEP5: Save/persist the model as a .ZIP file
using (var fs = new FileStream(ModelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
mlContext.Model.Save(trainedModel, fs);
Console.WriteLine("=============== End of training process ===============");
Console.WriteLine("=============== Predict a cluster for a single case (Single Iris data sample) ===============");
// Test with one sample text
var sampleIrisData = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
using (var stream = new FileStream(ModelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
ITransformer model = mlContext.Model.Load(stream);
// Create prediction engine related to the loaded trained model
var predEngine = model.CreatePredictionEngine <IrisData, IrisPrediction>(mlContext);
//Score
var resultprediction = predEngine.Predict(sampleIrisData);
Console.WriteLine($"Cluster assigned for setosa flowers:" + resultprediction.SelectedClusterId);
}
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
private static void Client_OnPong(DateTime date)
{
ConsoleHelper.WriteLine("WSClient 收到pong：{0}", ConsoleColor.DarkGray, date);
}
public void SendEmail(String pMessage, String pAddress)
{
SystemWideLogging.LogServiceClient.LogEvent("EmailService :: EmailService.Services\\EmailService.Services\\EmailService.cs :: public void SendEmail(String pMessage, String pAddress)", "Received an MSMQ Request and Sending Email ( pMessage=" + pMessage + " , pAddress " + pAddress + " )");
ConsoleHelper.WriteLine(ConsoleColor.Green, "Send to: " + pAddress);
Console.WriteLine(pMessage);
}
private static void Client_OnDisconnected(string ID, Exception ex)
{
ConsoleHelper.WriteLine("WSClient 连接已断开：" + ex.Message, ConsoleColor.DarkGray);
}
async static Task Main(string[] args)
{
try
{
ConsoleHelper.Initialize();
// Print identity.
Console.WriteLine("Tugas Kelompok Teknik Visualisasi Grafis - Transformasi 3D");
Console.WriteLine("Identitas Anggota Kelompok :");
Console.WriteLine("Daffa Bil Nadzary 19/439811/TK/48541");
Console.WriteLine("Firdaus Bisma Suryakusuma 19/444051/TK/49247");
Console.WriteLine("Hafizh Aradhana Harimurti 19/444053/TK/49249");
Console.WriteLine("Please wait a while.");
Console.WriteLine();
await Task.Delay(3000);
// Start up renderer
_renderer = new RendererServer();
Console.WriteLine();
// Load default model
Console.WriteLine("Loading default model(cube)...");
var modelFileContent = File.ReadAllText($"./Models/default-cube.txt");
_model = ModelData.Deserialize(modelFileContent);
_originalModel = _model.Clone();
await _renderer.RenderAwaitableAsync(_originalModel);
// Load default scene
Console.WriteLine("Loading default scene...");
var sceneFileContent = File.ReadAllText($"./Scenes/default-scene.txt");
_scene = SceneData.Deserialize(sceneFileContent);
await _renderer.SetSceneAwaitableAsync(_scene);
Console.WriteLine();
// Prompt action
while (true)
{
Console.WriteLine("Select an action :");
Console.WriteLine("[0] Load model");
Console.WriteLine("[1] Load scene");
Console.WriteLine("[2] Save model");
Console.WriteLine("[3] Save scene");
Console.WriteLine("[4] Perform a single transformation");
Console.WriteLine("[5] Chain multiple transformations");
Console.WriteLine("[6] Inspect model");
Console.WriteLine("[7] Stop inspecting model");
Console.WriteLine("[8] Reset model");
Console.WriteLine("[9] Exit");
Console.Write("Select : ");
var ans = Console.ReadLine();
Console.WriteLine();
if (ans == "9")
{
break;
}
switch (ans)
{
case "0":
if (_inspectionCancellation != null)
{
_inspectionCancellation.Cancel();
}
await LoadModel();
break;
case "1":
if (_inspectionCancellation != null)
{
_inspectionCancellation.Cancel();
}
await LoadScene();
break;
case "2":
if (_inspectionCancellation != null)
{
_inspectionCancellation.Cancel();
}
_model.Serialize();
Console.Write("Input desired model name without .txt extension : ");
var _modelName = Console.ReadLine();
SaveModel($"./Models/{_modelName}.txt", _model);
break;
case "3":
if (_inspectionCancellation != null)
{
_inspectionCancellation.Cancel();
}
_scene.Serialize();
Console.WriteLine("Input desired scene name without .txt extension : ");
var _sceneName = Console.ReadLine();
SaveScene($"./Models/{_sceneName}.txt", _scene);
break;
case "4":
if (_inspectionCancellation != null)
{
_inspectionCancellation.Cancel();
}
_preTransformedModel = _model.Clone();
BeginTransformation(false);
await _renderer.RenderAwaitableAsync(_model);
break;
case "5":
if (_inspectionCancellation != null)
{
_inspectionCancellation.Cancel();
}
_preTransformedModel = _model.Clone();
BeginTransformation(true);
await _renderer.RenderAwaitableAsync(_model);
break;
case "6":
if (_inspectionCancellation != null)
{
_inspectionCancellation.Cancel();
}
_inspectionCancellation = new CancellationTokenSource();
InspectModel(_inspectionCancellation.Token);
break;
case "7":
_inspectionCancellation.Cancel();
_inspectionCancellation = null;
break;
case "8":
_model = _originalModel.Clone();
await _renderer.RenderAwaitableAsync(_model);
if (_inspectionCancellation != null)
{
_inspectionCancellation.Cancel();
}
break;
default:
if (_inspectionCancellation != null)
{
_inspectionCancellation.Cancel();
}
WriteError("Action not recognized.");
break;
}
Console.WriteLine();
}
Console.WriteLine("Press any key to exit...");
Console.ReadKey();
_renderer.Close();
}
catch (Exception e)
{
WriteError(e.Message);
}
}
static void Main(string[] args)
{
ConsoleHelper.WriteLine("WSServer 正在初始化....", ConsoleColor.Green);
_server.OnMessage += Server_OnMessage;
_server.Start();
ConsoleHelper.WriteLine("WSServer 就绪,回车启动客户端", ConsoleColor.Green);
ConsoleHelper.ReadLine();
WSClient client = new WSClient();
client.OnPong += Client_OnPong;
client.OnMessage += Client_OnMessage;
client.OnError += Client_OnError;
client.OnDisconnected += Client_OnDisconnected;
ConsoleHelper.WriteLine("WSClient 正在连接到服务器...", ConsoleColor.DarkGray);
var connected = client.Connect();
if (connected)
{
ConsoleHelper.WriteLine("WSClient 连接成功，回车测试消息", ConsoleColor.DarkGray);
ConsoleHelper.ReadLine();
//client.Close();
//ConsoleHelper.ReadLine();
var loop = true;
Task.Run(() =>
{
while (loop)
{
ConsoleHelper.WriteLine("WSClient 正在发送消息...", ConsoleColor.DarkGray);
client.Send("hello world!");
Thread.Sleep(1000);
}
});
ConsoleHelper.ReadLine();
loop = false;
ConsoleHelper.WriteLine("WSClient 正在ping服务器...", ConsoleColor.DarkGray);
Thread.Sleep(2000);
client.Ping();
ConsoleHelper.ReadLine();
ConsoleHelper.WriteLine("WSClient 正在断开连接...");
Thread.Sleep(1000);
client.Close();
}
else
{
ConsoleHelper.WriteLine("WSClient 连接失败", ConsoleColor.DarkGray);
}
ConsoleHelper.ReadLine();
}
public GatherFoodActivity(Person actor, NeedEntry needEntry, EngineSettings settings, ConsoleHelper console)
: base(actor, needEntry, settings, console)
{
}
private static void Server_OnDisconnected(string ID, Exception ex)
{
ConsoleHelper.WriteLine($"{ID}已断开连接，ex:{ex.Message}", ConsoleColor.Red);
}
public FightActivity(Person person, NeedEntry needEntry, EngineSettings settings, ConsoleHelper console)
: base(person, needEntry, settings, console)
{
}
private static void FunTest()
{
//===============================================================
ConsoleHelper.WriteLine("回车开始私信测试开始...");
ConsoleHelper.ReadLine();
var cc1 = new MessageClient();
cc1.OnPrivateMessage += Client_OnPrivateMessage;
var cc2 = new MessageClient();
cc2.OnPrivateMessage += Client_OnPrivateMessage;
cc1.Connect();
cc2.Connect();
cc1.Login();
cc2.Login();
Task.Run(() =>
{
while (true)
{
cc1.SendPrivateMsg(cc2.ID, "你好呀,cc2！");
cc2.SendPrivateMsg(cc1.ID, "你好呀,cc1！");
Thread.Sleep(500);
}
});
Parallel.For(0, 1000, (i) =>
{
cc1.SendPrivateMsg(cc2.ID, "你好呀,cc2！");
});
//===============================================================
ConsoleHelper.WriteLine("回车开始频道测试开始...");
ConsoleHelper.ReadLine();
MessageClient client = new MessageClient();
client.OnChannelMessage += Client_OnChannelMessage;
ConsoleHelper.WriteLine("客户正在连接...");
string channelName = "频道测试一";
client.Connect();
ConsoleHelper.WriteLine("客户端就绪");
client.Login();
ConsoleHelper.WriteLine("客户端登录成功");
client.Subscribe(channelName);
ConsoleHelper.WriteLine("客户端订阅成功");
Task.Run(() =>
{
while (true)
{
client.SendChannelMsg(channelName, "hello!");
Thread.Sleep(500);
}
});
//===============================================================
ConsoleHelper.WriteLine("回车开始订阅频道消息...");
ConsoleHelper.ReadLine();
List <MessageClient> list = new List <MessageClient>();
Task.Factory.StartNew(() =>
{
for (int i = 0; i < 100; i++)
{
var c = new MessageClient();
if (i < 10)
{
c.OnChannelMessage += Client_OnChannelMessage2;
}
list.Add(c);
c.Connect();
c.Login();
c.Subscribe(channelName);
}
});
//===============================================================
ConsoleHelper.WriteLine("回车开始群组测试");
ConsoleHelper.ReadLine();
cc1.OnGroupMessage += Client_OnGroupMessage;
cc2.OnGroupMessage += Client_OnGroupMessage;
var groupName = "萌一号蠢";
cc1.SendCreateGroup(groupName);
cc2.SendAddMember(groupName);
Task.Run(() =>
{
while (true)
{
cc1.SendGroupMessage(groupName, "群主广播了！");
cc2.SendGroupMessage(groupName, "群主万岁！");
Thread.Sleep(100);
}
});
ConsoleHelper.ReadLine();
ConsoleHelper.WriteLine("The function test has been completed.");
//===============================================================
cc2.SendRemoveMember(groupName);
cc1.SendRemoveGroup(groupName);
client.Unsubscribe(channelName);
}
public BuildShelterNeedIdentifier(EngineSettings settings, ConsoleHelper console)
: base(ActivityVerb.Build, ItemType.Shelter, PersonVitalType.NotSet, settings, console)
{
}
public EatFoodNeedIdentifier(EngineSettings settings, ConsoleHelper console)
: base(ActivityVerb.Eat, ItemType.Food, PersonVitalType.Hunger, settings, console)
{
}
