本文整理汇总了C#中IExampleInterface.Exit方法的典型用法代码示例。如果您正苦于以下问题：C# IExampleInterface.Exit方法的具体用法？C# IExampleInterface.Exit怎么用？C# IExampleInterface.Exit使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类IExampleInterface的用法示例。
在下文中一共展示了IExampleInterface.Exit方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: Execute
public void Execute(IExampleInterface app)
{
this.app = app;
FileInfo dataDir = new FileInfo(Environment.CurrentDirectory);
if (String.Compare(app.Args[0], "sun", true) == 0)
{
SVMPredictSunSpots.PredictSunspotSVM.run();
MakeAPause();
app.Exit();
}
if (String.Compare(app.Args[0], "svm", true) == 0)
{
CreateSVMNetWork.Process(
MLMethodFactory.TypeSVM,
METHOD_SVMR_A,
MLTrainFactory.TypeSVM,
"", 1);
}
if (String.Compare(app.Args[0], "random", true) == 0)
{
if (app.Args.Length > 1)
{
SVM_Predict.CreateSVMNetWork.RandomTrainerMethod(Convert.ToInt16(app.Args[1]), Convert.ToInt16(app.Args[2]));
MakeAPause();
app.Exit();
}
else
{
Console.WriteLine(@"You didn't input enough args in your request, will default to 3000 inputs , and 50 prediction size");
SVM_Predict.CreateSVMNetWork.RandomTrainerMethod(3000, 1);
MakeAPause();
app.Exit();
}
}
}示例2: Execute
public void Execute(IExampleInterface app)
{
this.app = app;
FileInfo dataDir = new FileInfo(Environment.CurrentDirectory);
if (String.Compare(app.Args[0], "randomtrainer", true) == 0)
{
if (app.Args.Length > 1)
{
Encog.Examples.RangeandMarket.RandomTrainer.RandomTrainerMethod(Convert.ToInt16(app.Args[1]), Convert.ToInt16(app.Args[2]));
}
else
{
Console.WriteLine(@"You didn't input enough args in your request, will default to 3000 inputs , and 50 prediction size");
Encog.Examples.RangeandMarket.RandomTrainer.RandomTrainerMethod(3000, 50);
MakeAPause();
}
}
if (String.Compare(app.Args[0], "eval", true) == 0)
{
if (app.Args.Length > 0)
{
//We have enough arguments, lets test them.
if (File.Exists(app.Args[1]))
{
BasicMLDataSet set = CreateEval.CreateEvaluationSetAndLoad(app.Args[1], CONFIG.EvalHowMany, CONFIG.EvalStartFrom, CONFIG.Inputs,
CONFIG.Outputs);
//create our network.
BasicNetwork network =
(BasicNetwork) SuperUtils.LoadNetwork(CONFIG.DIRECTORY, CONFIG.NetWorkFile);
CreateEval.EvaluateNetworks(network, set);
MakeAPause();
app.Exit();
}
}
}
if (String.Compare(app.Args[0], "prune", true) == 0)
{
//Start pruning.
Console.WriteLine("Starting the pruning process....");
Prunes.Incremental(new FileInfo(CONFIG.DIRECTORY), CONFIG.NetWorkFile,
CONFIG.TrainingFile);
MakeAPause();
app.Exit();
}
if (String.Compare(app.Args[0], "train", true) == 0)
{
if (app.Args.Length> 0)
{
//We have enough arguments, lets test them.
if (File.Exists(app.Args[1]))
{
//the file exits lets build the training.
//create our basic ml dataset.
BasicMLDataSet set = CreateEval.CreateEvaluationSetAndLoad(app.Args[1], CONFIG.HowMany, CONFIG.StartFrom, CONFIG.Inputs,
CONFIG.Outputs);
//create our network.
BasicNetwork network = (BasicNetwork) CreateEval.CreateElmanNetwork(CONFIG.Inputs, CONFIG.Outputs);
//Train it..
double LastError = CreateEval.TrainNetworks(network, set);
Console.WriteLine("NetWork Trained to :" + LastError);
SuperUtils.SaveTraining(CONFIG.DIRECTORY, CONFIG.TrainingFile, set);
SuperUtils.SaveNetwork(CONFIG.DIRECTORY, CONFIG.NetWorkFile, network);
Console.WriteLine("Network Saved to :" + CONFIG.DIRECTORY + " File Named :" +
CONFIG.NetWorkFile);
Console.WriteLine("Training Saved to :" + CONFIG.DIRECTORY + " File Named :" +
CONFIG.TrainingFile);
MakeAPause();
}
else
{
Console.WriteLine("Couldnt find the file :" + app.Args[2].ToString());
Console.WriteLine("Exiting");
MakeAPause();
app.Exit();
}
//.........这里部分代码省略.........