本文整理汇总了C#中RC.Run方法的典型用法代码示例。如果您正苦于以下问题：C# RC.Run方法的具体用法？C# RC.Run怎么用？C# RC.Run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RC的用法示例。
在下文中一共展示了RC.Run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: _ELM
private void _ELM(Particula p)
{
DataProvider prov = new DataProvider(_DataSetPath, EExecutionType.Predction, Util.Random);
RCConfiguration config = _GetELMConfigurationFromPSOParticle(p, prov);
config.Prov.MaxValue = prov.MaxValue.Clone() as double[];
config.Prov.MinValue = prov.MinValue.Clone() as double[];
config.Prov.NormalizeData(inputA, inputB, outputA, outputB);
config.Prov.ShuffleDataSet(1);
config.Prov.SplitData();
RC elm = new RC(config.Prov.TrainSet, config.Prov.ValidationSet, config);
try
{
elm.Run();
}
catch
{ }
RCEvaluator eval = new RCEvaluator(elm, EEvaluationInfo.EMQ | EEvaluationInfo.DEV | EEvaluationInfo.EPMA);
eval.Evaluate();
double fitness = eval.TrainEPMA + (2 * eval.ValidationDEV * p.GetFlagCountFromSubListValues((int)config.Prov.InputsN) / config.Prov.ValidationSetLines) + (config.HidenNodesNumber / MaxHiddenNodes);
p.Eval = eval;
p.Fitness = fitness;
p.Config = config;
count++;
// Variação de C1 e C2
if (_VariarC1C2)
{
if (count % _LimiteVariacaoC1C2 == 0)
{
_C1 = _C1 - _TaxaVariacaoC1C2;
_C2 = _C2 + _TaxaVariacaoC1C2;
}
}
// Variação de W
if (_VariarW)
{
if (count % _LimiteVariacaoW == 0)
_W = _W - _TaxaVariacaoW;
}
}示例2: Main
static void Main(string[] args)
{
try
{
// TestLags();
DateTime StartTime, EndTime;
List<object[]> bestRCs = new List<object[]>();
int MaxRCMemory = 10;
int seed = 1;
int MaxHiddenNodesNumber = 150;
int MaxWarmUpCicles = 100;
double MaxInterConnectivity = 1;
double SpectralRadious = 0.9;
string dataSetPath = @"C:\Users\Edgar\Desktop\Dados\PLD\lag.csv";
for (int h = 10; h < MaxHiddenNodesNumber && bestRCs.Count < 1; h += 5)
{
for (double i = 0.0; i < MaxInterConnectivity && bestRCs.Count < 1; i += 0.02)
{
for (int w = 1; w < MaxWarmUpCicles && bestRCs.Count < 1; w += 5)
{
StartTime = DateTime.Now;
DataProvider dp = new DataProvider(dataSetPath, EExecutionType.Predction, seed, trainSize, validationSize, testSize);
//dp.ApplyLogToData();
dp.NormalizeData(inputA, inputB, outputA, outputB);
dp.ShuffleDataSet();
dp.SplitData();
RC rc = new RC(dp.TrainSet, dp.ValidationSet, dp.TestSet, new RCConfiguration(dp, seed, h, i, w, SpectralRadious, ERCActivationFunctionType.HyperbolicTangent));
rc.Run();
RCEvaluator eval = new RCEvaluator(rc, dp, EEvaluationInfo.EMQ | EEvaluationInfo.DEV | EEvaluationInfo.EPMA | EEvaluationInfo.RMSE);
eval.Evaluate();
RCEvaluator eval2 = new RCEvaluator(rc, dp, EEvaluationInfo.EMQ | EEvaluationInfo.DEV | EEvaluationInfo.EPMA | EEvaluationInfo.RMSE, true);
eval2.Evaluate();
EndTime = DateTime.Now;
Console.WriteLine("Neurônios camada escondida: " + h);
Console.WriteLine("Interconectividade (%): " + i.ToString("0.##"));
Console.WriteLine("Ciclos aquecimento: " + w);
Console.WriteLine("EMQ(7): " + eval.TestEMQ[6].ToString("0.##"));
Console.WriteLine("RMSE(7): " + eval.TestRMSE[6].ToString("0.##"));
Console.WriteLine("EPMA(7): " + eval.TestEPMA[6].ToString("0.##"));
Console.WriteLine("Tempo: " + EndTime.Subtract(StartTime).ToReadableString());
Console.WriteLine("#################################");
object[] b = new object[4];
b[0] = eval.TestEPMA[0];
b[1] = dp;
b[2] = rc;
b[3] = eval;
bestRCs.Add(b);
bestRCs.Sort(delegate(object[] p1, object[] p2)
{
return ((double)p1[0]).CompareTo(((double)p2[0]));
});
if (bestRCs.Count > MaxRCMemory)
bestRCs.RemoveAt(MaxRCMemory);
}
}
}
Console.WriteLine("Salvando Predição da melhor configuração");
#region Salvando Predição
double prediction = 0;
double Error = 0;
DataProvider dataProv = bestRCs[0][1] as DataProvider;
RC elm = bestRCs[0][2] as RC;
RCEvaluator evaluator = bestRCs[0][3] as RCEvaluator;
StreamWriter File = new System.IO.StreamWriter(@"C:\Users\Edgar\Desktop\Dados\Furnas\Predicao\predicao_" +
elm.HiddenNodesNumber + "_" + elm.InterConnectivity + "_" + elm.WarmUpCicles + "_" + evaluator.TestEPMA[6] + ".csv", false);
for (int m = 0; m < dataProv.TestSetlines; m++)
{
string temp = string.Empty;
for (int j = 0; j < dataProv.OutputsN; j++)
{
prediction = dataProv.DeNormalizeOutputData(elm.TestT[m][j], j);
Error = dataProv.TestSet[m].RealOutput[j] - prediction;
temp = string.Concat(temp + dataProv.TestSet[m].RealOutput[j] + ";" + prediction + ";" + Error + ";");
}
File.WriteLine(temp);
}
File.Close();
#endregion
Console.WriteLine("FIM");
Console.ReadLine();
}
//.........这里部分代码省略.........