public static void SaveToFile(ETopology top, EFunction fun, EConstrictionFactor cons, List <List <Double> > outputSamples, int id)
{
List <String> stringList = new List <string>();
String s = "";
for (int i = 0; i < Parameters.SAMPLE_COUNT; i++)
{
s += (i + 1) + "\t";
}
stringList.Add(s + "Fitness\tIterations (Average from 30 samples)");
for (int i = 0; i < Parameters.ITERATION_AMOUNT; i++)
{
String temp = "";
Double sum = 0d;
for (int j = 0; j < Parameters.PARTICLE_AMOUNT; j++)
{
sum += outputSamples[j][i];
temp += outputSamples[j][i] + "\t";
}
stringList.Add(temp + i + "\t" + sum / Parameters.PARTICLE_AMOUNT);
}
System.IO.File.WriteAllLines(@"WriteLines" + top.ToString() + "," + fun.ToString() + "," + cons.ToString() + "," + id + ".txt", stringList.ToArray());
}
/// <summary>
/// Defines CNTK Function with certain arguments.
/// </summary>
/// <param name="function">CNTK function</param>
/// <param name="prediction">First parameters of the function.</param>
/// <param name="target">Second parameters of the function</param>
/// <returns></returns>
private Function createFunction(EFunction function, Function prediction, Variable target)
{
switch (function)
{
case EFunction.BinaryCrossEntropy:
return(CNTKLib.BinaryCrossEntropy(prediction, target, function.ToString()));
case EFunction.CrossEntropyWithSoftmax:
return(CNTKLib.CrossEntropyWithSoftmax(prediction, target, function.ToString()));
case EFunction.ClassificationError:
return(CNTKLib.ClassificationError(prediction, target, function.ToString()));
case EFunction.SquaredError:
return(CNTKLib.SquaredError(prediction, target, function.ToString()));
case EFunction.RMSError:
return(StatMetrics.RMSError(prediction, target, function.ToString()));
case EFunction.MSError:
return(StatMetrics.MSError(prediction, target, function.ToString()));
case EFunction.ClassificationAccuracy:
return(StatMetrics.ClassificationAccuracy(prediction, target, function.ToString()));
default:
throw new Exception($"The '{function}' function is not supported!");
}
}