public void Execute(IExampleInterface app)
{
// create a neural network, without using a factory
var svm = new SupportVectorMachine(1,true); // 1 input, & true for regression
// create training data
IMLDataSet trainingSet = new BasicMLDataSet(RegressionInput, RegressionIdeal);
// train the SVM
IMLTrain train = new SVMSearchTrain(svm, trainingSet);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
} while (train.Error > 0.01);
// test the SVM
Console.WriteLine(@"SVM Results:");
foreach (IMLDataPair pair in trainingSet)
{
IMLData output = svm.Compute(pair.Input);
Console.WriteLine(pair.Input[0]
+ @", actual=" + output[0] + @",ideal=" + pair.Ideal[0]);
}
}
public override void TrainNetwork()
{
IMLTrain train = new SVMSearchTrain(Svm, TrainingSet);
var iteration = 1;
var errors = new List<double[]>();
do
{
train.Iteration();
errors.Add(new[] { -1, iteration, train.Error });
Console.WriteLine(@"Iteration #" + iteration++ + @" Training error:" + train.Error);
} while ((iteration < Parameters.IterationsCount) && (train.Error > Parameters.AcceptedError));
train.FinishTraining();
ErrorSet = errors.ToArray();
}
public static SupportVectorMachine SVMSearch(SupportVectorMachine anetwork, IMLDataSet training)
{
SVMSearchTrain bestsearch = new SVMSearchTrain(anetwork, training);
StopTrainingStrategy stop = new StopTrainingStrategy(0.00000000001, 1);
bestsearch.AddStrategy(stop);
while (bestsearch.IterationNumber < 30 && !stop.ShouldStop())
{
bestsearch.Iteration();
Console.WriteLine("Iteration #" + bestsearch.IterationNumber + " Error :" + bestsearch.Error);
}
bestsearch.FinishTraining();
return anetwork;
}
/// <summary>
/// Create a SVM trainer.
/// </summary>
///
/// <param name="method">The method to use.</param>
/// <param name="training">The training data to use.</param>
/// <param name="argsStr">The arguments to use.</param>
/// <returns>The newly created trainer.</returns>
public IMLTrain Create(IMLMethod method,
IMLDataSet training, String argsStr)
{
if (!(method is SupportVectorMachine))
{
throw new EncogError(
"SVM Train training cannot be used on a method of type: "
+ method.GetType().FullName);
}
IDictionary<String, String> args = ArchitectureParse.ParseParams(argsStr);
new ParamsHolder(args);
var holder = new ParamsHolder(args);
double gammaStart = holder.GetDouble(
PropertyGamma1, false,
SVMSearchTrain.DefaultGammaBegin);
double cStart = holder.GetDouble(PropertyC1,
false, SVMSearchTrain.DefaultConstBegin);
double gammaStop = holder.GetDouble(
PropertyGamma2, false,
SVMSearchTrain.DefaultGammaEnd);
double cStop = holder.GetDouble(PropertyC2,
false, SVMSearchTrain.DefaultConstEnd);
double gammaStep = holder.GetDouble(
PropertyGammaStep, false,
SVMSearchTrain.DefaultGammaStep);
double cStep = holder.GetDouble(PropertyCStep,
false, SVMSearchTrain.DefaultConstStep);
var result = new SVMSearchTrain((SupportVectorMachine) method, training)
{
GammaBegin = gammaStart,
GammaEnd = gammaStop,
GammaStep = gammaStep,
ConstBegin = cStart,
ConstEnd = cStop,
ConstStep = cStep
};
return result;
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
ParamsHolder holder;
double num;
double num2;
double num3;
double num4;
double num5;
double num6;
SVMSearchTrain train2;
if (method is SupportVectorMachine)
{
IDictionary<string, string> theParams = ArchitectureParse.ParseParams(argsStr);
new ParamsHolder(theParams);
if ((((uint) num3) - ((uint) num4)) < 0)
{
goto Label_0053;
}
if ((((uint) num2) + ((uint) num6)) <= uint.MaxValue)
{
holder = new ParamsHolder(theParams);
num = holder.GetDouble("GAMMA1", false, -10.0);
num2 = holder.GetDouble("C1", false, -5.0);
goto Label_0101;
}
goto Label_016E;
}
goto Label_0185;
Label_0053:
train2.GammaEnd = num3;
if ((((uint) num5) + ((uint) num)) > uint.MaxValue)
{
goto Label_0185;
}
train2.GammaStep = num5;
if (((uint) num3) >= 0)
{
train2.ConstBegin = num2;
train2.ConstEnd = num4;
if ((((uint) num4) | 3) != 0)
{
train2.ConstStep = num6;
return train2;
}
}
else
{
return train2;
}
Label_0101:
num3 = holder.GetDouble("GAMMA2", false, 10.0);
num4 = holder.GetDouble("C2", false, 15.0);
Label_016E:
if (((uint) num4) <= uint.MaxValue)
{
num5 = holder.GetDouble("GAMMASTEP", false, 1.0);
num6 = holder.GetDouble("CSTEP", false, 2.0);
train2 = new SVMSearchTrain((SupportVectorMachine) method, training) {
GammaBegin = num
};
}
goto Label_0053;
Label_0185:
throw new EncogError("SVM Train training cannot be used on a method of type: " + method.GetType().FullName);
}