static void Main(string[] args)
{
// create a neural network, without using a factory
var svm = new SupportVectorMachine(2, false); // 2 input, & false for classification
// create training data
IMLDataSet trainingSet = new BasicMLDataSet(ClassificationInput, ClassificationIdeal);
// 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]);
}
Console.WriteLine("Done");
}
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 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 void train(double[][] trainingInput, double[][] idealOutput)
{
// create training data
trainingSet = new BasicMLDataSet(trainingInput, idealOutput);
// train the neural network
IMLTrain train = new SVMSearchTrain(network, trainingSet);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
} while (train.Error > 0.01);
train.FinishTraining();
}
public void Learn()
{
var xorInput = Normalize(XorInputOriginal[1][0]);
var classificationIdeal = Classify(XorIdealOriginal[1][0]);
var svm = new SupportVectorMachine(2, false);
IMLDataSet trainingSet = new BasicMLDataSet(xorInput, classificationIdeal);
IMLTrain train = new SVMSearchTrain(svm, trainingSet);
var epoch = 1;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
} while (train.Error > 0.01);
Console.WriteLine(@"SVM Results:");
var inputString = new StringBuilder();
var idealString = new StringBuilder();
var outputString = new StringBuilder();
foreach (var pair in trainingSet)
{
var output = svm.Compute(pair.Input);
outputString.Append(_alphas[(int)output[0]]);
inputString.Append(Denormalize(pair.Input));
idealString.Append(_alphas[(int)pair.Ideal[0]]);
}
Console.WriteLine("input=" + inputString + @" , " + @" , actual=" + outputString + @" , ideal=" + idealString);
Console.WriteLine("Done");
}