| public Compute ( IMLData input ) : IMLData | ||
| input | IMLData | The input to the SVM. |
| return | IMLData | |
public static void predict(SupportVectorMachine network, SupportVectorMachine network2)
{
Console.WriteLine(@"Year\tActual\tPredict\tClosed Loops");
for (int year = EVALUATE_START; year < EVALUATE_END; year++)
{
// calculate based on actual data
IMLData input = new BasicMLData(WINDOW_SIZE);
for (int i = 0; i < input.Count; i++)
{
input.Data[i] = normalizedSunspots[(year - WINDOW_SIZE) + i];
//input.setData(i,this.normalizedSunspots[(year-WINDOW_SIZE)+i]);
}
IMLData output = network.Compute(input);
IMLData output2 = network2.Compute(input);
double prediction = output.Data[0];
double prediction2 = output2.Data[0];
closedLoopSunspots[year] = prediction;
// calculate "closed loop", based on predicted data
for (int i = 0; i < input.Count; i++)
{
input.Data[i] = closedLoopSunspots[(year - WINDOW_SIZE) + i];
//input.setData(i,this.closedLoopSunspots[(year-WINDOW_SIZE)+i]);
}
output = network.Compute(input);
double closedLoopPrediction = output[0];
IMLData output3 = network2.Compute(input);
double closedLoopPrediction2 = output[0];
// display
//System.out.println((STARTING_YEAR+year)
// +"\t"+f.format(this.normalizedSunspots[year])
// +"\t"+f.format(prediction)
// +"\t"+f.format(closedLoopPrediction)
Console.WriteLine(((STARTING_YEAR + year)
+ @"\t " + Format.FormatDouble(SUNSPOTS[year], 4)
+ @"\t " + Format.FormatDouble(normalizedSunspots[year], 4)
+ @"\t " + Format.FormatDouble(prediction, 4)
+ @"\t " + Format.FormatDouble(prediction2, 4)
+ @"\t " + Format.FormatDouble(closedLoopPrediction, 4)
+ @"\t " + Format.FormatDouble(closedLoopPrediction2, 4)
));
}
}
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]);
}
}
