/**
* Prints network output for the each element from the specified training set.
* @param neuralNet neural network
* @param trainingSet training set
*/
public static void TestNeuralNetwork(NeuralNetwork neuralNet, TrainingSet trainingSet)
{
foreach (TrainingElement trainingElement in trainingSet.TrainingElements)
{
neuralNet.SetInput(trainingElement.Input);
neuralNet.Calculate();
double[] networkOutput = neuralNet.Output;
Console.Write("Input: " + trainingElement.Input.ArrayString());
Console.WriteLine(" Output: " + networkOutput.ArrayString());
}
}
/**
* Predict sunspots.
* @param network Neural network to use.
*/
public void Predict(NeuralNetwork network)
{
Console.WriteLine("Year\tActual\tPredict\tClosed Loop Predict");
for (int year = EVALUATE_START; year < EVALUATE_END; year++)
{
// calculate based on actual data
double[] input = new double[WINDOW_SIZE];
for (int i = 0; i < input.Length; i++)
{
input[i] = this.normalizedSunspots[(year - WINDOW_SIZE) + i];
}
network.SetInput(input);
network.Calculate();
double[] output = network.Output;
double prediction = output[0];
this.closedLoopSunspots[year] = prediction;
// calculate "closed loop", based on predicted data
for (int i = 0; i < input.Length; i++)
{
input[i] = this.closedLoopSunspots[(year - WINDOW_SIZE) + i];
}
network.SetInput(input);
network.Calculate();
output = network.Output;
double closedLoopPrediction = output[0];
// display
Console.WriteLine((STARTING_YEAR + year) + "\t"
+ this.normalizedSunspots[year] + "\t"
+ prediction + "\t"
+ closedLoopPrediction);
}
}