/// <summary>
/// Created this to test the custom neuron network with binary inputs.
/// </summary>
/// <param name="writer"></param>
public static void Test(
string file,
int numberOfInputNeurons,
int numberOfHiddenNeurons,
int numberOfOutputNeurons,
int numberOfCycles = 50000,
double learningRate = 0.25)
{
TrainingSample sample = new TrainingSample(
new double[] { },
new double[] { });
//We might make a gui for this later.
double[] errorList = new double[numberOfCycles];
int totalNumberOfNeurons = numberOfInputNeurons + numberOfOutputNeurons;
LinearLayer inputLayer = new LinearLayer(numberOfInputNeurons);
SigmoidLayer hiddenLayer = new SigmoidLayer(numberOfHiddenNeurons);
SigmoidLayer outputLayer = new SigmoidLayer(numberOfOutputNeurons);
// This layer is a event handler that fires when the output is generated, hence backpropagation.
BackpropagationConnector conn1 = new BackpropagationConnector(inputLayer, hiddenLayer);
BackpropagationConnector conn2 = new BackpropagationConnector(hiddenLayer, outputLayer);
BackpropagationNetwork network = new BackpropagationNetwork(inputLayer, outputLayer);
network.SetLearningRate(learningRate);
TrainingSet trainingSet = new TrainingSet(10, 8);
// A file stream reader.
var inDefaule = Console.In;
using (StreamReader reader = new StreamReader(file))
{
Console.SetIn(reader);
String line = "";
//trainingSet.Add(new TrainingSample(new double[] { 0, 0, 0, 0, 1 }, new double[1] { 1 }));
while ((line = reader.ReadLine()) != null)
{
String[] array = line.Split(',');
double[] inputArray = new double[10];
double[] outputArray = new double[8];
for (int i = 0; i < 10; i++)
{
inputArray[i] = Convert.ToDouble(array[i]);
}
for (int i = 0; i < 8; i++)
{
outputArray[i] = Convert.ToDouble(array[i + 11]);
}
trainingSet.Add(new TrainingSample(inputArray, outputArray));
}
}
double max = 0;
// create an anonymous function to capture the error value of each iteration, and report back the percent of completion.
network.EndEpochEvent +=
delegate(object networkInput, TrainingEpochEventArgs args)
{
errorList[args.TrainingIteration] = network.MeanSquaredError;
max = Math.Max(max, network.MeanSquaredError);
// PercentComplete = args.TrainingIteration * 100 / numberOfCycles;
};
network.Learn(trainingSet, numberOfCycles);
double[] indices = new double[numberOfCycles];
// for (int i = 0; i < numberOfCycles; i++) { indices[i] = i; } .. oh nvm, its for graphing the learning curve
// what to do for error list?
// errorList => for plotting stuff.
// for (int i = 0; i < numberOfCycles; i++)
// {
//Console.WriteLine(errorList[i]);
// }
// print out the error list for scientific evaluation.
StreamUtilities.DumpData("dumpErrorValues.txt", errorList);
double[] outputResult = network.OutputLayer.GetOutput();
outputResult = network.Run(new double[] { 0.47, 0.41, 0.12, 0.05, 0.1, 0.5, 0.1, 0.1, 0.05, 0.1 });
foreach (var d in outputResult)
{
Console.WriteLine("output: " + d);
}
// Console.WriteLine("final output");
}
