/// <summary>
/// Create, Train and Test a Garden Design Network
/// ToDo: Add Dependency Injection
/// </summary>
private static void CreateGardenDesignNetwork()
{
//Create the Network
INeuralNetworkFactory neuralNetworkFactory = new NeuralNetworkFactory();
var neuralNetwork = neuralNetworkFactory.Create(3, 5, 3);
// Train the Network
ITrainingDataRepo trainingDataRepo = new TrainingDataRepo();
var trainingData = trainingDataRepo.GetTrainingDataGardenDesign();
INeuralNetworkTrainingService trainingService = new NeuralNetworkTrainingService(new NeuralNetworkProcessingService());
double[][] trainingResults = trainingService.Train(neuralNetwork, trainingData, 4000, new Sigmoid(), 1);
OutputResultsToConsole(trainingResults);
Console.WriteLine("Training Complete ---------------");
// Test the Trained Network
INeuralNetworkProcessingService processingService = new NeuralNetworkProcessingService();
double[] testInputs1 = { 0, 0, 0 };
double[][] testResults1 = { testInputs1, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs1, new Sigmoid()) };
Console.WriteLine("Test1 Results (0, 0, 0 Low Maint(1)) ---------------");
OutputResultsToConsole(testResults1);
double[] testInputs2 = { 1, 1, 0 };
double[][] testResults2 = { testInputs2, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs2, new Sigmoid()) };
Console.WriteLine("Test2 Results (1, 1, 0 General(5)) ---------------");
OutputResultsToConsole(testResults2);
double[] testInputs3 = { 0, 0, 1 };
double[][] testResults3 = { testInputs3, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs3, new Sigmoid()) };
Console.WriteLine("Test3 Results (0, 0, 1 Child(4)) ---------------");
OutputResultsToConsole(testResults3);
}
/// <summary>
/// Create, Train and Test an XOR Network
/// ToDo: Add Dependency Injection
/// </summary>
private static void CreateXORNetwork()
{
//Create the Network
// Initiate Weights
double[] hiddenLayerWeights = new double[] { 0.38286575972236, 0.441739972420847, 0.00519391335788831, 0.152334728814817 };
double[] hiddenLayerBias = new double[] { 0.79060369394282, 0.25095684418965 };
double[] outputLayerWeights = new double[] { 0.100294556049767, 0.24866028653861 };
double[] outputLayerBias = new double[] { 0.97103805186741 };
INeuralNetworkFactory neuralNetworkFactory = new NeuralNetworkFactory();
var neuralNetwork = neuralNetworkFactory.Create(2, 1, 2, hiddenLayerWeights, outputLayerWeights, hiddenLayerBias, outputLayerBias);
// Train the Network
ITrainingDataRepo trainingDataRepo = new TrainingDataRepo();
var trainingData = trainingDataRepo.GetTrainingDataXOR();
INeuralNetworkTrainingService trainingService = new NeuralNetworkTrainingService(new NeuralNetworkProcessingService());
double[][] trainingResults = trainingService.Train(neuralNetwork, trainingData, 2000, new Sigmoid(), 1);
OutputResultsToConsole(trainingResults);
Console.WriteLine("Training Complete ---------------");
// Test the Trained Network
INeuralNetworkProcessingService processingService = new NeuralNetworkProcessingService();
double[] testInputs1 = { 0, 0 };
double[][] testResults1 = { testInputs1, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs1, new Sigmoid()) };
Console.WriteLine("Test1 Results (0 XOR 0) ---------------");
OutputResultsToConsole(testResults1);
double[] testInputs2 = { 1, 0 };
double[][] testResults2 = { testInputs2, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs2, new Sigmoid()) };
Console.WriteLine("Test2 Results (1 XOR 0) ---------------");
OutputResultsToConsole(testResults2);
double[] testInputs3 = { 0, 1 };
double[][] testResults3 = { testInputs3, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs3, new Sigmoid()) };
Console.WriteLine("Test3 Results (0 XOR 1) ---------------");
OutputResultsToConsole(testResults3);
double[] testInputs4 = { 1, 1 };
double[][] testResults4 = { testInputs4, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs4, new Sigmoid()) };
Console.WriteLine("Test4 Results (1 XOR 1) ---------------");
OutputResultsToConsole(testResults4);
}
/// <summary>
/// Create, Train and Test an AND Network
/// ToDo: Add Dependency Injection
/// </summary>
private static void CreateANDNetwork()
{
//Create the Network (go with random weights and bias)
INeuralNetworkFactory neuralNetworkFactory = new NeuralNetworkFactory();
var neuralNetwork = neuralNetworkFactory.Create(2, 1, 2);
// Train the Network
ITrainingDataRepo trainingDataRepo = new TrainingDataRepo();
var trainingData = trainingDataRepo.GetTrainingDataAND();
INeuralNetworkTrainingService trainingService = new NeuralNetworkTrainingService(new NeuralNetworkProcessingService());
double[][] trainingResults = trainingService.Train(neuralNetwork, trainingData, 2000, new Sigmoid(), 1);
OutputResultsToConsole(trainingResults);
Console.WriteLine("Training Complete ---------------");
// Test the Trained Network
INeuralNetworkProcessingService processingService = new NeuralNetworkProcessingService();
double[] testInputs1 = { 0, 0 };
double[][] testResults1 = { testInputs1, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs1, new Sigmoid()) };
Console.WriteLine("Test1 Results (0 AND 0) ---------------");
OutputResultsToConsole(testResults1);
double[] testInputs2 = { 1, 0 };
double[][] testResults2 = { testInputs2, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs2, new Sigmoid()) };
Console.WriteLine("Test2 Results (1 AND 0) ---------------");
OutputResultsToConsole(testResults2);
double[] testInputs3 = { 0, 1 };
double[][] testResults3 = { testInputs3, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs3, new Sigmoid()) };
Console.WriteLine("Test3 Results (0 AND 1) ---------------");
OutputResultsToConsole(testResults3);
double[] testInputs4 = { 1, 1 };
double[][] testResults4 = { testInputs4, processingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, testInputs4, new Sigmoid()) };
Console.WriteLine("Test4 Results (1 AND 1) ---------------");
OutputResultsToConsole(testResults4);
}