/// <summary>
/// Train Neural Network (N.B Do not train for just 1 example, always randomize training data)
/// </summary>
/// <param name="neuralNetwork">Neural Network</param>
/// <param name="trainingData">Training Data</param>
/// <param name="epochs">Number of Training Epochs</param>
/// <param name="activationFn">Activation Function</param>
/// <param name="learningRate">Learning Rate</param>
/// <returns>Results Array</returns>
public double[][] Train(NeuralNetwork neuralNetwork, TrainingDataDTO trainingData, int epochs, IActivationFunction activationFn, double learningRate)
{
// Get / create some inital vars for results obj
int arrayWidth = trainingData.Inputs.GetLength(0) + trainingData.ExpectedResults.GetLength(0);
double[][] results = new double[epochs * trainingData.Inputs.Length][];
int counter = 0;
for (int i = 0; i < epochs; i++)
{
for (int j = 0; j < trainingData.Inputs.Length; j++)
{
double[] inputs = trainingData.Inputs[j];
double[] expectedResults = trainingData.ExpectedResults[j];
_neuralNetworkProcessingService.ProcessFeedForwardNeuralNetwork(neuralNetwork, inputs, activationFn);
BackPropagate(neuralNetwork, expectedResults, activationFn, learningRate);
// Populate results obj
results[counter] =
inputs.Concat(expectedResults) // Inputs and Expected Results
.Concat(neuralNetwork.OutputLayer.Select(x => x.Output)) // Actual Outputs
.Concat(new double[1] {
neuralNetwork.GlobalError
}).ToArray(); // Overall Global Network Error
counter++;
}
}
return(results);
}
/// <summary>
/// Get Training Data AND
/// </summary>
/// <returns>Training Data AND</returns>
public TrainingDataDTO GetTrainingDataAND()
{
var trainingDataDTO = new TrainingDataDTO();
// Training Data
double[] data1 = { 0, 0 };
double[] data2 = { 0, 1 };
double[] data3 = { 1, 0 };
double[] data4 = { 1, 1 };
trainingDataDTO.Inputs = new double[][] { data1, data2, data3, data4 };
trainingDataDTO.InputTitles = new string[2] {
"Inp1", "Inp2"
};
// Expected Results
double[] result1 = { 0 };
double[] result2 = { 0 };
double[] result3 = { 0 };
double[] result4 = { 1 };
trainingDataDTO.ExpectedResults = new double[][] { result1, result2, result3, result4 };
trainingDataDTO.ExpectedResultsTitles = new string[1] {
"Res1"
};
return(trainingDataDTO);
}
/// <summary>
/// Get Training Data Garden Design
/// </summary>
/// <returns>Training Data Garden Design</returns>
public TrainingDataDTO GetTrainingDataGardenDesign()
{
var trainingDataDTO = new TrainingDataDTO();
// Training Data
double[] data1 = { 0, 0, 0 };
double[] data2 = { 0, 0, 1 };
double[] data3 = { 0, 1, 1 };
double[] data4 = { 1, 1, 1 };
double[] data5 = { 0, 1, 0 };
double[] data6 = { 1, 0, 0 };
double[] data7 = { 1, 0, 1 };
double[] data8 = { 1, 1, 0 };
trainingDataDTO.Inputs = new double[][] { data1, data2, data3, data4, data5, data6, data7, data8 };
trainingDataDTO.InputTitles = new string[8] {
"Inp1", "Inp2", "Inp3", "Inp4", "Inp5", "Inp6", "Inp7", "Inp8"
};
// Expected Results
double[] result1 = { 1, 0, 0, 0, 0 };
double[] result2 = { 0, 0, 0, 1, 0 };
double[] result3 = { 0, 0, 0, 0, 1 };
double[] result4 = { 0, 0, 0, 0, 1 };
double[] result5 = { 0, 1, 0, 0, 0 };
double[] result6 = { 0, 0, 1, 0, 0 };
double[] result7 = { 0, 0, 0, 0, 1 };
double[] result8 = { 0, 0, 0, 0, 1 };
trainingDataDTO.ExpectedResults = new double[][] { result1, result2, result3, result4, result5, result6, result7, result8 };
trainingDataDTO.ExpectedResultsTitles = new string[8] {
"LowMaint", "Child", "Gen", "Gen", "Ent", "Gard", "Gen", "Gen"
};
return(trainingDataDTO);
}
