private void btnClear_Click(object sender, EventArgs e)
{
txtMain.Clear();
btnAnalyze.Enabled = btnCreateNeuralNet.Enabled = btnOutputNormalized.Enabled = btnTrain.Enabled =
btnSaveNetwork.Enabled = btnLoadNetwork.Enabled = btnTestNetwork.Enabled = false;
btnOpen.Enabled = true;
m_data = null;
m_predictor = null;
}
public Predictor(String fileName, TextBox txtOutput, CSVData data, double percentValidation)
{
m_network = (BasicNetwork)EncogDirectoryPersistence.LoadObject(new FileInfo(fileName));
m_txtOutputWindow = txtOutput;
m_data = data;
// Populate the input and output arrays
LoadData(percentValidation);
m_train = new Backpropagation(m_network, new BasicMLDataSet(m_inputTraining, m_outputTraining));
}
public Predictor(TextBox txtOutput, CSVData data, int hiddenNodes, double percentValidation)
{
m_txtOutputWindow = txtOutput;
m_data = data;
// Populate the input and output arrays
LoadData(percentValidation);
// Create Neural Network
m_network = new BasicNetwork();
m_network.AddLayer(new BasicLayer(null, true, m_data.InputNodes));
m_network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, hiddenNodes));
m_network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, m_data.OutputNodes));
m_network.Structure.FinalizeStructure();
m_network.Reset();
m_train = new Backpropagation(m_network, new BasicMLDataSet(m_inputTraining, m_outputTraining));
}
private void btnOpen_Click(object sender, EventArgs e)
{
var open = new OpenFileDialog();
txtMain.AppendText(String.Format("Open File Result: {0}{1}",open.ShowDialog().ToString(),
Environment.NewLine));
txtMain.AppendText(String.Format("File Name: {0}{1}", open.FileName, Environment.NewLine));
m_data = new CSVData(open.FileName);
txtInputNodes.Text = m_data.InputNodes.ToString();
txtOutputNodes.Text = m_data.OutputNodes.ToString();
txtTotalRecords.Text = (m_data.Rows - 1).ToString();
UpdateTrainingSetSize();
btnOpen.Enabled = false;
btnCreateNeuralNet.Enabled = true;
btnAnalyze.Enabled = true;
btnLoadNetwork.Enabled = true;
}