private void btnNeuralRun_Click(object sender, EventArgs e)
{
ges = new Dictionary<int, double>(); // Global Errors
barNeuralMatch.Value = 0;
if (txtNeuralInput.Lines.Count((s) => s.Trim() != "") != txtNeuralExpected.Lines.Count((s) => s.Trim() != ""))
return;
// update NN
Update();
bool shouldloop = true;
//do the magic
for (int i = 0; i < (int)numNeuralEpoch.Value; i++)
{
switch (cmbNeuralAlgorithm.Text)
{
case "Activate": // basic computation
{
brain.Think();
shouldloop = false; // no reason to repeat
break;
}
case "SOMA": // basic computation
{
List<Neural.Configuration> population = new List<Neural.Configuration>();
for (int j = 0; j < 50; j++)
population.Add(new Neural.Configuration(brain, true));
// also use actual configuration
population.Add(new Neural.Configuration(brain));
population = new SOMA().Run(population);
//use the best one
population.Sort((x, y) => x.GE.CompareTo(y.GE));
brain.UpdateConfiguration(population[0]);
brain.Think();
break;
}
case "Fixed Increments":
{
brain.Think(NeuralNetworkAlgorithm.FixedIncrement);
brain.Think(); // check again (no adaptation) to update values
break;
}
case "Back Propagation":
{
brain = brain.Think(NeuralNetworkAlgorithm.BackPropagation);
brain.Think(); // to compute final outputs
break;
}
default:
{
txtLog.AppendText(String.Format("'{0}' algorithm is not implemented.\r\n", cmbNeuralAlgorithm.Text));
shouldloop = false;
break;
}
}
barNeuralProgress.Value = (i + 1) * 10000 / (int)numNeuralEpoch.Value;
txtNeuralOutput.Text = brain.GetDataStr(brain.Outputs);
barNeuralMatch.Value = (int)(brain.ComputeMatch() * 100);
UpdateNeuronDataGrid();
txtNeuralSynapses.Text = brain.GetSynapsesStr();
UpdateChartLSP();
ges.Add(i + 1, brain.GetGlobalError());
UpdateChartStatus();
InvalidateAll();
if (!shouldloop || brain.ComputeMatch() == 1)
{
double ge = brain.GetGlobalError();
for (int j = i + 1; j < numNeuralEpoch.Value; j++)
ges.Add(j + 1, ge);
barNeuralProgress.Value = barNeuralProgress.Maximum;
UpdateChartStatus();
break;
}
if (i != numNeuralEpoch.Value - 1)
{
int sleeptime = (numNeuralEpoch.Value > 10) ? (int)(1000 / numNeuralEpoch.Value) : 200;
if (sleeptime > 16) Thread.Sleep(sleeptime);
}
}
txtLog.AppendText(String.Format("Finished with Global Error of {0}.\n", brain.GetGlobalError()));
}
private void btnNeuralLoadConfiguration_Click(object sender, EventArgs e)
{
openFileDialog1.FileName = "network.ann";
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
IFormatter formatter = new BinaryFormatter();
Stream stream = new FileStream(openFileDialog1.FileName, FileMode.Open, FileAccess.Read, FileShare.Read);
brain = (Brain)formatter.Deserialize(stream);
stream.Close();
//show actual data
txtNeuralInput.Text = brain.GetDataStr(brain.Inputs);
txtNeuralExpected.Text = brain.GetDataStr(brain.Expected);
gridNeuralLayers.Rows.Clear();
for (int i = 0; i < brain.Neurons.Count; i++)
{
AddNewLayer(brain.Neurons[i].Count, functionlist.FirstOrDefault(x => x.Value.ToString() == brain.Neurons[i][0].f.ToString()).Key);
}
barNeuralMatch.Value = 0;
UpdateNeuronDataGrid();
txtNeuralSynapses.Text = brain.GetSynapsesStr();
}
}
