//
public NCommittee(DataSet pat, DataSet tar, params string[] netFiles)
{
try
{
networks = new BackPropNet[netFiles.Length];
//
for(int i=0; i<netFiles.Length; i++)
networks[i] = BackPropNet.BinaryLoad(netFiles[i]);
//
this.SetData(pat, tar);
this.CheckNetworks();
//
// allocate memory
alpha = new double[networks.Length];
outputs = new Double[patterns.GetNumberOfVectors()];
errors = new double[networks.Length, patterns.GetNumberOfVectors()];
}
catch(System.Exception e)
{
Console.WriteLine(e.Message);
networks = null;
patterns = null;
targets = null;
errors = null;
alpha = null;
throw;
}
}
protected uint vPeriod = 5; // validation period.
#endregion Fields
#region Constructors
//
//
public TrainingAlgorithm(BackPropNet net, string tPatterns, string tTargets)
{
uint depth = net.Depth;
//
try
{
// allocate mem for matrices of error gradients
errGrad = new double[depth-1][,]; // allocate mem for depth-1 vectors
//
for(uint i=0; i<depth-1; i++)
{
uint temp1 = net.GetLayer(i).GetSize();
uint temp2 = net.GetLayer(i+1).GetSize();
//
errGrad[i] = new double[temp1, temp2];
}
//
//
this.trPatterns = new DataSet(tPatterns);
this.trTargets = new DataSet(tTargets);
this.network = net;
//
if(trPatterns.GetNumberOfVectors() != trTargets.GetNumberOfVectors())
throw new Exception("Datasets have different number of vectors.");
if(trPatterns.GetDataValidation() != true || trTargets.GetDataValidation() != true)
throw new Exception("Invalid Data");
if(trPatterns.GetLengthOfVectors() != net.GetLayer(0).GetSize() ||
trTargets.GetLengthOfVectors() != net.GetLayer(net.Depth-1).GetSize())
throw new Exception("Incosistent Data/Inputs or Data/Outputs");
}
catch(Exception ex)
{
Console.WriteLine(ex.Message);
throw;
}
}
//
public void SetValidationData(string vPatterns, string vTargets)
{
try
{
this.vaPatterns = new DataSet(vPatterns);
this.vaTargets = new DataSet(vTargets);
//
if(vaPatterns.GetNumberOfVectors() != vaTargets.GetNumberOfVectors())
throw new Exception("Datasets have different number of vectors.");
if(vaPatterns.GetDataValidation() != true || vaTargets.GetDataValidation() != true)
throw new Exception("Invalid Data");
if(vaPatterns.GetLengthOfVectors() != network.GetLayer(0).GetSize() ||
vaTargets.GetLengthOfVectors() != network.GetLayer(network.Depth-1).GetSize())
throw new Exception("Incosistent Data/Inputs or Data/Outputs");
this.validate = true;
}
catch(Exception ex)
{
Console.WriteLine(ex.Message);
this.validate = false;
throw;
}
}
//
public void SetData(NeuralLib.DataSet pat, NeuralLib.DataSet tar)
{
patterns = pat;
targets = tar;
//
if(patterns.GetDataValidation() != true || targets.GetDataValidation() != true
|| patterns.GetNumberOfVectors() != targets.GetNumberOfVectors())
throw new Exception("Bad Patterns/Targets or vectors differ in length");
//
this.CheckNetworks();
}
//
public double Simulate(DataSet patterns, DataSet targets)
{
double err = 0.0;
double sim = 0.0;
//
try
{
if(patterns.GetLengthOfVectors() != iLayer.GetSize() || targets.GetLengthOfVectors() != oLayer.GetSize())
throw new Exception("Input/Target Vectors Invalid with Input/Output Layer Size");
//
if(patterns.GetDataValidation() == true && targets.GetDataValidation() == true
&& patterns.GetNumberOfVectors() == targets.GetNumberOfVectors())
{
for(int i=0; i<patterns.GetNumberOfVectors(); i++)
{
this.SetInputVector(patterns.GetDataVector(i));
this.SetTargetVector(targets.GetDataVector(i));
this.FeedForward();
this.CalculateOutputError();
err = this.GetInstantOutputError();
sim += err;
}
//
}
else
{
throw new Exception("Invalid Simulation Data");
}
//
return sim/patterns.GetNumberOfVectors();
}
catch(System.Exception ex)
{
Console.WriteLine(ex.Message);
throw;
}
}
//
public void Simulate(string sPatterns, string sTargets, string results)
{
DataSet patterns = new DataSet(sPatterns);
DataSet targets = new DataSet(sTargets);
//
Simulate(patterns, targets, results);
}
//
public void Simulate(string patterns, string results)
{
DataSet ds = new DataSet(patterns);
//
Simulate(ds, results);
}
//
public void Simulate(DataSet patterns, DataSet targets, string results)
{
double err = 0.0;
double sim = 0.0;
//
string filename = results;
StreamWriter writer = new StreamWriter(filename, false);
//
try
{
if(patterns.GetLengthOfVectors() != iLayer.GetSize() || targets.GetLengthOfVectors() != oLayer.GetSize())
throw new Exception("Input/Target Vectors Invalid with Input/Output Layer Size");
//
if(patterns.GetDataValidation() == true && targets.GetDataValidation() == true
&& patterns.GetNumberOfVectors() == targets.GetNumberOfVectors())
{
for(int i=0; i<patterns.GetNumberOfVectors(); i++)
{
this.SetInputVector(patterns.GetDataVector(i));
this.SetTargetVector(targets.GetDataVector(i));
this.FeedForward();
this.CalculateOutputError();
err = this.GetInstantOutputError();
sim += err;
//
foreach(double d in this.oLayer.GetOutputVector())
{
writer.Write(d.ToString("F6")+" ");
}
//
writer.Write(err.ToString("E6"));
writer.Write("\n");
}
//
Console.WriteLine("Average Simulation Error: {0:E6}", sim/patterns.GetNumberOfVectors());
}
else
{
writer.WriteLine("Invalid Simulation Data");
throw new Exception("Invalid Simulation Data");
}
//
writer.Flush();
writer.Close();
}
catch(System.Exception ex)
{
Console.WriteLine(ex.Message);
throw;
}
finally
{
writer.Close();
}
}
private void button2_Click(object sender, System.EventArgs e)
{
try
{
if(of.ShowDialog() == DialogResult.OK && of.FileName != "")
{
network = BackPropNet.BinaryLoad(of.FileName);
rtb1.Text = "Network: "+network.GetStructure();
}
patterns = null;
targets = null;
}
catch(System.Exception ex)
{
MessageBox.Show(ex.Message, "Exception", MessageBoxButtons.OK, MessageBoxIcon.Error);
network = null;
}
//
}
//
public void Simulate(DataSet ds, string results)
{
string filename = results;
//
StreamWriter writer = new StreamWriter(filename, false);
//
try
{
//
if(ds.GetLengthOfVectors() != iLayer.GetSize())
throw new Exception("Input Vector Length differs from Input Layer's Size");
//
if(ds.GetDataValidation())
{
for(int i=0; i<ds.GetNumberOfVectors(); i++)
{
this.SetInputVector(ds.GetDataVector(i));
this.FeedForward();
//
foreach(double d in this.oLayer.GetOutputVector())
{
writer.Write(d.ToString("F4")+" ");
}
//
writer.Write("\n");
}
}
else
{
writer.WriteLine("Invalid Simulation Data");
}
//
writer.Flush();
writer.Close();
}
catch(System.Exception ex)
{
Console.WriteLine(ex.Message);
throw;
}
finally
{
writer.Close();
}
}
//
private void button5_Click(object sender, System.EventArgs e)
{
string filename = null;
//
if(sf.ShowDialog() == DialogResult.OK && of.FileName != " ")
{
filename = sf.FileName;
//
try
{
if(patterns != null)
{
if(targets != null)
network.Simulate(patterns, targets, filename);
else
network.Simulate(patterns, filename);
}
//
MessageBox.Show("Simulation Finished", "Attention", MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
}
catch(System.Exception ex)
{
MessageBox.Show(ex.Message, "Exception", MessageBoxButtons.OK, MessageBoxIcon.Error);
patterns = null;
patterns = null;
network = null;
this.button3.Enabled = false;
this.button4.Enabled = false;
this.button5.Enabled = false;
}
}
//
if(targets != null)
{
NeuralLib.DataSet dset = new NeuralLib.DataSet(filename);
//
double[] dummy;
//
chart1.Series.Clear();
chart1.OpenData(COD.Values, 0, dset.GetNumberOfVectors());
if(network.GetLayer(network.Depth-1).GetSize() == 1)
{
// network has one output neuron.
chart1.AxisY.Title.Text = "Real Output-Simulated Output";
double sse = 0.0;
for(int i=0; i<dset.GetNumberOfVectors(); i++)
{
dummy = dset.GetDataVector(i);
chart1.Value[0, i] = targets.GetDataVector(i)[0];
chart1.Value[1, i] = dummy[0];
sse += dummy[dummy.Length-1];
}
//
label1.Text = "SSE: "+sse.ToString("F6")+" MSE: "+(sse/dset.GetNumberOfVectors()).ToString("F6")+" RMSE: "+
Math.Sqrt(sse/dset.GetNumberOfVectors()).ToString("F6");
}
else
{
chart1.AxisY.Title.Text = "RMSE";
double sse = 0.0;
for(int i=0; i<dset.GetNumberOfVectors(); i++)
{
dummy = dset.GetDataVector(i);
chart1.Value[0, i] = Math.Sqrt(dummy[dummy.Length-1]);
sse += dummy[dummy.Length-1];
}
label1.Text = "SSE: "+sse.ToString("F6")+" MSE: "+(sse/dset.GetNumberOfVectors()).ToString("F6")+" RMSE: "+
Math.Sqrt(sse/dset.GetNumberOfVectors()).ToString("F6");
}
//
label1.Left = chart1.Left+chart1.Width/2-label1.Width/2;
chart1.CloseData(COD.Values);
chart1.RecalcScale();
chart1.Refresh();
}
}
//
private void button4_Click(object sender, System.EventArgs e)
{
try
{
if(of.ShowDialog() == DialogResult.OK && of.FileName != "")
{
targets = new NeuralLib.DataSet(of.FileName);
rtb1.Text = "\nTargets: "+targets.GetNumberOfVectors().ToString()+"x"+targets.GetLengthOfVectors().ToString();
}
}
catch(System.Exception ex)
{
MessageBox.Show(ex.Message, "Exception", MessageBoxButtons.OK, MessageBoxIcon.Error);
targets = null;
}
}
private void button3_Click(object sender, System.EventArgs e)
{
try
{
if(of.ShowDialog() == DialogResult.OK && of.FileName != "")
{
patterns = new NeuralLib.DataSet(of.FileName);
rtb1.Text = "\nPatterns: "+patterns.GetNumberOfVectors().ToString()+"x"+patterns.GetLengthOfVectors().ToString();
}
//
button4.Enabled = true;
button5.Enabled = true;
}
catch(Exception ex)
{
MessageBox.Show(ex.Message, "Exception", MessageBoxButtons.OK, MessageBoxIcon.Error);
patterns = null;
}
//
}
private void button7_Click(object sender, System.EventArgs e)
{
try
{
canStart = true;
// check training inputs and targets
int tinputs = new NeuralLib.DataSet(trainingInputs).GetNumberOfVectors();
int ttargets = new NeuralLib.DataSet(trainingTargets).GetNumberOfVectors();
//
if(tinputs != ttargets)
{
canStart = false;
MessageBox.Show("Training Inputs and Targets Differ in Size");
trIsOk = false;
}
else
{
trIsOk = true;
MessageBox.Show(tinputs.ToString()+" Training Inputs and Targets.", "Attention");
}
//
if(validationInputs != null && validationTargets != null)
{
int vinputs = new NeuralLib.DataSet(validationInputs).GetNumberOfVectors();
int vtargets = new NeuralLib.DataSet(validationTargets).GetNumberOfVectors();
//
if(vinputs != vtargets)
{
canStart = false;
MessageBox.Show("Validation Inputs and Targets Differ in Size");
valIsOk = false;
}
else
{
valIsOk = true;
MessageBox.Show(vinputs.ToString()+" Validation Inputs & Targets. ", "Attention");
}
}
//
if(testInputs != null && testTargets != null)
{
int sinputs = new NeuralLib.DataSet(testInputs).GetNumberOfVectors();
int stargets = new NeuralLib.DataSet(testTargets).GetNumberOfVectors();
//
if(sinputs != stargets)
{
canStart = false;
MessageBox.Show("Test Inputs and Targets Differ in Size");
testIsOk = false;
}
else
{
testIsOk = true;
MessageBox.Show(sinputs.ToString()+" Test Inputs and Targets.", "Attention");
}
}
//
}
catch(Exception ex)
{
MessageBox.Show(ex.Message, "Exception", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
private void button6_Click(object sender, System.EventArgs e)
{
string filename;
//
openFile.Title = "Choose Test Inputs File...";
//
if(openFile.ShowDialog() == DialogResult.OK)
{
filename = openFile.FileName;
//
if(openFile.FileName != " ")
{
this.testInputs = filename;
//
try
{
NeuralLib.DataSet temp = new NeuralLib.DataSet(testInputs);
//
if(!temp.GetDataValidation())
throw new Exception("Test Inputs Data Corrupt!");
else if(temp.GetLengthOfVectors() != Convert.ToInt32(cbInputNeurons.SelectedItem))
throw new Exception("Test Inputs Vector Size NOT EQUAL to Network's Number Of Input Neurons!");
else
MessageBox.Show("Test Inputs Loaded!", "Attention", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
catch(System.Exception ex)
{
MessageBox.Show(ex.Message, "Exception", MessageBoxButtons.OK, MessageBoxIcon.Error);
MessageBox.Show("Choose AGAIN Test Inputs!", "Warning");
this.testInputs = null;
}
}
}
}