/// <summary>
/// Run NBN training and its test - this is the first version
/// </summary>
/// <param name="trials">int - number of learning trials</param>
/// <returns>LearnResult</returns>R
private LearnResult RunFirstVersion(int trials)
{
backupSettings = new NeuralNetworkSettings();
backupSettings.MaxError = settings.MaxError;
backupSettings.MaxIterations = settings.MaxIterations;
backupSettings.MU = settings.MU;
backupSettings.MUH = settings.MUH;
backupSettings.MUL = settings.MUL;
backupSettings.Scale = settings.Scale;
Trials = trials;
LearnResult result = new LearnResult();
result.Filename = Filename;
if (MatLabCompareDataFolder.Length > 0)
{
result.Filename = string.Format("{0}\\{1}.dat", MatLabCompareDataFolder, Path.GetFileNameWithoutExtension(Directory.GetFiles(MatLabCompareDataFolder, "*.dat")[0]));
}
if (!loadInputData(Filename))
{
updateErrorNBN("Dane nie zostały wczytane.");
return(result);
}
if (OnDebug != null)
{
debug("Data loaded from file: " + Filename);
}
var tmp = new System.Collections.Generic.List <int>();
tmp.Add(inputLearn.Cols);
//setting number of hidden neurons
for (int i = 0; i < Handle; i++)
{
tmp.Add(1);
}
tmp.Add(1);
var vh = new VectorHorizontal(tmp.Count);
for (int i = 0; i < vh.Length; i++)
{
vh[i] = tmp[i];
}
switch (NBN_Topography)
{
case 0:
{
topo = Topography.Generate(TopographyType.BMLP, vh);
} break;
case 1:
{
topo = Topography.Generate(TopographyType.MLP, vh);
} break;
}
result.Topo = topo.Data[0];
if (OnDebug != null)
{
debug(topo.ToString());
}
if (topo == null)
{
updateErrorNBN("Topologia sieci nie została utworzona.");
return(result);
}
info = this.checkInputs(ref inputLearn, ref outputLearn, ref topo, out indexes);//here are set indexes
result.TopoIndex = indexes.Data[0];
result.Info = info;
if (OnDebug != null)
{
debug(indexes.ToString());
debug(info.ToString());
}
Activation act = new Activation(info.nn);
act.FillWithNumber(NBN_Activation);
act.setValue(info.nn - 1, 0);
result.ActivationFunction = act.Data[0];
Gain gain = new Gain(info.nn);
gain.FillWithNumber(NBN_Gain);
result.GainValue = gain.Data[0];
result.Settings = this.settings;
for (trial = 0; trial < trials; trial++)
{
Weights initialWeights = new Weights(info.nw);
if (MatLabCompareDataFolder.Length > 0)
{
initialWeights = MatrixMB.Load(string.Format("{0}\\poczatkowe_wagi_proba_{1}.txt", MatLabCompareDataFolder, trial + 1)).ToWeights();
}
else
{
initialWeights = Weights.Generate(info.nw);
}
if (IsResearchMode)
{
string initialWeightsFile = String.Format("{0}\\{1}{2}_initial_weights.dat", _reasearch_folder, trial, Path.GetFileNameWithoutExtension(result.Filename));
initialWeights.Store(initialWeightsFile);
}
initialWeights.Name = "Initial";
if (OnDebug != null)
{
debug(String.Format("\r\nTrial {0} from {1}\r\n", trial + 1, trials));
debug(initialWeights.ToString());
}
settings = null;
settings = NeuralNetworkSettings.Default();
settings.MaxError = backupSettings.MaxError;
settings.MaxIterations = backupSettings.MaxIterations;
settings.MU = backupSettings.MU;
settings.MUH = backupSettings.MUH;
settings.MUL = backupSettings.MUL;
settings.Scale = backupSettings.Scale;
I = MatrixMB.Eye(info.nw);
tic();//learn time measure start
var tr = Train(ref this.settings, ref this.info, ref this.inputLearn, ref this.outputLearn,
ref this.topo, initialWeights, ref act, ref gain, ref indexes);
String LearnExecutionTime = toc(); //learn time measure stop
LearnTimeList = time.ElapsedTicks; //learn time measure save
result.Add(tr.weights.Data[0], SSE.ToDoubleArray(), RMSE.ToDoubleArray());
result.LearnRMSE = (double)RMSE[RMSE.Count];
LearnRmseList = LastRMSE;
if (OnDebug != null)
{
debug(tr.weights.ToString());
debug("\r\nLearn execution time: " + LearnExecutionTime + "(hours:minutes:seconds:miliseconds)\r\n");
debug("\r\nLearn SSE: " + tr.sse.ToString() + "\r\n");
debug("\r\nLearn RMSE: " + result.LearnRMSE.ToString() + "\r\n");
}
updateError(result.LearnRMSE);
NetworkInfo infoTest = info.Copy();
infoTest.np = inputTest.Rows;
tic();
error.CalculateError(ref infoTest, ref inputTest, ref outputTest, ref topo, tr.weights, ref act, ref gain, ref indexes);
var TestExecutionTime = toc();
TestTimeList = time.ElapsedTicks;
result.TestRMSE = Math.Sqrt(error.Error / infoTest.np);
TestRmseList = result.TestRMSE;
result.TestingRmseList.Add(result.TestRMSE);
if (OnDebug != null)
{
debug("\r\nTest execution time: " + TestExecutionTime + "(hours:minutes:seconds:miliseconds)\r\n");
debug("\r\nTest SSE: " + error.Error.ToString() + "\r\n");
debug("\r\nTest RMSE: " + result.TestRMSE.ToString() + "\r\n");
}
//if (result.LearnRMSE < Threshold) IsTrainOK++;
if (result.SSE[trial][result.SSE[trial].Length - 1] < Threshold)
{
IsTrainOK++;
}
}
result.LearnRMSE = AverageLearnRMSE;
result.TestRMSE = AverageTestRMSE;
result.setStatisticsData(LearnRMSE, TestRMSE, LearnTime, TestTime, Trials);
result.SuccessRate = (double)IsTrainOK / Trials;
if (IsResearchMode)//save research
{
try
{
string filename = extractionFolder + "\\research result.pdf";
PDFGenerate data = new PDFGenerate();
data.Filename = filename;
data.Result = result;
data.ChartFilename = GeneratePlot(result.RMSE, Path.GetFileNameWithoutExtension(result.Filename));
HistoryPDF pdf = new HistoryPDF(data.Result, data.ChartFilename, true);
pdf.Save(data.Filename);
}
catch { }
}
return(result);
}
public void Run()
{
int counter = 0;
Parallel.ForEach(Items, file =>
{
for (int nn = 1; nn < NeuronNumber; nn++)//neuron number
{
for (int i = 0; i < RepeatForEachFile; i++)//learning tests for file with x neurons in network
{
try
{
NBN nbn = new NBN(nn, file);
var result = nbn.Run(1);
DateTime d = DateTime.Now;
History h = new History();
h.Name = String.Format("{0}_{1}_{2}_{3}_{4}_{5}_{6}_{7}", Path.GetFileNameWithoutExtension(result.Filename),
d.Year, d.Month, d.Day, d.Hour, d.Minute, d.Second, d.Millisecond);
h.Data = marekbar.Xml.Serialize<LearnResult>(result);
h.Insert();
if (PDF)
{
try
{
string filename = Common.Folder.Data + "\\" + h.Name + ".pdf";
PDFGenerate data = new PDFGenerate();
data.Filename = filename;
data.Result = result;
data.ChartFilename = GeneratePlot(result.RMSE[0], Path.GetFileNameWithoutExtension(result.Filename));
HistoryPDF pdf = new HistoryPDF(data.Result, data.ChartFilename, true);
pdf.Save(data.Filename);
}
catch
{ //don't care about it
}
}
}
catch (Exception ex)
{
ex.GetError();
}
finally
{
counter++;
if (OnUpdate != null)
{
OnUpdate(counter);
}
}
}
}
});
}
private void saveToPdfSimple(bool flag)
{
try
{
String filename = Common.Dialog.Save(LearnByError.Internazional.Resource.Inst.Get("r68"), "pdf");
if (filename != "" && result != null)
{
String chartFilename = Common.Folder.Data + "\\" +
DateTime.Now.ToString().Replace(':', '_').Replace(' ', '_').Replace('-', '_') + ".bmp";
chart.SaveImage(chartFilename, System.Drawing.Imaging.ImageFormat.Bmp);
PDFGenerate tmp = new PDFGenerate();
tmp.Filename = filename;
tmp.Result = result;
tmp.ChartFilename = chartFilename;
var bw = new System.ComponentModel.BackgroundWorker();
bw.DoWork += (a, b) =>
{
var data = (PDFGenerate)b.Argument;
HistoryPDF pdf = new HistoryPDF(data.Result, data.ChartFilename, flag);
if (pdf.Save(data.Filename))
{
pdf.Open();
b.Result = LearnByError.Internazional.Resource.Inst.Get("r69") + data.Filename;
}
else
{
b.Result = LearnByError.Internazional.Resource.Inst.Get("r70");
}
};
bw.RunWorkerCompleted += (a, b) =>
{
status.Text = (String)b.Result;
progress.Visible = false;
};
progress.Visible = true;
bw.RunWorkerAsync(tmp);
}
}
catch (Exception ex)
{
ex.ToLog();
status.Text = ex.Message;
}
}
/// <summary>
/// Run NBN training and its test - this is the first version
/// </summary>
/// <param name="trials">int - number of learning trials</param>
/// <returns>LearnResult</returns>R
private LearnResult RunFirstVersion(int trials)
{
backupSettings = new NeuralNetworkSettings();
backupSettings.MaxError = settings.MaxError;
backupSettings.MaxIterations = settings.MaxIterations;
backupSettings.MU = settings.MU;
backupSettings.MUH = settings.MUH;
backupSettings.MUL = settings.MUL;
backupSettings.Scale = settings.Scale;
Trials = trials;
LearnResult result = new LearnResult();
result.Filename = Filename;
if (MatLabCompareDataFolder.Length > 0)
{
result.Filename = string.Format("{0}\\{1}.dat", MatLabCompareDataFolder, Path.GetFileNameWithoutExtension(Directory.GetFiles(MatLabCompareDataFolder, "*.dat")[0]));
}
if (!loadInputData(Filename))
{
updateErrorNBN("Dane nie zostały wczytane.");
return result;
}
if (OnDebug != null) debug("Data loaded from file: " + Filename);
var tmp = new System.Collections.Generic.List<int>();
tmp.Add(inputLearn.Cols);
//setting number of hidden neurons
for (int i = 0; i < Handle; i++)
{
tmp.Add(1);
}
tmp.Add(1);
var vh = new VectorHorizontal(tmp.Count);
for (int i = 0; i < vh.Length; i++)
{
vh[i] = tmp[i];
}
switch (NBN_Topography)
{
case 0:
{
topo = Topography.Generate(TopographyType.BMLP, vh);
} break;
case 1:
{
topo = Topography.Generate(TopographyType.MLP, vh);
} break;
}
result.Topo = topo.Data[0];
if (OnDebug != null) debug(topo.ToString());
if (topo == null)
{
updateErrorNBN("Topologia sieci nie została utworzona.");
return result;
}
info = this.checkInputs(ref inputLearn, ref outputLearn, ref topo, out indexes);//here are set indexes
result.TopoIndex = indexes.Data[0];
result.Info = info;
if (OnDebug != null)
{
debug(indexes.ToString());
debug(info.ToString());
}
Activation act = new Activation(info.nn);
act.FillWithNumber(NBN_Activation);
act.setValue(info.nn - 1, 0);
result.ActivationFunction = act.Data[0];
Gain gain = new Gain(info.nn);
gain.FillWithNumber(NBN_Gain);
result.GainValue = gain.Data[0];
result.Settings = this.settings;
for (trial = 0; trial < trials; trial++)
{
Weights initialWeights = new Weights(info.nw);
if (MatLabCompareDataFolder.Length > 0)
{
initialWeights = MatrixMB.Load(string.Format("{0}\\poczatkowe_wagi_proba_{1}.txt", MatLabCompareDataFolder, trial + 1)).ToWeights();
}
else
{
initialWeights = Weights.Generate(info.nw);
}
if (IsResearchMode)
{
string initialWeightsFile = String.Format("{0}\\{1}{2}_initial_weights.dat", _reasearch_folder, trial, Path.GetFileNameWithoutExtension(result.Filename));
initialWeights.Store(initialWeightsFile);
}
initialWeights.Name = "Initial";
if (OnDebug != null)
{
debug(String.Format("\r\nTrial {0} from {1}\r\n", trial + 1, trials));
debug(initialWeights.ToString());
}
settings = null;
settings = NeuralNetworkSettings.Default();
settings.MaxError = backupSettings.MaxError;
settings.MaxIterations = backupSettings.MaxIterations;
settings.MU = backupSettings.MU;
settings.MUH = backupSettings.MUH;
settings.MUL = backupSettings.MUL;
settings.Scale = backupSettings.Scale;
I = MatrixMB.Eye(info.nw);
tic();//learn time measure start
var tr = Train(ref this.settings, ref this.info, ref this.inputLearn, ref this.outputLearn,
ref this.topo, initialWeights, ref act, ref gain, ref indexes);
String LearnExecutionTime = toc();//learn time measure stop
LearnTimeList = time.ElapsedTicks;//learn time measure save
result.Add(tr.weights.Data[0], SSE.ToDoubleArray(), RMSE.ToDoubleArray());
result.LearnRMSE = (double)RMSE[RMSE.Count];
LearnRmseList = LastRMSE;
if (OnDebug != null)
{
debug(tr.weights.ToString());
debug("\r\nLearn execution time: " + LearnExecutionTime + "(hours:minutes:seconds:miliseconds)\r\n");
debug("\r\nLearn SSE: " + tr.sse.ToString() + "\r\n");
debug("\r\nLearn RMSE: " + result.LearnRMSE.ToString() + "\r\n");
}
updateError(result.LearnRMSE);
NetworkInfo infoTest = info.Copy();
infoTest.np = inputTest.Rows;
tic();
error.CalculateError(ref infoTest, ref inputTest, ref outputTest, ref topo, tr.weights, ref act, ref gain, ref indexes);
var TestExecutionTime = toc();
TestTimeList = time.ElapsedTicks;
result.TestRMSE = Math.Sqrt(error.Error / infoTest.np);
TestRmseList = result.TestRMSE;
result.TestingRmseList.Add(result.TestRMSE);
if (OnDebug != null)
{
debug("\r\nTest execution time: " + TestExecutionTime + "(hours:minutes:seconds:miliseconds)\r\n");
debug("\r\nTest SSE: " + error.Error.ToString() + "\r\n");
debug("\r\nTest RMSE: " + result.TestRMSE.ToString() + "\r\n");
}
//if (result.LearnRMSE < Threshold) IsTrainOK++;
if (result.SSE[trial][result.SSE[trial].Length - 1] < Threshold) IsTrainOK++;
}
result.LearnRMSE = AverageLearnRMSE;
result.TestRMSE = AverageTestRMSE;
result.setStatisticsData(LearnRMSE, TestRMSE, LearnTime, TestTime, Trials);
result.SuccessRate = (double)IsTrainOK / Trials;
if (IsResearchMode)//save research
{
try
{
string filename = extractionFolder + "\\research result.pdf";
PDFGenerate data = new PDFGenerate();
data.Filename = filename;
data.Result = result;
data.ChartFilename = GeneratePlot(result.RMSE, Path.GetFileNameWithoutExtension(result.Filename));
HistoryPDF pdf = new HistoryPDF(data.Result, data.ChartFilename, true);
pdf.Save(data.Filename);
}
catch { }
}
return result;
}
