/// <summary>
/// Learing weights
/// </summary>
/// <param name="document">Document - pdf document</param>
/// <param name="result">LearnResult - learn result</param>
private static void DefineWeightsSection(Document document, LearnResult result)
{
Section section = document.AddSection();
document.LastSection.AddParagraph("Wagi otrzymane w procesie uczenia", "Heading2");
Paragraph paragraph = section.AddParagraph("Wagi otrzymane w procesie uczenia dla: " + Path.GetFileNameWithoutExtension(result.Filename) + "\r\n");
paragraph.AddBookmark("Wagi otrzymane w procesie uczenia");
paragraph.Format.SpaceAfter = "3cm";
paragraph.Format.Font.Size = 16;
paragraph.Format.Font.Color = Colors.Black;
paragraph.Format.SpaceBefore = "1cm";
paragraph.Format.SpaceAfter = "1cm";
int i = 1;
foreach (var item in result.ResultWeights)
{
paragraph = section.AddParagraph("\r\n" + "Wagi otrzymane z uczenia dla próby nr: " + i.ToString() + "\r\n");
paragraph.Format.SpaceAfter = "3cm";
paragraph.Format.Font.Size = 16;
paragraph.Format.Font.Color = Colors.Black;
paragraph.Format.SpaceBefore = "1cm";
paragraph.Format.SpaceAfter = "1cm";
Table table = new Table();
table.Borders.Width = 0.75;
Column column = table.AddColumn(Unit.FromCentimeter(1.5));
column.Format.Alignment = ParagraphAlignment.Center;
Column column2 = table.AddColumn(Unit.FromCentimeter(5.5));
column2.Format.Alignment = ParagraphAlignment.Center;
int w = 1;
Row _row = table.AddRow();
Cell _cell1 = _row.Cells[0];
_cell1.AddParagraph("Numer wagi");
Cell _cell2 = _row.Cells[1];
_cell2.AddParagraph("Waga");
foreach (var weight in item)
{
Row row = table.AddRow();
Cell cell1 = row.Cells[0];
cell1.AddParagraph(w.ToString());
Cell cell2 = row.Cells[1];
cell2.AddParagraph(weight.ToString());
w++;
}
paragraph.Section.Add(table);
//document.LastSection.Add(table);
i++;
}
}
/// <summary>
/// Main page
/// </summary>
/// <param name="document">Document - pdf document</param>
/// <param name="result">LearnResult - learning result</param>
private void DefineInfo(Document document, LearnResult result)
{
Section section = document.AddSection();
document.LastSection.AddParagraph("Informacje na temat sieci neuronowej", "Heading2");
Paragraph paragraph = section.AddParagraph("Informacje na temat sieci neuronowej" + "\r\n");
paragraph.AddBookmark("Informacje na temat sieci neuronowej");
paragraph.Format.SpaceAfter = "3cm";
paragraph.Format.Font.Size = 16;
paragraph.Format.Font.Color = Colors.Black;
paragraph.Format.SpaceBefore = "1cm";
paragraph.Format.SpaceAfter = "1cm";
Table table = new Table();
table.Borders.Width = 0.75;
Column column = table.AddColumn(Unit.FromCentimeter(10.0));
column.Format.Alignment = ParagraphAlignment.Left;
Column column2 = table.AddColumn(Unit.FromCentimeter(5.5));
column2.Format.Alignment = ParagraphAlignment.Center;
Row _row = table.AddRow();
Cell _cell1 = _row.Cells[0];
_cell1.AddParagraph("Nazwa parametru");
Cell _cell2 = _row.Cells[1];
_cell2.AddParagraph("Wartość parametru");
foreach (var item in result.GetInformation())
{
Row row = table.AddRow();
Cell cell1 = row.Cells[0];
cell1.AddParagraph(item[0]);
Cell cell2 = row.Cells[1];
cell2.AddParagraph(item[1]);
}
document.LastSection.Add(table);
}
/// <summary>
/// Load from xml string
/// </summary>
/// <param name="xmlText">String - xml</param>
/// <returns>LearnResult</returns>
public static LearnResult LoadFromXMLString(string xmlText)
{
try
{
LearnResult lr = Xml.Deserialize <LearnResult>(xmlText);
if (Xml.Error != XmlSerializeError.NONE)
{
return(null);
}
else
{
return(lr);
}
}
catch (Exception)
{
return(null);
}
}
/// <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);
}
/// <summary>
/// Load learning result into interface
/// </summary>
/// <param name="lr">LearnResult</param>
private void loadIntoInterface(LearnResult lr)
{
try
{
status.Text = "";
int trial = 1;
weightsList.Items.Clear();
chart.Series.Clear();
foreach (var weights in lr.ResultWeights)
{
String name = LearnByError.Internazional.Resource.Inst.Get("r53") + trial.ToString();
weightsList.Items.Add(name);
for (int i = 0; i < weights.Length; i++)
{
weightsList.Items.Add(weights[i].ToString());
}
weightsList.Items.Add("");
if (lr.Info.np == 0) throw new Exception(LearnByError.Internazional.Resource.Inst.Get("r54"));
int jj = 1;
foreach (var rmse in lr.RMSE)
{
for (int j = 0; j < rmse.Length; j++)
{
SetChart(LearnByError.Internazional.Resource.Inst.Get("r53") + jj.ToString(), j + 1, rmse[j]);
}
jj++;
}
trial++;
}
chart.Update();
status.Text = LearnByError.Internazional.Resource.Inst.Get("r55");
learnRMSE.Text = string.Format(LearnByError.Internazional.Resource.Inst.Get("r230"), result.AverageLearnRMSE);
testRMSE.Text = string.Format(LearnByError.Internazional.Resource.Inst.Get("r231"), result.AverageTestRMSE);
times.Text = string.Format(LearnByError.Internazional.Resource.Inst.Get("r232"), result.AverageLearnTime, result.AverageTestTime);
neuronNumber.Text = string.Format(LearnByError.Internazional.Resource.Inst.Get("r233"), result.Filename, result.Info != null ? result.Info.nn : 0);
}
catch (Exception ex)
{
ex.ToLog();
status.Text = ex.Message;
}
finally
{
this.Text = String.Format(LearnByError.Internazional.Resource.Inst.Get("r229"), history.Items.Count);
}
}
/// <summary>
/// Selection action - selecting learning result
/// </summary>
/// <param name="sender">who</param>
/// <param name="e">arguments</param>
private void list_SelectedIndexChanged(object sender, EventArgs e)
{
try
{
History h = new History();
h.Id = items[history.SelectedIndex];
h.Read();
result = marekbar.Xml.Deserialize<LearnResult>(h.Data);
loadIntoInterface(result);
}
catch (Exception ex)
{
ex.ToLog();
}
}
/// <summary>
/// Main page
/// </summary>
/// <param name="document">Document - pdf document</param>
/// <param name="result">LearnResult - learning result</param>
private void DefineInfo(Document document, LearnResult result)
{
Section section = document.AddSection();
document.LastSection.AddParagraph("Informacje na temat sieci neuronowej", "Heading2");
Paragraph paragraph = section.AddParagraph("Informacje na temat sieci neuronowej" + "\r\n");
paragraph.AddBookmark("Informacje na temat sieci neuronowej");
paragraph.Format.SpaceAfter = "3cm";
paragraph.Format.Font.Size = 16;
paragraph.Format.Font.Color = Colors.Black;
paragraph.Format.SpaceBefore = "1cm";
paragraph.Format.SpaceAfter = "1cm";
Table table = new Table();
table.Borders.Width = 0.75;
Column column = table.AddColumn(Unit.FromCentimeter(10.0));
column.Format.Alignment = ParagraphAlignment.Left;
Column column2 = table.AddColumn(Unit.FromCentimeter(5.5));
column2.Format.Alignment = ParagraphAlignment.Center;
Row _row = table.AddRow();
Cell _cell1 = _row.Cells[0];
_cell1.AddParagraph("Nazwa parametru");
Cell _cell2 = _row.Cells[1];
_cell2.AddParagraph("Wartość parametru");
foreach (var item in result.GetInformation())
{
Row row = table.AddRow();
Cell cell1 = row.Cells[0];
cell1.AddParagraph(item[0]);
Cell cell2 = row.Cells[1];
cell2.AddParagraph(item[1]);
}
document.LastSection.Add(table);
}
/// <summary>
/// Learing weights
/// </summary>
/// <param name="document">Document - pdf document</param>
/// <param name="result">LearnResult - learn result</param>
private static void DefineWeightsSection(Document document, LearnResult result)
{
Section section = document.AddSection();
document.LastSection.AddParagraph("Wagi otrzymane w procesie uczenia", "Heading2");
Paragraph paragraph = section.AddParagraph("Wagi otrzymane w procesie uczenia dla: " + Path.GetFileNameWithoutExtension(result.Filename) + "\r\n");
paragraph.AddBookmark("Wagi otrzymane w procesie uczenia");
paragraph.Format.SpaceAfter = "3cm";
paragraph.Format.Font.Size = 16;
paragraph.Format.Font.Color = Colors.Black;
paragraph.Format.SpaceBefore = "1cm";
paragraph.Format.SpaceAfter = "1cm";
int i = 1;
foreach (var item in result.ResultWeights)
{
paragraph = section.AddParagraph("\r\n" + "Wagi otrzymane z uczenia dla próby nr: " + i.ToString() + "\r\n");
paragraph.Format.SpaceAfter = "3cm";
paragraph.Format.Font.Size = 16;
paragraph.Format.Font.Color = Colors.Black;
paragraph.Format.SpaceBefore = "1cm";
paragraph.Format.SpaceAfter = "1cm";
Table table = new Table();
table.Borders.Width = 0.75;
Column column = table.AddColumn(Unit.FromCentimeter(1.5));
column.Format.Alignment = ParagraphAlignment.Center;
Column column2 = table.AddColumn(Unit.FromCentimeter(5.5));
column2.Format.Alignment = ParagraphAlignment.Center;
int w = 1;
Row _row = table.AddRow();
Cell _cell1 = _row.Cells[0];
_cell1.AddParagraph("Numer wagi");
Cell _cell2 = _row.Cells[1];
_cell2.AddParagraph("Waga");
foreach (var weight in item)
{
Row row = table.AddRow();
Cell cell1 = row.Cells[0];
cell1.AddParagraph(w.ToString());
Cell cell2 = row.Cells[1];
cell2.AddParagraph(weight.ToString());
w++;
}
paragraph.Section.Add(table);
//document.LastSection.Add(table);
i++;
}
}
/// <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;
}