/// <summary>
/// Default constructor
/// </summary>
/// <param name="result">LearnResult - learning result</param>
/// <param name="chartFilename">string - char filename</param>
/// <param name="includeDataTable">bool - true id learning data should be included</param>
public HistoryPDF(LearnByErrorLibrary.LearnResult result, String chartFilename, bool includeDataTable = false)
{
this.result = result;
this.chartFilename = chartFilename;
document = new Document();
document.Info.Title = "NBN C# - wynik uczenia sieci neuronowej";
document.Info.Subject = "Uczenie sieci neuronowej za pomocą algorytmu NBN C#";
document.Info.Author = "Marek Bar 33808";
document.Info.Comment = "...ponieważ tylko dobry kod się liczy.";
document.Info.Keywords = "C#, NBN, neuron, uczenie, sieć, nbn c#";
DefineStyles(document);
DefineCover(document);
DefineInfo(document, result);
DefineChartArea(document, chartFilename, Path.GetFileNameWithoutExtension(result.Filename));
DefineWeightsSection(document, result);
if (includeDataTable)
{
MatrixMB mat = MatrixMB.Load(result.Filename);
AddMatrixTable(document, mat);
}
Section section = document.LastSection;
section.AddPageBreak();//index of pages
Paragraph paragraph = section.AddParagraph("Spis treści");
paragraph.Format.Font.Size = 14;
paragraph.Format.Font.Bold = true;
paragraph.Format.SpaceAfter = 24;
paragraph.Format.OutlineLevel = OutlineLevel.Level1;
paragraph = section.AddParagraph();
paragraph.Style = "TOC";
//first link
Hyperlink hyperlink = paragraph.AddHyperlink("Informacje na temat sieci neuronowej");
hyperlink.AddText("\r\n" + "Informacje na temat sieci neuronowej" + "\t");
hyperlink.AddPageRefField("Informacje na temat sieci neuronowej");
hyperlink = paragraph.AddHyperlink("Wykres przebiegu uczenia");
hyperlink.AddText("\r\n" + "Wykres przebiegu uczenia" + "\t");
hyperlink.AddPageRefField("Wykres przebiegu uczenia");
hyperlink = paragraph.AddHyperlink("Wagi otrzymane w procesie uczenia");
hyperlink.AddText("\r\n" + "Wagi otrzymane w procesie uczenia" + "\t");
hyperlink.AddPageRefField("Wagi otrzymane w procesie uczenia");
if (includeDataTable)
{
hyperlink = paragraph.AddHyperlink("Dane wejściowe");
hyperlink.AddText("\r\n" + "Dane wejściowe" + "\t");
hyperlink.AddPageRefField("Dane wejściowe");
}
}
/// <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>
/// Initialize NBN with data from specified file
/// </summary>
/// <param name="filename">String - filename</param>
/// <returns>bool - success flag</returns>
private bool loadInputData(String filename)
{
try
{
if (!File.Exists(filename))
{
throw new NeuralNetworkError(String.Format(Properties.Settings.Default.NBN1, filename));
}
try
{
var data = MatrixMB.Load(filename);
if (MatLabCompareDataFolder.Length > 0)
{
//data from matlab are normalized
string name = Path.GetFileNameWithoutExtension(Directory.GetFiles(MatLabCompareDataFolder, "*.dat")[0]);
inputLearn = MatrixMB.Load(string.Format("{0}\\uczenie_wejscie_{1}.txt", MatLabCompareDataFolder, name)).ToInput();
inputTest = MatrixMB.Load(string.Format("{0}\\testowanie_wejscie_{1}.txt", MatLabCompareDataFolder, name)).ToInput();
outputLearn = MatrixMB.Load(string.Format("{0}\\uczenie_wyjscie_{1}.txt", MatLabCompareDataFolder, name)).ToOutput();
outputTest = MatrixMB.Load(string.Format("{0}\\testowanie_wyjscie_{1}.txt", MatLabCompareDataFolder, name)).ToOutput();
}
else
{
input = data.CopyColumnsWithoutLast().ToInput();
input.Normalize();
output = data.LastColumn.ToOutput();
output.Normalize();
if (IsClassification)
{
int Tnp = data.Rows;
inputLearn = input.CopyRows(Tnp - 1).ToInput();
inputTest = input.CopyRows(Tnp - 1).ToInput();
outputLearn = output.CopyRows(Tnp - 1).ToOutput();
outputTest = output.CopyRows(Tnp - 1).ToOutput();
}
else
{
int[] ind = data.Rows.RandomPermutation();
int Tnp = Math.Round(data.Rows * 0.7).ToInt();
inputTest = input.CopyRows(Tnp, input.Rows - 1).ToInput();
inputLearn = input.CopyRows(Tnp - 1).ToInput();
outputTest = output.CopyRows(Tnp, input.Rows - 1).ToOutput();
outputLearn = output.CopyRows(Tnp - 1).ToOutput();
}
}
try
{
if (IsResearchMode)//export for matlab - out of use
{
string name = Path.GetFileNameWithoutExtension(filename);
DateTime d = DateTime.Now;
string dir = String.Format("{0}\\Executed research\\{1}\\{2}_{3}_{4}_{5}_{6}_{7}",
Path.GetDirectoryName(filename), name,
d.Year, d.Month, d.Day, d.Hour, d.Minute, d.Second);
if (!Directory.Exists(dir))
{
Directory.CreateDirectory(dir);
}
extractionFolder = dir;
_reasearch_folder = dir;
string learn_input = String.Format("{0}\\{1}_learn_input.dat", dir, name);
string test_input = String.Format("{0}\\{1}_test_input.dat", dir, name);
string learn_output = String.Format("{0}\\{1}_learn_output.dat", dir, name);
string test_output = String.Format("{0}\\{1}_test_output.dat", dir, name);
string initialWweights = String.Format("{0}\\{1}_initial_weights.dat", dir, name);
inputTest.Store(test_input);
inputLearn.Store(learn_input);
outputTest.Store(test_output);
outputLearn.Store(learn_output);
}
}
catch (Exception)
{ }
}
catch (Exception iex)
{
throw new NeuralNetworkError(String.Format(Properties.Settings.Default.NBN2, filename), iex);
}
return(true);
}
catch (Exception ex)
{
updateErrorNBN(ex);
return(false);
}
}
