private static Topography BridgedMultiLayerPerceptron(ref VectorHorizontal lbl)
{
try
{
var vals = new System.Collections.Generic.List<double>();
int nl = lbl.Length;
for (int i = 1; i < nl; i++)
{
int s = lbl.Sum(0, i - 1).ToInt();
for (int j = 0; j < lbl.getValue(i); j++)
{
vals.Add(s + j);
double[] tmp = new double[s];
for (int ii = 0; ii < s; ii++)
{
tmp[ii] = ii;
}
vals.AddRange(tmp);
}
}
var topo = new Topography(vals.ToArray());
topo.Type = TopographyType.BMLP;
return topo;
}
catch
{
return null;
}
}
/// <summary>
/// Convert VectorHorizontal to List
/// </summary>
/// <param name="vh">VectorHorizontal</param>
/// <returns>List - generic</returns>
public static List <double> ToList(this VectorHorizontal vh)
{
List <double> list = new List <double>();
list.AddRange(vh.Data[0]);
return(list);
}
private static Topography MultiLayerPerceptron(ref VectorHorizontal lbl)
{
try
{
var vals = new System.Collections.Generic.List<double>();
int nl = lbl.Length;
for (int i = 1; i < nl; i++)
{
int lw = 0;
for (int a = 0; a < i; a++) lw += (int)lbl[a];
for (int j = 0; j < lbl[i]; j++)
{
vals.Add(lw + j);
for (int k = (int)(lw - lbl[i - 1] + 1); k <= lw; k++)
{
vals.Add(k-1);
}
}
}
var topo = new Topography(vals.ToArray());
topo.Type = TopographyType.MLP;
return topo;
}
catch
{
return null;
}
}
/// <summary>
/// Generate network topography
/// </summary>
/// <param name="type">TopographyType - topo type</param>
/// <param name="lbl">VectorHorizontal</param>
/// <returns>Topography - generated topography</returns>
public static Topography Generate(TopographyType type, VectorHorizontal lbl)
{
switch (type)
{
case TopographyType.BMLP: return BridgedMultiLayerPerceptron(ref lbl);
case TopographyType.MLP: return MultiLayerPerceptron(ref lbl);
default: return null;//oops, not implemented
}
}//generate
/// <summary>
/// Convert MatrixMB to VectorHorizontal
/// </summary>
/// <param name="mat">MatrixMB</param>
/// <returns>VectorHorizontal</returns>
public static VectorHorizontal ToVectorHorizontal(this MatrixMB mat)
{
VectorHorizontal vh = new VectorHorizontal(mat.Cols);
for (int col = 0; col < mat.Cols; col++)
{
vh.Data[0][col] = mat.Data[0][col];
}
return(vh);
}
public void VectorHorizontalTests()
{
try
{
VectorHorizontal vh = new VectorHorizontal(3);
vh[0] = 1;
vh[1] = 2;
vh[2] = 3;
Assert.AreEqual(1, vh[0]);
Assert.AreEqual(2, vh[1]);
Assert.AreEqual(3, vh[2]);
}
catch(Exception ex)
{
Assert.Fail(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);
}
/// <summary>
/// Convert MatrixMB to VectorHorizontal
/// </summary>
/// <param name="mat">MatrixMB</param>
/// <returns>VectorHorizontal</returns>
public static VectorHorizontal ToVectorHorizontal(this MatrixMB mat)
{
VectorHorizontal vh = new VectorHorizontal(mat.Cols);
for (int col = 0; col < mat.Cols; col++)
{
vh.Data[0][col] = mat.Data[0][col];
}
return vh;
}
public void Test___Error___Calculation___Test()
{
/*
*
*
Kod testowy matlaba
*
function obliczanie_bledu()
inp = [-1 -1;-1 1; 1 -1];
dout = [1;0;0];
topo = [3 1 2 4 1 2 3];
w = [1 1 1 1 1 1 1];
act = [2 0];
gain = [1 1];
param = [3 2 1 7 2];
iw = [1 4 8];
format long;
error = calculate_error(inp,dout,topo,w,act,gain,param,iw);
fprintf('Obliczanie błędu uczenia sieci\nBłąd wynosi: %.20f\n', error);
%Otrzymany wynik
%Obliczanie błędu uczenia sieci
%Błąd wynosi: 13.83283022280404000000
end
*
*
*/
Input input = new Input(3, 2);//inp = [-1 -1;-1 1; 1 -1];
input[0, 0] = -1;
input[0, 1] = -1;
input[1, 0] = -1;
input[1, 1] = 1;
input[2, 0] = 1;
input[2, 1] = -1;
Output output = new Output(3, 1);//dout = [1;0;0];
output[0, 0] = 1;
output[1, 0] = 0;
output[2, 0] = 0;
NetworkInfo info = new NetworkInfo();//param = [3 2 1 7 2];
info.ni = 2;
info.nn = 2;
info.no = 1;
info.np = 3;
info.nw = 7;
VectorHorizontal vh = new VectorHorizontal(3);
vh[0, 0] = 2;
vh[0, 1] = 1;
vh[0, 2] = 1;
Topography topo = Topography.Generate(TopographyType.BMLP, vh);//topo = [3 1 2 4 1 2 3];
//w C# indeksy są od zera a nie od 1 więc wszystko o 1 w dół przestawione jest
Assert.AreEqual(2, topo[0]);
Assert.AreEqual(0, topo[1]);
Assert.AreEqual(1, topo[2]);
Assert.AreEqual(3, topo[3]);
Assert.AreEqual(0, topo[4]);
Assert.AreEqual(1, topo[5]);
Assert.AreEqual(2, topo[6]);
Weights weights = new Weights(info.nw);//w = [1 1 1 1 1 1 1];
weights.FillWithNumber(1);//załatwione
Activation act = new Activation(2);//act = [2 0];
act[0] = 2;
act[1] = 0;
Gain gain = new Gain(2);//gain = [1 1];
gain[0] = 1;
gain[1] = 1;
Index iw = Index.Find(ref topo);//iw = [1 4 8];
//ta sama sytuacja, indeksy od 0 startują
Assert.AreEqual(0, iw[0]);
Assert.AreEqual(3, iw[1]);
Assert.AreEqual(7, iw[2]);
NetworkError ne = new NetworkError();
var error = ne.CalculateError(ref info, ref input, ref output, ref topo, weights, ref act, ref gain, ref iw);
double errorFromMatLab = 13.83283022280404000000;
Console.WriteLine("Testowanie obliczania błędu");
Console.WriteLine("Użyte dane:");
Console.WriteLine("\nDane wejściowe:");
Console.WriteLine(input.MatrixToString());
Console.WriteLine("\nDane wyjściowe:");
Console.WriteLine(output.MatrixToString());
Console.WriteLine("\nWagi;");
Console.WriteLine(weights.MatrixToString());
Console.WriteLine("\nTopologia:");
Console.WriteLine(topo.MatrixToString());
Console.WriteLine("\nIndeksy topologii:");
Console.WriteLine(iw.MatrixToString());
Console.WriteLine("\nFunkcje aktywacji:");
Console.WriteLine(act.MatrixToString());
Console.WriteLine("\nWzmocnienia (gains):");
Console.WriteLine(gain.MatrixToString());
Console.WriteLine("\nParametry (param):");
Console.WriteLine(info.ToString());
Assert.AreEqual(errorFromMatLab, error);
Console.WriteLine(string.Format("{0} - wynik NBN C#",error));
Console.WriteLine(string.Format("{0} - wynik NBN w MatLabie",errorFromMatLab));
}
/// <summary>
/// Generate network topography
/// </summary>
/// <param name="type">TopographyType - topo type</param>
/// <param name="lbl">VectorHorizontal</param>
/// <returns>Topography - generated topography</returns>
public static Topography Generate(TopographyType type, VectorHorizontal lbl)
{
switch (type)
{
case TopographyType.BMLP: return BridgedMultiLayerPerceptron(ref lbl);
case TopographyType.MLP: return MultiLayerPerceptron(ref lbl);
default: return null;//oops, not implemented
}
}
private static Topography MultiLayerPerceptron(ref VectorHorizontal lbl)
{
try
{
var vals = new System.Collections.Generic.List<double>();
int nl = lbl.Length;
for (int i = 1; i < nl; i++)
{
int lw = 0;
for (int a = 0; a < i; a++) lw += (int)lbl[a];
for (int j = 0; j < lbl[i]; j++)
{
vals.Add(lw + j);
for (int k = (int)(lw - lbl[i - 1] + 1); k <= lw; k++)
{
vals.Add(k-1);
}
}
}
var topo = new Topography(vals.ToArray());
topo.Type = TopographyType.MLP;
return topo;
}
catch
{
return null;
}
}
private static Topography BridgedMultiLayerPerceptron(ref VectorHorizontal lbl)
{
try
{
var vals = new System.Collections.Generic.List<double>();
int nl = lbl.Length;
for (int i = 1; i < nl; i++)
{
int s = lbl.Sum(0, i - 1).ToInt();
for (int j = 0; j < lbl.getValue(i); j++)
{
vals.Add(s + j);
double[] tmp = new double[s];
for (int ii = 0; ii < s; ii++)
{
tmp[ii] = ii;
}
vals.AddRange(tmp);
}
}
var topo = new Topography(vals.ToArray());
topo.Type = TopographyType.BMLP;
return topo;
}
catch
{
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;
}
public void Hessian___Gradient___Calculation___Test()
{
int accuracy = 15;
/*
function obliczanie_hesjan_gradient()
clear();
inp = [-1 -1;-1 1; 1 -1];
dout = [1;0;0];
topo = [3 1 2 4 1 2 3];
ww = [1 1 1 1 1 1 1];
act = [2 0];
gain = [1 1];
param = [3 2 1 7 2];
iw = [1 4 8];
format long;
[gradient,hessian] = Hessian(inp,dout,topo,ww,act,gain,param,iw);
fprintf('Otrzymany gradient:\n');
disp(gradient);
fprintf('\nOtrzymany hesjan:\n');
disp(hessian);
% To jest otrzymywane
% Otrzymany gradient:
% -0.839948683228052
% 2.319597374905329
% 2.319597374905329
% -2.000000000000000
% 5.523188311911530
% 5.523188311911531
% 6.889667569278484
%
%
% Otrzymany hesjan:
% Columns 1 through 6
%
% 1.058270685684809 -0.705513790456539 -0.705513790456539 2.519846049684157 -1.679897366456105 -1.679897366456105
% -0.705513790456539 1.058270685684809 0.352756895228269 -1.679897366456105 2.519846049684157 0.839948683228052
% -0.705513790456539 0.352756895228269 1.058270685684809 -1.679897366456105 0.839948683228052 2.519846049684157
% 2.519846049684157 -1.679897366456105 -1.679897366456105 6.000000000000000 -4.000000000000000 -4.000000000000000
% -1.679897366456105 2.519846049684157 0.839948683228052 -4.000000000000000 6.000000000000000 2.000000000000000
% -1.679897366456105 0.839948683228052 2.519846049684157 -4.000000000000000 2.000000000000000 6.000000000000000
% -0.639700008449225 1.279400016898449 1.279400016898449 -1.523188311911530 3.046376623823059 3.046376623823059
%
% Column 7
%
% -0.639700008449225
% 1.279400016898449
% 1.279400016898449
% -1.523188311911530
% 3.046376623823059
% 3.046376623823059
% 3.480153950315843
end
*/
Input input = new Input(3, 2);//inp = [-1 -1;-1 1; 1 -1];
input[0, 0] = -1;
input[0, 1] = -1;
input[1, 0] = -1;
input[1, 1] = 1;
input[2, 0] = 1;
input[2, 1] = -1;
Output output = new Output(3, 1);//dout = [1;0;0];
output[0, 0] = 1;
output[1, 0] = 0;
output[2, 0] = 0;
NetworkInfo info = new NetworkInfo();//param = [3 2 1 7 2];
info.ni = 2;
info.nn = 2;
info.no = 1;
info.np = 3;
info.nw = 7;
VectorHorizontal vh = new VectorHorizontal(3);
vh[0, 0] = 2;
vh[0, 1] = 1;
vh[0, 2] = 1;
Topography topo = Topography.Generate(TopographyType.BMLP, vh);//topo = [3 1 2 4 1 2 3];
//w C# indeksy są od zera a nie od 1 więc wszystko o 1 w dół przestawione jest
Assert.AreEqual(2, topo[0]);
Assert.AreEqual(0, topo[1]);
Assert.AreEqual(1, topo[2]);
Assert.AreEqual(3, topo[3]);
Assert.AreEqual(0, topo[4]);
Assert.AreEqual(1, topo[5]);
Assert.AreEqual(2, topo[6]);
Weights weights = new Weights(info.nw);//w = [1 1 1 1 1 1 1];
weights.FillWithNumber(1);//załatwione
Activation act = new Activation(2);//act = [2 0];
act[0] = 2;
act[1] = 0;
Gain gain = new Gain(2);//gain = [1 1];
gain[0] = 1;
gain[1] = 1;
Index iw = Index.Find(ref topo);//iw = [1 4 8];
//ta sama sytuacja, indeksy od 0 startują
Assert.AreEqual(0, iw[0]);
Assert.AreEqual(3, iw[1]);
Assert.AreEqual(7, iw[2]);
Console.WriteLine("Testowanie obliczania gradientu i macierzy hesjana");
Console.WriteLine("Użyte dane:");
Console.WriteLine("\nDane wejściowe:");
Console.WriteLine(input.MatrixToString());
Console.WriteLine("\nDane wyjściowe:");
Console.WriteLine(output.MatrixToString());
Console.WriteLine("\nWagi;");
Console.WriteLine(weights.MatrixToString());
Console.WriteLine("\nTopologia:");
Console.WriteLine(topo.MatrixToString());
Console.WriteLine("\nIndeksy topologii:");
Console.WriteLine(iw.MatrixToString());
Console.WriteLine("\nFunkcje aktywacji:");
Console.WriteLine(act.MatrixToString());
Console.WriteLine("\nWzmocnienia (gains):");
Console.WriteLine(gain.MatrixToString());
Console.WriteLine("\nParametry (param):");
Console.WriteLine(info.ToString());
Hessian hess = new Hessian(ref info);
hess.Compute(ref info, ref input, ref output, ref topo, weights, ref act, ref gain, ref iw);
var g = hess.GradientMat;
var h = hess.HessianMat;
Console.WriteLine("\nSprawdzanie gradientu z dokładnością do 15 miejsc po przecinku");
var matG = new double[] { -0.839948683228052, 2.319597374905329, 2.319597374905329, -2.000000000000000, 5.523188311911530, 5.523188311911531, 6.889667569278484 };
/*
% Otrzymany gradient:
% -0.839948683228052
% 2.319597374905329
% 2.319597374905329
% -2.000000000000000
% 5.523188311911530
% 5.523188311911531
% 6.889667569278484
*/
for (int i = 0; i < matG.Length; i++)
{
Console.WriteLine(string.Format("NBN C#: {0}\tMatLab NBN: {1}\t{2}", Math.Round(g[i, 0], accuracy), matG[i], Math.Round(g[i, 0], accuracy) == matG[i] ? "OK" : "źle"));
}
Assert.AreEqual(-0.839948683228052, Math.Round(g[0, 0], accuracy));
Assert.AreEqual(2.319597374905329, Math.Round(g[1, 0], accuracy));
Assert.AreEqual(2.319597374905329, Math.Round(g[2, 0], accuracy));
Assert.AreEqual(-2.000000000000000, Math.Round(g[3, 0], accuracy));
Assert.AreEqual(5.523188311911530, Math.Round(g[4, 0], accuracy));
Assert.AreEqual(5.523188311911531, Math.Round(g[5, 0], accuracy));
Assert.AreEqual(6.889667569278484, Math.Round(g[6, 0], accuracy));
Console.WriteLine("\nSprawdzanie macierzy hesjana\nPorównania z dokładnością do 15 miejsc po przecinku");
MatrixMB matH = new MatrixMB(7, 7);
//col 1
matH[0, 0] = 1.058270685684809;
matH[1, 0] = -0.705513790456539;
matH[2, 0] = -0.705513790456539;
matH[3, 0] = 2.519846049684157;
matH[4, 0] = -1.679897366456105;
matH[5, 0] = -1.679897366456105;
matH[6, 0] = -0.639700008449225;
//col 2
matH[0, 1] = -0.705513790456539;
matH[1, 1] = 1.058270685684809;
matH[2, 1] = 0.352756895228269;
matH[3, 1] = -1.679897366456105;
matH[4, 1] = 2.519846049684157;
matH[5, 1] = 0.839948683228052;
matH[6, 1] = 1.279400016898449;
//col 3
matH[0, 2] = -0.705513790456539;
matH[1, 2] = 0.352756895228269;
matH[2, 2] = 1.058270685684809;
matH[3, 2] = -1.679897366456105;
matH[4, 2] = 0.839948683228052;
matH[5, 2] = 2.519846049684157;
matH[6, 2] = 1.279400016898449;
//col 4
matH[0, 3] = 2.519846049684157;
matH[1, 3] = -1.679897366456105;
matH[2, 3] = -1.679897366456105;
matH[3, 3] = 6.000000000000000;
matH[4, 3] = -4.000000000000000;
matH[5, 3] = -4.000000000000000;
matH[6, 3] = -1.523188311911530;
//col 5
matH[0, 4] = -1.679897366456105;
matH[1, 4] = 2.519846049684157;
matH[2, 4] = 0.839948683228052;
matH[3, 4] = -4.000000000000000;
matH[4, 4] = 6.000000000000000;
matH[5, 4] = 2.000000000000000;
matH[6, 4] = 3.046376623823059;
//col 6
matH[0, 5] = -1.679897366456105;
matH[1, 5] = 0.839948683228052;
matH[2, 5] = 2.519846049684157;
matH[3, 5] = -4.000000000000000;
matH[4, 5] = 2.000000000000000;
matH[5, 5] = 6.000000000000000;
matH[6, 5] = 3.046376623823059;
//col 7
matH[0, 6] = -0.639700008449225;
matH[1, 6] = 1.279400016898449;
matH[2, 6] = 1.279400016898449;
matH[3, 6] = -1.523188311911530;
matH[4, 6] = 3.046376623823059;
matH[5, 6] = 3.046376623823059;
matH[6, 6] = 3.480153950315843;
for (int k = 0; k < h.Cols; k++)
{
Console.WriteLine(string.Format("Kolumna {0}", k + 1));
for (int w = 0; w < h.Rows; w++)
{
decimal dh = Math.Round((decimal)h[w, k], accuracy);
decimal dmh = Math.Round((decimal)matH[w, k], accuracy);
Console.WriteLine(string.Format("NBN C#: {0}\tMatLab NBN: {1}\t{2}", dh, dmh, dh == dmh ? "OK" : "źle"));
}
Console.WriteLine("");
}
for (int k = 0; k < h.Cols; k++)
{
for (int w = 0; w < h.Rows; w++)
{
decimal dh = Math.Round((decimal)h[w, k], accuracy);
decimal dmh = Math.Round((decimal)matH[w, k], accuracy);
Assert.AreEqual(dmh, dh);
}
}
/*
% Otrzymany hesjan:
% Columns 1 through 6
%
% 1.058270685684809 -0.705513790456539 -0.705513790456539 2.519846049684157 -1.679897366456105 -1.679897366456105
% -0.705513790456539 1.058270685684809 0.352756895228269 -1.679897366456105 2.519846049684157 0.839948683228052
% -0.705513790456539 0.352756895228269 1.058270685684809 -1.679897366456105 0.839948683228052 2.519846049684157
% 2.519846049684157 -1.679897366456105 -1.679897366456105 6.000000000000000 -4.000000000000000 -4.000000000000000
% -1.679897366456105 2.519846049684157 0.839948683228052 -4.000000000000000 6.000000000000000 2.000000000000000
% -1.679897366456105 0.839948683228052 2.519846049684157 -4.000000000000000 2.000000000000000 6.000000000000000
% -0.639700008449225 1.279400016898449 1.279400016898449 -1.523188311911530 3.046376623823059 3.046376623823059
%
% Column 7
%
% -0.639700008449225
% 1.279400016898449
% 1.279400016898449
% -1.523188311911530
% 3.046376623823059
% 3.046376623823059
% 3.480153950315843
*/
}
