public void XorTest()
{
NeuralNetwork network = new NeuralNetwork(2, 1, 1, 1);
float[,] xorInput = new float[4, 2]
{
{ 0, 0 },
{ 0, 1 },
{ 1, 0 },
{ 1, 1 }
};
float[,] xorOutput = new float[4, 1]
{
{ 1 },
{ 0 },
{ 0 },
{ 1 }
};
SparseMatrix X = new SparseMatrix(SparseCompressedRowMatrixStorage <float> .OfArray(xorInput));
SparseMatrix Y = new SparseMatrix(SparseCompressedRowMatrixStorage <float> .OfArray(xorOutput));
network.LearnNetwork(X, Y, 5000, 0.001f, 0);
network.Inputs = DenseVector.OfArray(new float[2] {
0, 0
});
network.ForwardPropagation();
var test = network.GetAswer();
Assert.IsNotNull(test);
Assert.AreEqual(Math.Round(test[0]), 1);
}
public static MatrixStorage <T> MatrixStorage <T>(TestMatrixStorage type, T[,] data)
where T : struct, IEquatable <T>, IFormattable
{
switch (type)
{
case TestMatrixStorage.DenseMatrix:
return(DenseColumnMajorMatrixStorage <T> .OfArray(data));
case TestMatrixStorage.SparseMatrix:
return(SparseCompressedRowMatrixStorage <T> .OfArray(data));
case TestMatrixStorage.DiagonalMatrix:
return(DiagonalMatrixStorage <T> .OfArray(data));
default:
throw new NotSupportedException();
}
}
// Apenas um teste, não é pra ser executado pela aplicação
/*
* public void GeraImagem()
* {
* String im = ".\\SaidaProcessadaVetor.txt";
* StreamReader sr = new StreamReader(im);
*
* double[] temp = new double[3600];
* using (sr)
* {
* string line;
* int i = 0;
* while ((line = sr.ReadLine()) != null)
* {
* temp[i] = Double.Parse(line);
* i++;
* }
* }
*
* new GeraBitmap().ToBitmap(temp, im.Replace(".\\", ""));
* }
*/
/// <summary>
/// Separar essa função em 3: lê vetor, lê matriz e CGNE
/// </summary>
public void CGNE()
{
try
{
int rows = 50816;
int columns = 3600;
//var path = HttpContext.Current.Server.MapPath("~/Content/Signals/");
string hFile = path + "H-1.txt";
string gFile = path + @"Sent\" + this.filaDeProcessos.First();
double intensidade = Double.Parse(gFile.Split('#').GetValue(1).ToString());
var M = Matrix <double> .Build;
var V = Vector <double> .Build;
StreamReader sr = new StreamReader(gFile);
var g = V.Dense(rows);
/*
* Separar para função de vetor
*
*/
try
{
System.Diagnostics.Debug.WriteLine(DateTime.Now);
double[] temp = new double[rows];
using (sr)
{
string line;
int i = 0;
while ((line = sr.ReadLine()) != null)
{
if (intensidade != 1)
{
temp[i] = Double.Parse(line) * intensidade;
}
else
{
temp[i] = Double.Parse(line);
}
i++;
}
}
g = V.Dense(temp);
System.Diagnostics.Debug.WriteLine(DateTime.Now + " -> Arquivo g lido");
}
catch (Exception e)
{
System.Diagnostics.Debug.WriteLine(e.Message);
System.Diagnostics.Debug.WriteLine(e.StackTrace);
}
sr.Dispose();
sr = new StreamReader(hFile);
/*
* Separar para função de matriz
*
*/
try
{
//var f = CGNECall(hFile, rows, columns, g);
System.Diagnostics.Debug.WriteLine(DateTime.Now + " -> começou o CGNE");
Matrix <double> h;
using (StreamReader reader = new StreamReader(File.OpenRead(hFile)))
{
string line;
int i = 0;
double[,] matrixTemp = new double[rows, columns];
while ((line = sr.ReadLine()) != null)
{
string[] tokens = line.Split(',');
int j = 0;
foreach (var token in tokens)
{
matrixTemp[i, j] = Double.Parse(token);
j++;
}
i++;
}
/*
* var blasMatrix = new BlasMatrix(reader, columns);
* int i = 0;
* double[,] matrixTemp = new double[rows, columns];
* foreach (var element in blasMatrix.Records)
* {
* for (int j = 0; j < columns; j++)
* {
* matrixTemp[i, j] = element.getRow(0, j);
* }
* i++;
* }
*/
System.Diagnostics.Debug.WriteLine(DateTime.Now + "Arquivo H lido");
sr.DiscardBufferedData();
reader.DiscardBufferedData();
reader.Dispose();
sr.Dispose();
h = M.Sparse(SparseCompressedRowMatrixStorage <double> .OfArray(matrixTemp));
//h = M.DenseOfArray(matrixTemp);
/*
* Usando Dense dobra o uso de memória (é feita uma cópia de matrixTemp)
*
* h = M.Dense(DenseColumnMajorMatrixStorage<double>.OfArray(matrixTemp))
*
*/
}
sr.Dispose();
/*
* Aqui são as operações de inicialização
*/
Vector <double> f = V.Dense(columns);
Vector <double> f_aux = V.Dense(columns);
//var r = g - h.Multiply(f);
var r = g;
Vector <double> r_aux;
var hT = h.Transpose();
var p = hT.Multiply(r);
Vector <double> p_aux;
for (int i = 0; i < 15; i++)
{
var r_T = r.ToRowMatrix();
var alfa_upper = r_T.Multiply(r);
var p_T = p.ToRowMatrix();
var alfa_down = p_T.Multiply(p);
var alfa = alfa_upper.PointwiseDivide(alfa_down);
double alfa_scalar = alfa.Single();
f_aux += f.Add(p.Multiply(alfa_scalar));
f = f_aux;
r_aux = r.Subtract(h.Multiply(alfa_scalar).Multiply(p));
var r_auxT = r_aux.ToRowMatrix();
var beta_upper = r_auxT.Multiply(r_aux);
var beta = beta_upper.PointwiseDivide(alfa_upper);
System.Diagnostics.Debug.WriteLine(DateTime.Now + " -> before matrix 2");
double beta_scalar = beta.Single();
p_aux = hT.Multiply(r_aux);
var pplus = p_aux.Add(p.Multiply(beta_scalar));
p = pplus;
r = r_aux;
System.Diagnostics.Debug.WriteLine(DateTime.Now + $" -> iteração {i}");
}
string sinal = this.filaDeProcessos.First();
//COMENTAR ESSA LINHA PARA OTIMIZAR
salvaVetor(sinal, f_aux.ToArray(), @"Processed\");
// Remove da fila
this.filaDeProcessos.Remove(sinal);
new GeraBitmap().ToBitmap(f_aux.ToArray(), sinal, pathImagens);
}
catch (Exception e)
{
System.Diagnostics.Debug.WriteLine(e.Message);
System.Diagnostics.Debug.WriteLine(e.StackTrace);
}
sr.Dispose();
}
catch (Exception e)
{
System.Diagnostics.Debug.WriteLine("The file could not be read:");
System.Diagnostics.Debug.WriteLine(e.Message);
}
}
