Beispiel #1
0
 /// <summary>
 /// Uczenie warstwy wyjsciowej
 /// </summary>
 /// <param name="outputDesirableData"></param>
 private void OutputLayerLearning(List <double[]> outputDesirableData)
 {
     greenMatrix = CreateGreenMatrix();
     double[,] invertedGreenmatrix = Pseudoinverse.Solve(greenMatrix);
     for (int i = 0; i < neuronOutputLayer.Count; i++)
     {
         double[] weight = Matrix.Multiply(invertedGreenmatrix, outputDesirableData[i]);
         ((LinearNeuron)neuronOutputLayer[i]).Weights = weight;
     }
 }
Beispiel #2
0
        public static void SVDMemoryUse()
        {
            int sizeX = 4000, sizeY = 5000;

            double[,] matrix = new double[sizeX, sizeY];
            for (int i = 0; i < sizeX; i++)
            {
                for (int j = 0; j < sizeY; j++)
                {
                    matrix[i, j] = i + j;
                }
            }
            double[,] svdA = Pseudoinverse.Solve(matrix);
            Console.WriteLine("Macierz wynikowa.");
            Console.WriteLine(Matrix.ToString(svdA));
            GC.SuppressFinalize(matrix);
            GC.SuppressFinalize(svdA);
            matrix = null;
            svdA   = null;
        }
Beispiel #3
0
 public void SVDtest()
 {
     double[,] a    = StartMatrix();
     double[,] svdA = Pseudoinverse.Solve(a);
     Console.WriteLine("Macierz wynikowa.");
     Console.WriteLine(Matrix.ToString(svdA));
     svdA = Pseudoinverse.Solve(svdA);
     Console.WriteLine("Macierz po kolejnej operacji SVD wynikowa.");
     Console.WriteLine(Matrix.ToString(svdA));
     for (int i = 0; i < svdA.GetLength(0); i++)
     {
         for (int j = 0; j < svdA.GetLength(1); j++)
         {
             if (Math.Abs(svdA[i, j] - a[i, j]) > 0.05)
             {
                 Assert.IsTrue(false);
             }
         }
     }
     Assert.IsTrue(true);
 }