public void MatrixArrayConversion()
{
// start with a .NET Array
double[,] A = new double[,] {
{ 0, 1, 2 },
{ 3, 4, 5 }
};
// Convert it to a Matrix
RectangularMatrix B = new RectangularMatrix(A);
Assert.IsTrue(B.RowCount == A.GetLength(0));
Assert.IsTrue(B.ColumnCount == A.GetLength(1));
for (int r = 0; r < B.RowCount; r++) {
for (int c = 0; c < B.ColumnCount; c++) {
Assert.IsTrue(B[r, c] == A[r, c]);
}
}
// Convert that Matrix back to an Array
double[,] C = B.ToArray();
Assert.IsTrue(C.Rank == 2);
Assert.IsTrue(C.GetLength(0) == B.RowCount);
Assert.IsTrue(C.GetLength(1) == B.ColumnCount);
for (int r = 0; r < B.RowCount; r++) {
for (int c = 0; c < B.ColumnCount; c++) {
Assert.IsTrue(C[r, c] == A[r, c]);
}
}
}
static MixedMatrixTest()
{
R = new RectangularMatrix(2, 3);
R[0, 0] = 0;
R[0, 1] = 1;
R[0, 2] = 2;
R[1, 0] = 3;
R[1, 1] = 4;
R[1, 2] = 5;
M = new SquareMatrix(3);
M[0, 0] = 0;
M[0, 1] = 1;
M[0, 2] = 2;
M[1, 0] = 3;
M[1, 1] = 4;
M[1, 2] = 5;
M[2, 0] = 6;
M[2, 1] = 7;
M[2, 2] = 8;
S = new SymmetricMatrix(3);
M[0, 0] = 0;
M[0, 1] = 1;
M[0, 2] = 2;
M[1, 1] = 3;
M[1, 2] = 4;
M[2, 2] = 5;
}
private RectangularMatrix Division(RectangularMatrix first, double second)
{
RectangularMatrix divMatrix = new RectangularMatrix(first.RowCount, first.ColumnCount);
for (int i = 0; i < divMatrix.RowCount; i++)
{
for (int j = 0; j < divMatrix.ColumnCount; j++)
{
divMatrix[i, j] = first[i, j] / second;
}
}
return divMatrix;
}
public Network(int windowSize, int imagesNumber, double learningCoefficient, double maxError, int maxIterations)
{
this.windowSize = windowSize;
this.imagesNumber = imagesNumber;
this.learningCoefficient = learningCoefficient;
this.maxError = maxError;
this.maxIterations = maxIterations;
weightMatrix1 = new RectangularMatrix(imagesNumber, windowSize + imagesNumber);
CreateRandomMatrix(weightMatrix1);
weightMatrix2 = new RowVector(imagesNumber);
CreateRandomRowVector(weightMatrix2);
contextNeurons = new ColumnVector(imagesNumber);
}
// Solve a linear system using QR decomposition. This isn't really the obvious place for this algorithm, but since
// it's used by both BivariableSample and UncertainMeasurementSample, it needs to be centralized somewhere and here
// is as good a place as any. Probably it should be moved in to RectangularMatrixAlgorithms and it should
// work directly on the underlying array storage for increased performance.
internal static void SolveLinearSystem(RectangularMatrix A, ColumnVector b, out ColumnVector a, out SymmetricMatrix C)
{
Debug.Assert(A.RowCount == b.Dimension);
Debug.Assert(A.ColumnCount <= A.RowCount);
// Our problem is to "solve" A a = b, where a is the vector of coefficients. QR gives the least squares
// solution that minimizes | A a - y |.
QRDecomposition QR = A.QRDecomposition();
a = QR.Solve(b);
// The covariance matrix C = (A^T A)^{-1} = (R^T R)^{-1}. This is a matrix multiplication plus an inversion.
// A faster way is to use the direct solution
// for k = n ... 1
// C_{kk} = R_{kk}^{-1} \left[ R_{kk}^{-1} - \sum_{j=k+1}^{n} R_{kj} C_{kj} \right]
// for i = k-1 ... 1
// C_{ik} = -R_{ii}^{-1} \left[ \sum_{j=i+1}^{k} R_{ij} C_{jk} + \sum_{j=k+1}^{n} R_{ij} C_{kj} \right]
// end
// end
// This is detailed in Ake Bjorck, "Numerical Methods for Least Squares Problems", pp. 118-120
C = new SymmetricMatrix(a.Dimension);
RectangularMatrix R = QR.RMatrix;
double c; // used for storage so we don't call bounds-checking accessors each time
for (int k = C.Dimension - 1; k >= 0; k--)
{
c = 1.0 / R[k, k];;
for (int j = k + 1; j < C.Dimension; j++)
{
c -= R[k, j] * C[k, j];
}
C[k, k] = c / R[k, k];
for (int i = k - 1; i >= 0; i--)
{
c = 0.0;
for (int j = i + 1; j <= k; j++)
{
c += R[i, j] * C[j, k];
}
for (int j = k + 1; j < C.Dimension; j++)
{
c += R[i, j] * C[k, j];
}
C[i, k] = -c / R[i, i];
}
}
}
/*
* /// <summary>
* /// Multiplies any real, rectangular matrix by a column vector.
* /// </summary>
* /// <param name="A">The matrix.</param>
* /// <param name="x">The column vector.</param>
* /// <returns>The column vector Ax.</returns>
* /// <remarks>
* /// <para>Matrix multiplication is an O(N<sup>2</sup>) process.</para>
* /// </remarks>
* public static ColumnVector operator * (RectangularMatrixBase A, ColumnVector x) {
* if (A == null) throw new ArgumentNullException("A");
* if (x == null) throw new ArgumentNullException("x");
* if (A.RowCount != x.Dimension) throw new DimensionMismatchException();
* ColumnVector Ax = new ColumnVector(A.ColumnCount);
* for (int i = 0; i < A.ColumnCount; i++) {
* for (int j = 0; j < A.RowCount; j++) {
* Ax[i] += A[i, j] * x[j];
* }
* }
* return (Ax);
* }
*/
/// <summary>
/// Multiplies any real, rectangular matrix by a real constant.
/// </summary>
/// <param name="alpha">The constant.</param>
/// <param name="A">The matrix.</param>
/// <returns>The product matrix.</returns>
public static RectangularMatrix operator *(double alpha, AnyRectangularMatrix A)
{
if (A == null)
{
throw new ArgumentNullException(nameof(alpha));
}
RectangularMatrix B = new RectangularMatrix(A.RowCount, A.ColumnCount);
for (int i = 0; i < A.RowCount; i++)
{
for (int j = 0; j < A.ColumnCount; j++)
{
B[i, j] = alpha * A[i, j];
}
}
return(B);
}
//Method that calculates the calibration parameters
void calibrazione(double[] X_kinect, double[] Y_kinect, double[] Z_kinect)
{
//Grid coordinates
double[] X_Ref_B = new double[12] { -517.5, -172.5, 172.5, 517.5, -517.5, -172.5, 172.5, 517.5, -517.5, -172.5, 172.5, 517.5 };// coordinate X dei punti della griglia di riferimento (origine posta al centro della griglia stessa)
double[] Y_Ref_B = new double[12] { 345.0, 345.0, 345.0, 345.0, 0.0, 0.0, 0.0, 0.0, -345.0, -345.0, -345.0, -345.0 };// coordinate Y dei punti della griglia di riferimento (origine posta al centro della griglia stessa)
double[] Z_Ref_B = new double[12];
Sample XKINECT = new Sample(X_kinect);
Sample YKINECT = new Sample(Y_kinect);
Sample ZKINECT = new Sample(Z_kinect);
// barycenter of Kinect coordinates
double X_Bar = XKINECT.Mean;
double Y_Bar = YKINECT.Mean;
double Z_Bar = ZKINECT.Mean;
//Kinect barycentric coordinates contaneirs
double[] X_Kinect_Bar = new double[12];
double[] Y_Kinect_Bar = new double[12];
double[] Z_Kinect_Bar = new double[12];
//Kinect barycentric coordinates
for (int i = 0; i < 12; i++)
{
X_Kinect_Bar[i] = X_kinect[i] - X_Bar;
Y_Kinect_Bar[i] = Y_kinect[i] - Y_Bar;
Z_Kinect_Bar[i] = Z_kinect[i] - Z_Bar;
}
//Modello rotazione + 2 parametri di scala Yoss=AX+b oppure B=Yoss-b=AX X=B*A^-1
RectangularMatrix A = new RectangularMatrix(36, 5);
ColumnVector B = new ColumnVector(36);
ColumnVector bb = new ColumnVector(36);
ColumnVector Yoss = new ColumnVector(36);
ColumnVector X = new ColumnVector(5);
SquareMatrix X1 = new SquareMatrix(5);
ColumnVector X2 = new ColumnVector(5);
for (int i = 0; i < 12; i++)
{
Yoss[3 * i] = X_Ref_B[i];
Yoss[3 * i + 1] = Y_Ref_B[i];
Yoss[3 * i + 2] = Z_Ref_B[i];
A[3 * i, 0] = Y_Kinect_Bar[i];
A[3 * i, 1] = -Z_Kinect_Bar[i];
A[3 * i, 2] = 0.0;
A[3 * i, 3] = X_Kinect_Bar[i];
A[3 * i, 4] = 0.0;
A[3 * i + 1, 0] = -X_Kinect_Bar[i];
A[3 * i + 1, 1] = 0.0;
A[3 * i + 1, 2] = Z_Kinect_Bar[i];
A[3 * i + 1, 3] = 0.0;
A[3 * i + 1, 4] = Y_Kinect_Bar[i];
A[3 * i + 2, 0] = 0.0;
A[3 * i + 2, 1] = X_Kinect_Bar[i];
A[3 * i + 2, 2] = -Y_Kinect_Bar[i];
A[3 * i + 2, 3] = 0.0;
A[3 * i + 2, 4] = 0.0;
bb[3 * i] = X_Kinect_Bar[i];
bb[3 * i + 1] = Y_Kinect_Bar[i];
bb[3 * i + 2] = Z_Kinect_Bar[i];
B[3 * i] = X_Ref_B[i] - X_Kinect_Bar[i];
B[3 * i + 1] = Y_Ref_B[i] - Y_Kinect_Bar[i];
B[3 * i + 2] = Z_Ref_B[i] - Z_Kinect_Bar[i];
}
X1 = (SquareMatrix)(A.Transpose() * A);
X2 = (A.Transpose() * B);
X = (X1.Inverse()) * X2;
a = X[0];
b = X[1];
c = X[2];
lambdaX = X[3];
lambdaY = X[4];
this.sw9.Write("{0:#####.0000} {1:#####.0000} {2:#####.0000} {3:#####.0000} {4:#####.0000} ", a, b, c, lambdaX, lambdaY);
this.sw9.WriteLine();
}
public void PC()
{
Random rng = new Random(1);
double s = 1.0 / Math.Sqrt(2.0);
MultivariateSample MS = new MultivariateSample(2);
RectangularMatrix R = new RectangularMatrix(1000, 2);
for (int i = 0; i < 1000; i++) {
double r1 = 2.0 * rng.NextDouble() - 1.0;
double r2 = 2.0 * rng.NextDouble() - 1.0;
double x = r1 * 4.0 * s - r2 * 9.0 * s;
double y = r1 * 4.0 * s + r2 * 9.0 * s;
R[i, 0] = x; R[i, 1] = y;
MS.Add(x, y);
}
Console.WriteLine("x {0} {1}", MS.Column(0).Mean, MS.Column(0).Variance);
Console.WriteLine("y {0} {1}", MS.Column(1).Mean, MS.Column(1).Variance);
Console.WriteLine("SVD");
SingularValueDecomposition SVD = R.SingularValueDecomposition();
for (int i = 0; i < SVD.Dimension; i++) {
Console.WriteLine("{0} {1}", i, SVD.SingularValue(i));
ColumnVector v = SVD.RightSingularVector(i);
Console.WriteLine(" {0} {1}", v[0], v[1]);
}
Console.WriteLine("PCA");
PrincipalComponentAnalysis PCA = MS.PrincipalComponentAnalysis();
Console.WriteLine("Dimension = {0} Count = {1}", PCA.Dimension, PCA.Count);
for (int i = 0; i < PCA.Dimension; i++) {
PrincipalComponent PC = PCA.Component(i);
Console.WriteLine(" {0} {1} {2} {3}", PC.Index, PC.Weight, PC.VarianceFraction, PC.CumulativeVarianceFraction);
RowVector v = PC.NormalizedVector();
Console.WriteLine(" {0} {1}", v[0], v[1]);
}
// reconstruct
SquareMatrix U = SVD.LeftTransformMatrix();
SquareMatrix V = SVD.RightTransformMatrix();
double x1 = U[0, 0] * SVD.SingularValue(0) * V[0, 0] + U[0, 1] * SVD.SingularValue(1) * V[0, 1];
Console.WriteLine("x1 = {0} {1}", x1, R[0, 0]);
double y1 = U[0, 0] * SVD.SingularValue(0) * V[1, 0] + U[0, 1] * SVD.SingularValue(1) * V[1, 1];
Console.WriteLine("y1 = {0} {1}", y1, R[0, 1]);
double x100 = U[100, 0] * SVD.SingularValue(0) * V[0, 0] + U[100, 1] * SVD.SingularValue(1) * V[0, 1];
Console.WriteLine("x100 = {0} {1}", x100, R[100, 0]);
double y100 = U[100, 0] * SVD.SingularValue(0) * V[1, 0] + U[100, 1] * SVD.SingularValue(1) * V[1, 1];
Console.WriteLine("y100 = {0} {1}", y100, R[100, 1]);
ColumnVector d1 = U[0,0] * SVD.SingularValue(0) * SVD.RightSingularVector(0) +
U[0, 1] * SVD.SingularValue(1) * SVD.RightSingularVector(1);
Console.WriteLine("d1 = ({0} {1})", d1[0], d1[1]);
ColumnVector d100 = U[100, 0] * SVD.SingularValue(0) * SVD.RightSingularVector(0) +
U[100, 1] * SVD.SingularValue(1) * SVD.RightSingularVector(1);
Console.WriteLine("d100 = ({0} {1})", d100[0], d100[1]);
Console.WriteLine("compare");
MultivariateSample RS = PCA.TransformedSample();
IEnumerator<double[]> RSE = RS.GetEnumerator();
RSE.MoveNext();
double[] dv1 = RSE.Current;
Console.WriteLine("{0} {1}", dv1[0], dv1[1]);
Console.WriteLine("{0} {1}", U[0, 0], U[0, 1]);
RSE.Dispose();
}
private RectangularMatrix GenerateRandomMatrix(int rd, int cd)
{
RectangularMatrix M = new RectangularMatrix(rd, cd);
Random rng = new Random(1);
for (int r = 0; r < rd; r++) {
for (int c = 0; c < cd; c++) {
M[r,c] = 2.0 * rng.NextDouble() - 1.0;
}
}
return (M);
}
private void CreateRandomMatrix(RectangularMatrix weightMatrix)
{
Random rand = new Random();
for (int i = 0; i < weightMatrix.RowCount; i++)
{
for (int j = 0; j < weightMatrix.ColumnCount; j++)
{
weightMatrix[i, j] = rand.NextDouble() * 0.01;
}
}
}
// Method that calculates the calibration parameters
public void Calibration(double[] X_Kinect, double[] Y_Kinect, double[] Z_Kinect)
{
double[] X_Ref_B = new double[dim];
double[] Y_Ref_B = new double[dim];
double[] Z_Ref_B = new double[dim];
if (colonne_griglia_calibrazione % 2 == 0 && righe_griglia_calibrazione % 2 == 0)
{
for (int i = 0; i < righe_griglia_calibrazione; i++)
{
for (int j = 0; j < colonne_griglia_calibrazione; j++)
{
X_Ref_B[colonne_griglia_calibrazione*i + j] = (-colonne_griglia_calibrazione/2 + j)*step + step/2;
Y_Ref_B[colonne_griglia_calibrazione*i + j] = (righe_griglia_calibrazione/2 - i)*step - step/2;
}
}
}
if (colonne_griglia_calibrazione % 2 == 0 && righe_griglia_calibrazione % 2 != 0)
{
for (int i = 0; i < righe_griglia_calibrazione; i++)
{
for (int j = 0; j < colonne_griglia_calibrazione; j++)
{
X_Ref_B[colonne_griglia_calibrazione * i + j] = (-colonne_griglia_calibrazione / 2 + j) * step + step / 2;
Y_Ref_B[colonne_griglia_calibrazione * i + j] = ((righe_griglia_calibrazione-1)/ 2 - i) * step;
}
}
}
if (colonne_griglia_calibrazione % 2 != 0 && righe_griglia_calibrazione % 2 == 0)
{
for (int i = 0; i < righe_griglia_calibrazione; i++)
{
for (int j = 0; j < colonne_griglia_calibrazione; j++)
{
X_Ref_B[colonne_griglia_calibrazione*i + j] = (-(colonne_griglia_calibrazione-1)/2 + j)*step;
Y_Ref_B[colonne_griglia_calibrazione*i + j] = (righe_griglia_calibrazione/2 - i)*step - step/2;
}
}
}
if (colonne_griglia_calibrazione % 2 != 0 && righe_griglia_calibrazione % 2 != 0)
{
for (int i = 0; i < righe_griglia_calibrazione; i++)
{
for (int j = 0; j < colonne_griglia_calibrazione; j++)
{
X_Ref_B[colonne_griglia_calibrazione * i + j] = (-(colonne_griglia_calibrazione - 1) / 2 + j) * step;
Y_Ref_B[colonne_griglia_calibrazione * i + j] = ((righe_griglia_calibrazione - 1) / 2 - i) * step;
}
}
}
// Grid coordinates
for (int k = 0; k < dim; k++)
{
this.Win.sw5.WriteLine("{0:#####.0000} {1:#####.0000} {2:#####.0000} ", X_Ref_B[k], Y_Ref_B[k], Z_Ref_B[k]);
}
this.Win.sw5.WriteLine();
Sample XKINECT = new Sample(X_Kinect);
Sample YKINECT = new Sample(Y_Kinect);
Sample ZKINECT = new Sample(Z_Kinect);
// barycenter of Kinect coordinates
double X_Bar = XKINECT.Mean;
double Y_Bar = YKINECT.Mean;
double Z_Bar = ZKINECT.Mean;
//Kinect barycentric coordinates contaneirs
double[] X_Kinect_Bar = new double[dim];
double[] Y_Kinect_Bar = new double[dim];
double[] Z_Kinect_Bar = new double[dim];
//Kinect barycentric coordinates
for (int i = 0; i < dim; i++)
{
X_Kinect_Bar[i] = X_Kinect[i] - X_Bar;
Y_Kinect_Bar[i] = Y_Kinect[i] - Y_Bar;
Z_Kinect_Bar[i] = Z_Kinect[i] - Z_Bar;
this.Win.sw3.WriteLine("{0:#####.0000} {1:#####.0000} {2:#####.0000} ", X_Kinect_Bar[i], Y_Kinect_Bar[i], Z_Kinect_Bar[i]);
}
this.Win.sw3.WriteLine();
//Modello rotazione + 2 parametri di scala Yoss = AX+b oppure B = Yoss-bb = AX X = B*A^-1
RectangularMatrix A = new RectangularMatrix(dim * 3, 5);
ColumnVector B = new ColumnVector(dim * 3);
ColumnVector bb = new ColumnVector(dim * 3);
ColumnVector Yoss = new ColumnVector(dim * 3);
ColumnVector X = new ColumnVector(5);
SquareMatrix X1 = new SquareMatrix(5);
ColumnVector X2 = new ColumnVector(5);
for (int i = 0; i < dim; i++)
{
Yoss[3 * i ] = X_Ref_B[i];
Yoss[3 * i + 1] = Y_Ref_B[i];
Yoss[3 * i + 2] = Z_Ref_B[i];
A[3 * i, 0] = Y_Kinect_Bar[i];
A[3 * i, 1] = -Z_Kinect_Bar[i];
A[3 * i, 2] = 0.0;
A[3 * i, 3] = X_Kinect_Bar[i];
A[3 * i, 4] = 0.0;
A[3 * i + 1, 0] = -X_Kinect_Bar[i];
A[3 * i + 1, 1] = 0.0;
A[3 * i + 1, 2] = Z_Kinect_Bar[i];
A[3 * i + 1, 3] = 0.0;
A[3 * i + 1, 4] = Y_Kinect_Bar[i];
A[3 * i + 2, 0] = 0.0;
A[3 * i + 2, 1] = X_Kinect_Bar[i];
A[3 * i + 2, 2] = -Y_Kinect_Bar[i];
A[3 * i + 2, 3] = 0.0;
A[3 * i + 2, 4] = 0.0;
bb[3 * i] = X_Kinect_Bar[i];
bb[3 * i + 1] = Y_Kinect_Bar[i];
bb[3 * i + 2] = Z_Kinect_Bar[i];
B[3 * i] = X_Ref_B[i] - X_Kinect_Bar[i];
B[3 * i + 1] = Y_Ref_B[i] - Y_Kinect_Bar[i];
B[3 * i + 2] = Z_Ref_B[i] - Z_Kinect_Bar[i];
}
X1 = (SquareMatrix)(A.Transpose() * A);
X2 = (A.Transpose() * B);
X = (X1.Inverse()) * X2;
a = X[0];
b = X[1];
c = X[2];
lambdaX = X[3];
lambdaY = X[4];
this.Win.sw12.Write("{0:#####.0000} {1:#####.0000} {2:#####.0000} {3:#####.0000} {4:#####.0000} ", a, b, c, lambdaX, lambdaY);
this.Win.sw12.WriteLine();
this.Win.textBlock8.Text = string.Format("Calibration parameters:\na = {0} \nb = {1} \nc = {2} \nlambdaX = {3} \nlambdaY = {4}", a, b, c, lambdaX, lambdaY);
SquareMatrix ROT = new SquareMatrix(3);
ROT[0, 0] = 1 + lambdaX;
ROT[0, 1] = a;
ROT[0, 2] = -b;
ROT[1, 0] = -a;
ROT[1, 1] = 1 + lambdaY;
ROT[1, 2] = c;
ROT[2, 0] = b;
ROT[2, 1] = -c;
ROT[2, 2] = 1;
}
private void TeachNeuralNetwork(ImageRectangle[] rectangles, BackgroundWorker worker, DoWorkEventArgs doWorkEvent, bool test)
{
int N = n * m * 3;
RectangularMatrix weightMatrix = new RectangularMatrix(N, p);
Random rand = new Random();
for (int i = 0; i < N; i++)
{
for (int j = 0; j < p; j++)
{
weightMatrix[i, j] = rand.NextDouble() * 0.1;
}
}
RectangularMatrix secondWeightMatrix = weightMatrix.Transpose();
double totalError = e + 1;
int totalIterationNumber = 0;
state = new CurrentState();
RowVector[] vectors = new RowVector[rectangles.Length];
for (int i = 0; i < rectangles.Length; i++)
{
vectors[i] = new RowVector(((ImageRectangle)rectangles.GetValue(i)).GetVector());
}
while (totalError > e && totalIterationNumber < iterationNumber)
{
totalError = 0;
for (int i = 0; i < rectangles.Length; i++)
{
RowVector xVector = vectors[i];
RowVector yVector = xVector * weightMatrix;
RowVector xSecondVector = yVector * secondWeightMatrix;
RowVector deltaXVector = xSecondVector - xVector;
weightMatrix = weightMatrix - a * xVector.Transpose() * deltaXVector * secondWeightMatrix.Transpose();
secondWeightMatrix = secondWeightMatrix - a * yVector.Transpose() * deltaXVector;
}
for (int i = 0; i < rectangles.Length; i++)
{
RowVector xVector = vectors[i];
RowVector yVector = xVector * weightMatrix;
RowVector xSecondVector = yVector * secondWeightMatrix;
RowVector deltaXVector = xSecondVector - xVector;
for (int j = 0; j < deltaXVector.ColumnCount; j++)
{
totalError += deltaXVector[0, j] * deltaXVector[0, j];
}
}
totalIterationNumber++;
state.CurentError = totalError;
state.IterationNumber = totalIterationNumber;
if (!test)
{
worker.ReportProgress(0, state);
if (worker.CancellationPending)
{
doWorkEvent.Cancel = true;
break;
}
}
}
for (int i = 0; i < rectangles.Length; i++)
{
RowVector xVector = vectors[i];
RowVector yVector = xVector * weightMatrix;
RowVector xSecondVector = yVector * secondWeightMatrix;
((ImageRectangle)rectangles.GetValue(i)).SetPixelMatrixFromVector(xSecondVector.ToArray());
}
state.Compressing = CalculateCompressing(rectangles.Length, N);
state.FirstWeightMatrix = GetMatrixString(weightMatrix);
state.SecondWeightMatrix = GetMatrixString(secondWeightMatrix);
}
private string GetMatrixString(RectangularMatrix matrix)
{
StringBuilder builder = new StringBuilder();
for (int i = 0; i < matrix.RowCount; i++)
{
RowVector rowVector = matrix.Row(i);
string elements =String.Join(", ", rowVector.ToArray().Select(element => Math.Round(element, 4).ToString()).ToArray());
builder.Append("[" + elements + "]" + Environment.NewLine);
}
return builder.ToString();
}
private void LearnImage(Image image)
{
int[] vector = image.ToVector();
ColumnVector X = new ColumnVector(vector.Length);
for (int i = 0; i < X.RowCount; i++)
{
X[i] = vector[i];
}
ColumnVector b = W * X;
ColumnVector a = b - X;
RectangularMatrix first = a * a.Transpose();
double second = (X.Transpose() * X) - (X.Transpose() * W * X);
W = W + Division(first, second);
for (int i = 0; i < W.ColumnCount; i++) {
W[i, i] = 0;
}
}
private void FillMatrixWithZeros(RectangularMatrix matrix)
{
for (int i = 0; i < matrix.RowCount; i++)
{
for (int j = 0; j < matrix.ColumnCount; j++)
{
matrix[i, j] = 0.0;
}
}
}
public void Bell1()
{
RectangularMatrix A = new RectangularMatrix(new double[,] {
{1.0, 0.0, 0.0, 0.0, 2.0},
{0.0, 0.0, 3.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 0.0, 0.0},
{0.0, 4.0, 0.0, 0.0, 0.0}
});
//A = A.Transpose();
SingularValueDecomposition svd = A.SingularValueDecomposition();
Console.WriteLine("values");
for (int i = 0; i < svd.Dimension; i++) {
Console.WriteLine(svd.SingularValue(i));
}
Console.WriteLine("left");
WriteMatrix(svd.LeftTransformMatrix());
Console.WriteLine("right");
WriteMatrix(svd.RightTransformMatrix());
Console.WriteLine("product");
WriteMatrix(svd.LeftTransformMatrix().Transpose() * A * svd.RightTransformMatrix());
}
public void Bug7686()
{
// This SVD failed with a IndexOutOfBoundsException because we didn't handle SVD for cols > rows and didn't check for this condition on entry.
RectangularMatrix A = new RectangularMatrix(new double[,] {
{-418.746, 310.726, 313.969, 1},
{-418.746, 337.451, 229.786, 1},
{-305.253, 321.895, 304.895, 1}
});
var SVD = A.SingularValueDecomposition();
}
public void RectangularMatrixEquality()
{
RectangularMatrix A = new RectangularMatrix(new double[,] {
{ 1.0, 2.0, 3.0 },
{ 4.0, 5.0, 6.0 }
});
RectangularMatrix AC = A.Copy();
RectangularMatrix B = 2.0 * A;
// Equality operator
Assert.IsTrue(A == AC);
Assert.IsTrue(AC == A);
Assert.IsFalse(A == B);
Assert.IsFalse(B == A);
Assert.IsFalse(A == null);
Assert.IsFalse(null == A);
Assert.IsTrue((RectangularMatrix) null == (RectangularMatrix) null);
// Inequality operator
Assert.IsFalse(A != AC);
Assert.IsFalse(AC != A);
Assert.IsTrue(A != B);
Assert.IsTrue(B != A);
Assert.IsTrue(A != null);
Assert.IsTrue(null != A);
Assert.IsFalse((RectangularMatrix) null != (RectangularMatrix) null);
// Equals method
Assert.IsTrue(A.Equals(AC));
Assert.IsFalse(A.Equals(B));
Assert.IsFalse(A.Equals(new object()));
Assert.IsFalse(A.Equals(null));
}
private RectangularMatrix CreateScalarMatrix(double value, int size)
{
RectangularMatrix scalarMatrix = new RectangularMatrix(size, size);
for (int i = 0; i < size; i++)
{
scalarMatrix[i, i] = value;
}
return scalarMatrix;
}
public void RectangularMatrixNorms()
{
RectangularMatrix Z = new RectangularMatrix(3, 4);
Assert.IsTrue(Z.OneNorm() == 0.0);
Assert.IsTrue(Z.InfinityNorm() == 0.0);
Assert.IsTrue(Z.FrobeniusNorm() == 0.0);
RectangularMatrix A = GenerateRandomMatrix(4, 5);
Assert.IsTrue(A.OneNorm() > 0.0);
Assert.IsTrue(A.InfinityNorm() > 0.0);
Assert.IsTrue(A.FrobeniusNorm() > 0.0);
RectangularMatrix B = GenerateRandomMatrix(5, 6);
Assert.IsTrue(B.OneNorm() > 0.0);
Assert.IsTrue(B.InfinityNorm() > 0.0);
Assert.IsTrue(B.FrobeniusNorm() > 0.0);
// Frobenius norm is sub-multiplicative
RectangularMatrix P = A * B;
Assert.IsTrue(P.FrobeniusNorm() <= A.FrobeniusNorm() * B.FrobeniusNorm());
}
public void Learn(BackgroundWorker backgroundWorker, DoWorkEventArgs e, double[] sequence, bool? showIteration)
{
ColumnVector[] learningMatrix = createLearningMatrix(sequence);
double[] etalons = createEtalons(sequence);
backgroundResult = new BackgroundResult();
backgroundResult.IsComplete = false;
double totalError;
int iterations = 0;
do
{
// learn
for (int i = 0; i < learningMatrix.Length; i++)
{
ColumnVector X = ConcatVertically(learningMatrix[i], contextNeurons);
ColumnVector Xn = Normalize(X);
double norm = X.FrobeniusNorm();
ColumnVector Y1 = weightMatrix1 * Xn;
double Y2 = weightMatrix2 * Y1;
double gamma2 = (Y2 * norm) - etalons[i];
RowVector gamma1 = gamma2 * weightMatrix2;
RowVector a = learningCoefficient * gamma1;
RectangularMatrix b = Xn * a;
weightMatrix1 = weightMatrix1 - b.Transpose();
weightMatrix2 = weightMatrix2 - ((gamma2 * learningCoefficient) * Y1).Transpose();
contextNeurons = Y1;
}
totalError = 0;
// calculate total error
for (int i = 0; i < learningMatrix.Length; i++)
{
ColumnVector X = ConcatVertically(learningMatrix[i], contextNeurons);
ColumnVector Xn = Normalize(X);
double norm = X.FrobeniusNorm();
ColumnVector Y1 = weightMatrix1 * Xn;
double Y2 = weightMatrix2 * Y1;
double gamma2 = Y2 * norm - etalons[i];
totalError += Math.Pow(gamma2, 2);
}
backgroundResult.IterationNumber = iterations;
backgroundResult.Error = totalError;
backgroundResult.IsComplete = false;
if (showIteration.Equals(true))
{
backgroundWorker.ReportProgress(0, backgroundResult);
Thread.Sleep(200);
if (backgroundWorker.CancellationPending)
{
e.Cancel = true;
break;
}
}
iterations++;
}
while (totalError >= maxError && iterations <= maxIterations);
backgroundResult.IterationNumber = iterations;
backgroundResult.Error = totalError;
backgroundResult.IsComplete = true;
backgroundWorker.ReportProgress(0, backgroundResult);
}
public Network(int width, int height)
{
W = new RectangularMatrix(width * height, width * height);
FillMatrixWithZeros(W);
}
