public static void cudaTranspose(ref MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm)
{
GPGPU gpu = CudafyHost.GetDevice(eGPUType.Cuda);
GPGPUBLAS blas = GPGPUBLAS.Create(gpu);
int cols = dm.ColumnCount, rows = dm.RowCount;
int restRows = rows - cols;
//double[] a = dm.Storage.ToColumnMajorArray();
double[] a = dm.SubMatrix(0, cols, 0, cols).Storage.ToColumnMajorArray();
double[] b = dm.SubMatrix(cols, restRows, 0, cols).Storage.ToColumnMajorArray();
dm = null;
double[] a_d = gpu.CopyToDevice <double>(a);
a = null;
double[] c_d = gpu.Allocate <double>(cols * cols);
double[] x_d = gpu.CopyToDevice <double>(new double[] { 1 });
blas.GEMV(cols, cols, 1, c_d, x_d, 0, x_d, Cudafy.Maths.BLAS.Types.cublasOperation.T);
a = new double[cols * rows];
gpu.CopyFromDevice <double>(c_d, 0, a, 0, cols * cols);
gpu.FreeAll();
a_d = gpu.CopyToDevice <double>(b);
b = null;
c_d = gpu.Allocate <double>(restRows * cols);
x_d = gpu.CopyToDevice <double>(new double[] { 1 });
blas.GEMV(restRows, cols, 1, c_d, x_d, 0, x_d, Cudafy.Maths.BLAS.Types.cublasOperation.T);
gpu.CopyFromDevice <double>(c_d, 0, a, cols * cols, restRows * cols);
gpu.FreeAll();
dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(cols, rows, a);
}
public static void writeMatrixToFile(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix matrix, string file, System.Windows.Forms.RichTextBox tb1, MainForm frm)
{
System.IO.Stream fileStream = new System.IO.FileStream(file, System.IO.FileMode.Create);
System.IO.BinaryWriter bw = new System.IO.BinaryWriter(fileStream, System.Text.Encoding.Default);
double[] arr = matrix.ToColumnWiseArray();
System.IO.TextWriter tr = new System.IO.StreamWriter(file + ".metainfo");
tr.WriteLine("Column count");
tr.WriteLine(matrix.ColumnCount);
tr.WriteLine("Row count");
tr.WriteLine(matrix.RowCount);
tr.Flush();
tr.Close();
int len = arr.Length;
matrix = null;
int index = 0;
byte[] bytes = new byte[arr.Length * 8];
foreach (double item in arr)
{
BitConverter.GetBytes(item).CopyTo(bytes, index);
if (index / 8 / 500D == Math.Round(index / 8 / 500D))
{
tb1.BeginInvoke(new MainForm.setProgressDel(frm.addVal), new object[] { (int)index / 8, len, "Записано" });
}
index += 8;
}
bw.Write(bytes);
bw.Flush();
tb1.BeginInvoke(new MainForm.setProgressDel(frm.addVal), new object[] { (int)0, len, "" });
bw.Flush();
bw.Close();
fileStream.Close();
}
public static string matrixToString(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm, ReferenceSystem rs)
{
StringBuilder sb = new StringBuilder();
int index = 0;
for (int i = 0; i < 21; i++)
{
sb.AppendLine();
}
for (int i = 2; i <= rs.maxDegree; i++)
{
for (int j = 0; j <= i; j++)
{
if (j != 0)
{
sb.AppendLine(string.Format("{0}\t{1}\t{2:0.000000000000e+00}\t{3:0.000000000000e+00}", i, j, dm.Values[index], dm.Values[index + 1]));
index += 2;
}
else
{
sb.AppendLine(string.Format("{0}\t{1}\t{2:0.000000000000e+00}\t{3:0.000000000000e+00}", i, j, dm.Values[index], 0));
index += 1;
}
}
}
return(sb.ToString());
}
public static void cudaTranspose(ref MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm)
{
GPGPU gpu = CudafyHost.GetDevice(eGPUType.Cuda);
GPGPUBLAS blas = GPGPUBLAS.Create(gpu);
int cols = dm.ColumnCount, rows = dm.RowCount;
int restRows = rows - cols;
//double[] a = dm.Storage.ToColumnMajorArray();
double[] a = dm.SubMatrix(0, cols, 0, cols).Storage.ToColumnMajorArray();
double[] b = dm.SubMatrix(cols, restRows, 0, cols).Storage.ToColumnMajorArray();
dm = null;
double[] a_d = gpu.CopyToDevice<double>(a);
a = null;
double[] c_d = gpu.Allocate<double>(cols * cols);
double[] x_d = gpu.CopyToDevice<double>(new double[] { 1 });
blas.GEMV(cols, cols, 1, c_d, x_d, 0, x_d, Cudafy.Maths.BLAS.Types.cublasOperation.T);
a = new double[cols * rows];
gpu.CopyFromDevice<double>(c_d, 0, a, 0, cols * cols);
gpu.FreeAll();
a_d = gpu.CopyToDevice<double>(b);
b = null;
c_d = gpu.Allocate<double>(restRows * cols);
x_d = gpu.CopyToDevice<double>(new double[] { 1 });
blas.GEMV(restRows, cols, 1, c_d, x_d, 0, x_d, Cudafy.Maths.BLAS.Types.cublasOperation.T);
gpu.CopyFromDevice<double>(c_d, 0, a, cols * cols, restRows * cols);
gpu.FreeAll();
dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(cols, rows, a);
}
private Matrix <double> GetPointsMatrix()
{
Matrix <double> m = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(3, pointsList.Count);
for (int i = 0; i < pointsList.Count; i++)
{
m[0, i] = pointsList[i].x;
m[1, i] = pointsList[i].y;
m[2, i] = pointsList[i].z;
}
return(m);
}
public BenchmarkMatrixMultiplication()
{
matrixA = Matrix.CreateRandomMatrix(random, MATRIX_SIZE);
matrixB = Matrix.CreateRandomMatrix(random, MATRIX_SIZE);
matrixRes = new Matrix(MATRIX_SIZE);
mnMatrixA = (DenseMatrix)Matrix <double> .Build.Random(MATRIX_SIZE, MATRIX_SIZE);
mnMatrixB = (DenseMatrix)Matrix <double> .Build.Random(MATRIX_SIZE, MATRIX_SIZE);
mnMatrixRes = (DenseMatrix)Matrix <double> .Build.Dense(MATRIX_SIZE, MATRIX_SIZE);
}
public static void writeMatrixToTxtFile(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm, string file)
{
StringBuilder sb = new StringBuilder();
for (int i = 0; i < dm.RowCount; i++)
{
foreach (var item in dm.SubMatrix(i, 1, 0, dm.ColumnCount).ToColumnWiseArray())
{
sb.AppendFormat("{0,5:0.0000000000000e+00};", item);
}
sb.Append("\r\n");
}
System.IO.File.WriteAllText(file, sb.ToString());
}
public static void cudaTransposeAndMultiply(ref MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm)
{
Cudafy.CudafyModule km = Cudafy.Translator.CudafyTranslator.Cudafy();
km.Serialize();
GPGPU gpu = CudafyHost.GetDevice(eGPUType.Cuda);
int cols = dm.ColumnCount, rows = dm.RowCount;
dm.Storage.ToColumnMajorArray();
double[] a = dm.ToColumnWiseArray();
dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(1, 1);
double[] dev_a = gpu.Allocate<double>(a.Length);
GPGPUBLAS blas = GPGPUBLAS.Create(gpu);
double[] a_d = gpu.CopyToDevice<double>(a);
double[] c_d = gpu.Allocate<double>(cols * cols);
gpu.StartTimer();
blas.GEMM(cols, rows, cols, 1, a_d, a_d, 0, c_d, Cudafy.Maths.BLAS.Types.cublasOperation.T);
a = new double[cols * cols];
gpu.CopyFromDevice<double>(c_d, a);
gpu.FreeAll();
dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(cols, cols, a);
}
public static MathNet.Numerics.LinearAlgebra.Double.DenseMatrix readDenceMatrixFromBinFile(string file, MainForm.setProgressDel d)
{
string[] meta = System.IO.File.ReadAllLines(file + ".metainfo");
System.IO.FileStream fs = new System.IO.FileStream(file, System.IO.FileMode.Open);
System.IO.BinaryReader br = new System.IO.BinaryReader(fs, System.Text.Encoding.Default);
int cols = int.Parse(meta[1]);
int rows = int.Parse(meta[3]);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(rows, cols);
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
dm[i, j] = br.ReadDouble();
}
d.Invoke(br.BaseStream.Position / br.BaseStream.Length, 1, "Завантаження");
}
br.Close();
fs.Close();
return(dm);
}
public RegisteredCamera(float FocalLength, float k1, float k2, Matrix RotationMatrix, Vector3 TranslationVector, int ImageHeight, int ImageWidth)
{
this.FocalLength = FocalLength;
this.k1 = k1;
this.k2 = k2;
this.RotationMatrix = RotationMatrix;
this.TranslationVector = TranslationVector;
this.ImageHeight = ImageHeight;
this.ImageWidth = ImageWidth;
Double[,] aTranslationMatrix = new Double[3, 1];
aTranslationMatrix[0, 0] = TranslationVector.X;
aTranslationMatrix[1, 0] = TranslationVector.Y;
aTranslationMatrix[2, 0] = TranslationVector.Z;
Double[,] aRotationMatrix = new Double[3, 3];
aRotationMatrix[0, 0] = RotationMatrix.M11;
aRotationMatrix[0, 1] = RotationMatrix.M12;
aRotationMatrix[0, 2] = RotationMatrix.M13;
aRotationMatrix[1, 0] = RotationMatrix.M21;
aRotationMatrix[1, 1] = RotationMatrix.M22;
aRotationMatrix[1, 2] = RotationMatrix.M23;
aRotationMatrix[2, 0] = RotationMatrix.M31;
aRotationMatrix[2, 1] = RotationMatrix.M32;
aRotationMatrix[2, 2] = RotationMatrix.M33;
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseRotationMatrix = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(aRotationMatrix);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseTranslationMatrix = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(aTranslationMatrix);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix densePositionVector = (-(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)denseRotationMatrix.Transpose()) * denseTranslationMatrix;
PositionVector = new Vector3((float)densePositionVector[0, 0], (float)densePositionVector[1, 0], (float)densePositionVector[2, 0]);
//denseTranslationMatrix = -(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)denseRotationMatrix.Transpose() * denseTranslationMatrix;
//TranslationVector.X = (float)denseTranslationMatrix[0, 0];
//TranslationVector.Y = (float)denseTranslationMatrix[1, 0];
//TranslationVector.Z = (float)denseTranslationMatrix[2, 0];
}
private double[] Polyfit(double[] x, double[] y, int degree)
{
// Vandermonde matrix
var v = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(x.Length, degree + 1);
for (int i = 0; i < v.RowCount; i++)
{
for (int j = 0; j <= degree; j++)
{
v[i, j] = Math.Pow(x[i], j);
}
}
var yv = new MathNet.Numerics.LinearAlgebra.Double.DenseVector(y).ToColumnMatrix();
QR <double> qr = v.QR(QRMethod.Full);
// Math.Net doesn't have an "economy" QR, so:
// cut R short to square upper triangle, then recompute Q
var r = qr.R.SubMatrix(0, degree + 1, 0, degree + 1);
var q = v.Multiply(r.Inverse());
var p = r.Inverse().Multiply(q.TransposeThisAndMultiply(yv));
return(p.Column(0).ToArray());
}
public static void cudaTransposeAndMultiply(ref MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm)
{
Cudafy.CudafyModule km = Cudafy.Translator.CudafyTranslator.Cudafy();
km.Serialize();
GPGPU gpu = CudafyHost.GetDevice(eGPUType.Cuda);
int cols = dm.ColumnCount, rows = dm.RowCount;
dm.Storage.ToColumnMajorArray();
double[] a = dm.ToColumnWiseArray();
dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(1, 1);
double[] dev_a = gpu.Allocate <double>(a.Length);
GPGPUBLAS blas = GPGPUBLAS.Create(gpu);
double[] a_d = gpu.CopyToDevice <double>(a);
double[] c_d = gpu.Allocate <double>(cols * cols);
gpu.StartTimer();
blas.GEMM(cols, rows, cols, 1, a_d, a_d, 0, c_d, Cudafy.Maths.BLAS.Types.cublasOperation.T);
a = new double[cols * cols];
gpu.CopyFromDevice <double>(c_d, a);
gpu.FreeAll();
dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(cols, cols, a);
}
public RegisteredCamera(float FocalLength, float k1, float k2, Matrix RotationMatrix, Vector3 TranslationVector, int ImageHeight, int ImageWidth)
{
this.FocalLength = FocalLength;
this.k1 = k1;
this.k2 = k2;
this.RotationMatrix = RotationMatrix;
this.TranslationVector = TranslationVector;
this.ImageHeight = ImageHeight;
this.ImageWidth = ImageWidth;
Double[,] aTranslationMatrix = new Double[3, 1];
aTranslationMatrix[0, 0] = TranslationVector.X;
aTranslationMatrix[1, 0] = TranslationVector.Y;
aTranslationMatrix[2, 0] = TranslationVector.Z;
Double[,] aRotationMatrix = new Double[3, 3];
aRotationMatrix[0, 0] = RotationMatrix.M11;
aRotationMatrix[0, 1] = RotationMatrix.M12;
aRotationMatrix[0, 2] = RotationMatrix.M13;
aRotationMatrix[1, 0] = RotationMatrix.M21;
aRotationMatrix[1, 1] = RotationMatrix.M22;
aRotationMatrix[1, 2] = RotationMatrix.M23;
aRotationMatrix[2, 0] = RotationMatrix.M31;
aRotationMatrix[2, 1] = RotationMatrix.M32;
aRotationMatrix[2, 2] = RotationMatrix.M33;
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseRotationMatrix = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(aRotationMatrix);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseTranslationMatrix = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(aTranslationMatrix);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix densePositionVector = (-(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)denseRotationMatrix.Transpose()) * denseTranslationMatrix;
PositionVector = new Vector3((float)densePositionVector[0, 0], (float)densePositionVector[1, 0], (float)densePositionVector[2, 0]);
//denseTranslationMatrix = -(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)denseRotationMatrix.Transpose() * denseTranslationMatrix;
//TranslationVector.X = (float)denseTranslationMatrix[0, 0];
//TranslationVector.Y = (float)denseTranslationMatrix[1, 0];
//TranslationVector.Z = (float)denseTranslationMatrix[2, 0];
}
public void Task8()
{
using (var reader = new StreamReader(@"C:\Users\MI\Documents\labs\Numerical Methods\Numerical Methods\Extension\LU.txt"))
{
var size = Int32.Parse(reader.ReadLine());
var matrix = new double[size, size];
var vertical = new double[size];
for (int i = 0; i < size; i++)
{
var values = reader.ReadLine().Split(' ');
for (int j = 0; j < size; j++)
{
matrix[i, j] = double.Parse(values[j]);
}
}
var A = Matrix.OfArray(matrix);
var splited = reader.ReadLine().Split(' ');
for (int i = 0; i < size; i++)
{
vertical[i] = double.Parse(splited[i]);
}
var lu = A.LU();
lu.Inverse().Print("inversed");
var l = lu.L;
var u = lu.U;
var B = new Matrix(size, 1);
for (int i = 0; i < size; i++)
{
B[i, 0] = vertical[i];
}
var solved = lu.Solve(B);
l.Print("L");
u.Print("U");
solved.Print("solved");
}
}
/// <summary>
/// Find Homography.
/// </summary>
/// <param name="source"> The source Transformer. </param>
/// <param name="destination"> The destination Transformer. </param>
/// <returns> The homologous matrix. </returns>
public static Matrix3x2 FindHomography(ITransformerLTRB source, ITransformerLTRB destination)
{
float x0 = source.LeftTop.X, x1 = source.RightTop.X, x2 = source.LeftBottom.X, x3 = source.RightBottom.X;
float y0 = source.LeftTop.Y, y1 = source.RightTop.Y, y2 = source.LeftBottom.Y, y3 = source.RightBottom.Y;
float u0 = destination.LeftTop.X, u1 = destination.RightTop.X, u2 = destination.LeftBottom.X, u3 = destination.RightBottom.X;
float v0 = destination.LeftTop.Y, v1 = destination.RightTop.Y, v2 = destination.LeftBottom.Y, v3 = destination.RightBottom.Y;
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix matrix = MathNet.Numerics.LinearAlgebra.Double.DenseMatrix.OfArray(new double[008, 008]
{
{ x0, y0, 1, 0, 0, 0, -u0 * x0, -u0 * y0 },
{ 0, 0, 0, x0, y0, 1, -v0 * x0, -v0 * y0 },
{ x1, y1, 1, 0, 0, 0, -u1 * x1, -u1 * y1 },
{ 0, 0, 0, x1, y1, 1, -v1 * x1, -v1 * y1 },
{ x3, y3, 1, 0, 0, 0, -u3 * x3, -u3 * y3 },
{ 0, 0, 0, x3, y3, 1, -v3 * x3, -v3 * y3 },
{ x2, y2, 1, 0, 0, 0, -u2 * x2, -u2 * y2 },
{ 0, 0, 0, x2, y2, 1, -v2 * x2, -v2 * y2 },
});
MathNet.Numerics.LinearAlgebra.Double.DenseVector vector = new MathNet.Numerics.LinearAlgebra.Double.DenseVector(new double[008]
{
u0, v0, u1, v1,
u3, v3, u2, v2
});
MathNet.Numerics.LinearAlgebra.Matrix <double> PseudoInverse = Transformer.PseudoInverse2(matrix);
MathNet.Numerics.LinearAlgebra.Vector <double> ret = Transformer.Multiply2(PseudoInverse, vector);
return(new Matrix3x2
(
m11: (float)ret[0], m12: (float)ret[3],
m21: (float)ret[1], m22: (float)ret[4],
m31: (float)ret[2], m32: (float)ret[5]
));
}
private double[] single_correction(double[] coefs, double xdata, double ydata, double zdata)
{
double[] result = new double[3];
MathNet.Numerics.LinearAlgebra.Double.Matrix B = new MathNet.Numerics.LinearAlgebra.Double.DiagonalMatrix(3, 3, 1);
MathNet.Numerics.LinearAlgebra.Double.Matrix A = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(3, 3);
A.At(0, 0, coefs[0]);
A.At(0, 1, coefs[3]);
A.At(0, 2, coefs[4]);
A.At(1, 0, coefs[5]);
A.At(1, 1, coefs[1]);
A.At(1, 2, coefs[6]);
A.At(2, 0, coefs[7]);
A.At(2, 1, coefs[8]);
A.At(2, 2, coefs[2]);
MathNet.Numerics.LinearAlgebra.Double.Matrix B1 = Kalman_class.Matrix_Minus(B, A);
MathNet.Numerics.LinearAlgebra.Double.Matrix C = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(3, 1);
C.At(0, 0, xdata);
C.At(1, 0, ydata);
C.At(2, 0, zdata);
MathNet.Numerics.LinearAlgebra.Double.Matrix D = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(3, 1);
D.At(0, 0, coefs[9]);
D.At(1, 0, coefs[10]);
D.At(2, 0, coefs[11]);
MathNet.Numerics.LinearAlgebra.Double.Matrix res = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(3, 1);
res = Kalman_class.Matrix_Mult(B1, Kalman_class.Matrix_Minus(C, D));
result[0] = res.At(0, 0);
result[1] = res.At(1, 0);
result[2] = res.At(2, 0);
return result;
}
public void Task5()
{
// qr
Console.WriteLine("qr");
var A = Matrix.OfArray(new double[4, 4]
{
{ 8, 2, 3, 1 },
{ 2, 8, 1, 2 },
{ 1, 2, 8, 4 },
{ 1, 1, 1, 7 }
});
var B = Matrix.OfArray(new double[4, 1]
{
{ 1 },
{ 2 },
{ 2 },
{ 3 }
});
var qr = A.QR();
var q = qr.Q;
var r = qr.R;
Console.WriteLine("Q");
Console.WriteLine();
for (int i = 0; i < q.RowCount; i++)
{
for (int j = 0; j < q.ColumnCount; j++)
{
Console.Write(q[i, j] + " ");
}
Console.WriteLine();
}
Console.WriteLine("R");
Console.WriteLine();
for (int i = 0; i < r.RowCount; i++)
{
for (int j = 0; j < r.ColumnCount; j++)
{
Console.Write(r[i, j] + " ");
}
Console.WriteLine();
}
var solvedViaQr = qr.Solve(B);
Console.WriteLine("result");
for (int i = 0; i < solvedViaQr.RowCount; i++)
{
for (int j = 0; j < solvedViaQr.ColumnCount; j++)
{
Console.Write(solvedViaQr[i, j] + " ");
}
Console.WriteLine();
}
Console.WriteLine("solved via Iteration");
Console.WriteLine();
var solvedViaIteration = A.SolveViaIterationMethod(B);
for (int i = 0; i < solvedViaIteration.RowCount; i++)
{
for (int j = 0; j < solvedViaIteration.ColumnCount; j++)
{
Console.Write(solvedViaIteration[i, j] + " ");
}
Console.WriteLine();
}
}
/// <summary>
///
/// </summary>
/// <param name="p"></param>
/// <param name="q"></param>
/// <returns></returns>
public static Matrix3D Rigid3D(IList <Vector3D> p, IList <Vector3D> q)
{
int n = p.Count;
double[] m = new double[9];
for (int i = 0; i < n; i++)
{
m[0] += p[i].X * q[i].X;
m[1] += p[i].X * q[i].Y;
m[2] += p[i].X * q[i].Z;
m[3] += p[i].Y * q[i].X;
m[4] += p[i].Y * q[i].Y;
m[5] += p[i].Y * q[i].Z;
m[6] += p[i].Z * q[i].X;
m[7] += p[i].Z * q[i].Y;
m[8] += p[i].Z * q[i].Z;
}
var M = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(3, 3, m);
//double[] S = new double[3];
//double[] U = new double[9];
//double[] VT = new double[9];
//mklSolver.SingularValueDecomposition(true, M.Data, 3, 3, S, U, VT);
//var RR = new Matrix3D()
//{
// M11 = U[0] * VT[0] + U[1] * VT[3] * U[2] * VT[6],
// M12 = U[0] * VT[1] + U[1] * VT[4] * U[2] * VT[7],
// M13 = U[0] * VT[2] + U[1] * VT[5] * U[2] * VT[8],
// M21 = U[3] * VT[0] + U[4] * VT[3] * U[5] * VT[6],
// M22 = U[3] * VT[1] + U[4] * VT[4] * U[5] * VT[7],
// M23 = U[3] * VT[2] + U[4] * VT[5] * U[5] * VT[8],
// M31 = U[6] * VT[0] + U[7] * VT[3] * U[8] * VT[6],
// M32 = U[6] * VT[1] + U[7] * VT[4] * U[8] * VT[7],
// M33 = U[6] * VT[2] + U[7] * VT[5] * U[8] * VT[8],
//};
//return RR;
// R = V*UT <==> RT = VT*U
var svd = M.Svd(true);
var RT = svd.U().Multiply(svd.VT());
var R = new Matrix3D()
{
M11 = RT[0, 0],
M12 = RT[1, 0],
M13 = RT[2, 0],
M21 = RT[0, 1],
M22 = RT[1, 1],
M23 = RT[2, 1],
M31 = RT[0, 2],
M32 = RT[1, 2],
M33 = RT[2, 2],
//M11 = RT[0, 0],
//M12 = RT[0, 1],
//M13 = RT[0, 2],
//M21 = RT[1, 0],
//M22 = RT[1, 1],
//M23 = RT[1, 2],
//M31 = RT[2, 0],
//M32 = RT[2, 1],
//M33 = RT[2, 2],
M44 = 1.0,
};
return(R);
}
public Vector3 ImageToWorld(double u, double v, double w, bool transposeRot, bool negateRot, bool invertRot, bool invertZ, float rotAngleX, float rotAngleY, float rotAngleZ)
{
double xprime = (u - ((float)(ImageWidth / 2))) / FocalLength;
double yprime = (v - ((float)(ImageHeight / 2))) / FocalLength;
//double xprime = (u) / FocalLength;
//double yprime = (v) / FocalLength;
if (invertZ)
{
w = -w;
}
double x = xprime * w;
double y = yprime * w;
double z = w;
Double[,] homoXYZ = new Double[3, 1];
homoXYZ[0, 0] = x;
homoXYZ[1, 0] = y;
homoXYZ[2, 0] = z;
Double[,] TranslationMatrix = new Double[3, 1];
TranslationMatrix[0, 0] = PositionVector.X;
TranslationMatrix[1, 0] = PositionVector.Y;
TranslationMatrix[2, 0] = PositionVector.Z;
Double[,] aRotationMatrix = new Double[3, 3];
aRotationMatrix[0, 0] = RotationMatrix.M11;
aRotationMatrix[0, 1] = RotationMatrix.M12;
aRotationMatrix[0, 2] = RotationMatrix.M13;
aRotationMatrix[1, 0] = RotationMatrix.M21;
aRotationMatrix[1, 1] = RotationMatrix.M22;
aRotationMatrix[1, 2] = RotationMatrix.M23;
aRotationMatrix[2, 0] = RotationMatrix.M31;
aRotationMatrix[2, 1] = RotationMatrix.M32;
aRotationMatrix[2, 2] = RotationMatrix.M33;
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseHomoXYZ = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(homoXYZ);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseTranslationMatrix = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(TranslationMatrix);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseRotationMatrix = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(aRotationMatrix);
if (transposeRot)
{
denseRotationMatrix = (MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)denseRotationMatrix.Transpose();
}
if (invertRot)
{
denseRotationMatrix = (MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)denseRotationMatrix.Inverse();
}
denseRotationMatrix = (MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)denseRotationMatrix.Inverse();
if (negateRot)
{
denseRotationMatrix = -denseRotationMatrix;
}
//Double[,] TranslationMatrix = new Double[3, 1];
//TranslationMatrix[0, 0] = TranslationVector.X;
//TranslationMatrix[1, 0] = TranslationVector.Y;
//TranslationMatrix[2, 0] = TranslationVector.Z;
//denseRotationMatrix = denseRotationMatrix.Transpose() *
//MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseWorldCoordinates = InverseRotationMatrix * (denseHomoXYZ - TranslationMatrix);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseWorldCoordinates = (MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)(denseRotationMatrix) * (denseHomoXYZ - denseTranslationMatrix);
float worldX = (float)denseWorldCoordinates[0, 0];
float worldY = (float)denseWorldCoordinates[1, 0];
float worldZ = (float)denseWorldCoordinates[2, 0];
//Microsoft.Xna.Framework.Matrix rotX;
//Microsoft.Xna.Framework.Matrix rotY;
//Microsoft.Xna.Framework.Matrix rotZ;
//Vector3 origin = new Vector3(0, 0, 0);
//Microsoft.Xna.Framework.Matrix.CreateRotationX((float)rotAngleX, out rotX);
//Microsoft.Xna.Framework.Matrix.CreateRotationY((float)rotAngleY, out rotY);
//Microsoft.Xna.Framework.Matrix.CreateRotationZ((float)rotAngleZ, out rotZ);
//Vector3 tempV = new Vector3((float)worldX, (float)worldY, (float)worldZ);
//tempV = Vector3.Transform(tempV, rotX);
//tempV = Vector3.Transform(tempV, rotY);
//tempV = Vector3.Transform(tempV, rotZ);
//worldX = tempV.X;
//worldY = tempV.Y;
//worldZ = tempV.Z;
Vector3 WorldCoodrinates = new Vector3(worldX, worldY, worldZ);
return WorldCoodrinates;
}
private void button4_Click(object sender, EventArgs e)
{
Action fileProc = () =>
{
System.Diagnostics.Stopwatch sw = new System.Diagnostics.Stopwatch();
MathNet.Numerics.Control.LinearAlgebraProvider = new MathNet.Numerics.Algorithms.LinearAlgebra.Mkl.MklLinearAlgebraProvider();
MathNet.Numerics.Control.NumberOfParallelWorkerThreads = Environment.ProcessorCount;
addText("Обробка файлу вимірювань...\r\n");
double[][] SGG_data = null;
if (System.IO.File.Exists("sgg_data.bin"))
{
SGG_data = IOFunc.binLoad_SGG_data("sgg_data.bin");
}
else
{
SGG_data = Data.IOFunc.read_SGG_data(SphericalHarmonicAnalyze.Properties.Settings.Default.SGG_measures, new setProgressDel(addVal));
IOFunc.binwrite_SGG_data("sgg_data.bin", SGG_data);
}
addText("Дані вимірювань оброблено: {0} шт.\r\n", SGG_data.Length); Thread.Sleep(500);
ReferenceSystem elipsoid = new ReferenceSystem(ReferenceSystem.Default.TideFree);
elipsoid.gridParameters.cellSize = SphericalHarmonicAnalyze.Properties.Settings.Default.GridCellSize;
elipsoid.gridParameters.coLatitudeBounds = SphericalHarmonicAnalyze.Properties.Settings.Default.minCoLatitude;
elipsoid.maxDegree = SphericalHarmonicAnalyze.Properties.Settings.Default.modelMaxOrder;
int greedColumnsCount, greedRowsCount;
List <double[]> greed = MathFunc.generateGrid(elipsoid.gridParameters.cellSize, out greedColumnsCount, out greedRowsCount, elipsoid.gridParameters.coLatitudeBounds, 180 - elipsoid.gridParameters.coLatitudeBounds);
addText("Сітку згенеровано: {0} комірок \r\n", greed.Count);
double avgR = MathFunc.getAvgRadius(SGG_data);
List <int>[] map = MathFunc.getMappingOfPoints(elipsoid, SGG_data, greed.ToArray(), greedRowsCount, greedColumnsCount, avgR); sw.Stop(); addText("Точки віднесено до комірок сітки за: {0}.\r\n", sw.Elapsed.ToString());
addText("Кількість клітинок сітки всього: {0}\r\n", greed.Count);
int res1 = 0; foreach (var item in map)
{
res1 += item.Count;
}
addText("Використано вимірів: {0}\r\nСер радіус: {1}\r\n", res1, avgR);
test.checkMap(SGG_data, map, greed, elipsoid);
List <int>[] newMap = null;
MathFunc.checkGreed(ref greed, map, out newMap);
addText("Кількість клітинок сітки, в яких присутні дані вимірювань: {0}\r\n", greed.Count);
map = newMap; newMap = null;
IOFunc.writeGreedToCsvFileWithMeasureCount(greed, map, "greed_new_map.txt");
double[] avgRadius; sw.Restart();
double[] regularisedValues = MathFunc.regularization(SGG_data, greed.ToArray(), map, out avgRadius); sw.Stop(); addText("Регуляризація (на основі сферичної відстані) виконана за: {0}.\r\n", sw.Elapsed.ToString());
IOFunc.writeGreedToCsvFileWithMeasureS(greed, regularisedValues, "greed_regular_grad.txt");
avgRadius[0] = Math.Round(avgRadius[0]);
elipsoid.satelliteSphere = avgRadius[0];
addText("Середній радіус: {0,10:0.000}.\r\nМінімальний радіус: {1,10:0.0000}\r\nМаксимальний радіус:{2,10:0.0000}\r\n", avgRadius[0], avgRadius[1], avgRadius[2]);
SGG_data = null; map = null;
int[][] t_nm = MathFunc.get_nm(elipsoid.maxDegree);
sw.Restart();
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(greed.Count, (MathFunc.getArraySize(elipsoid.maxDegree) - 3) * 2 - (elipsoid.maxDegree - 1));
sw.Stop(); addText("Пам'ять для матриці коефіцієнтів виділено за: {0}.\r\n", sw.Elapsed.ToString());
sw.Restart();
int progress = 0;
//Обчислення елементів матриці
var p = Parallel.For(0, dm.RowCount, (i) =>
{
double[] line = MathFunc.getCoefMatrixLineKoop(elipsoid, elipsoid.maxDegree, t_nm, elipsoid.satelliteSphere, greed[i][0], greed[i][1]);
lock (dm)
{
dm.SetRow(i, line);
}
progress++;
if (progress / 100D == Math.Round(progress / 100D))
{
addVal(progress, dm.RowCount, "Визначено");
}
});
if (!p.IsCompleted)
{
throw new Exception("Parallel.For");
}
;
IOFunc.writeMatrixToMatLabFile(dm, @"matlab\A.mat", "A");
sw.Stop();
richTextBox1.Invoke(new setProgressDel(addVal), new object[] { 0, dm.RowCount, "" });
addText("Матриця {0} на {1} ({2}MB) згенерована за: {3,10}\r\n", dm.RowCount, dm.ColumnCount, dm.ColumnCount * dm.RowCount * 8 / 1000000, sw.Elapsed.ToString() /* + "\r\nЗапис у файл...\r\n"*/);
if (true)
{
GravityModel gm08 = new GravityModel(elipsoid.maxDegree);
gm08.loadFromFile("GO_CONS_EGM_GCF_2.gfc", new setProgressDel(addVal));
MathNet.Numerics.LinearAlgebra.Double.DenseVector dmL = new MathNet.Numerics.LinearAlgebra.Double.DenseVector(gm08.getGradientForGrid(elipsoid, greed));//regularisedValues);
MathNet.Numerics.LinearAlgebra.Double.DenseVector dmL2;
GravityModel gm = new GravityModel(elipsoid.maxDegree);
if (radioButton1.Checked)
{
sw.Restart();
gm.loadFromFile(SphericalHarmonicAnalyze.Properties.Settings.Default.inGravityModel, new setProgressDel(addVal));
sw.Stop(); addText("Вихідна модель завантажена за: {0}.\r\n", sw.Elapsed.ToString());
sw.Restart();
dmL2 = new MathNet.Numerics.LinearAlgebra.Double.DenseVector(gm.getGradientForGrid(elipsoid, greed));
sw.Stop(); addText("Градієнти за вихідною моделлю обчислені для сітки за: {0}.\r\n", sw.Elapsed.ToString());
}
else
{
sw.Restart();
gm = GravityModel.getNormalModel(elipsoid, elipsoid.maxDegree);
dmL2 = new MathNet.Numerics.LinearAlgebra.Double.DenseVector(gm.getGradientForGrid(elipsoid, greed));
sw.Stop(); addText("Нормальні градієнти обчислені для сітки за: {0}.\r\n", sw.Elapsed.ToString());
}
dmL = dmL - dmL2;
dmL2 = null;
IOFunc.writeMatrixToMatLabFile(dmL.ToColumnMatrix(), @"matlab\L.mat", "L");
sw.Restart();
MathNet.Numerics.LinearAlgebra.Double.DenseVector dmLNormal = null;
dmLNormal = (MathNet.Numerics.LinearAlgebra.Double.DenseVector)dm.TransposeThisAndMultiply(dmL);
dmL = null;
IOFunc.writeMatrixToMatLabFile(dmLNormal.ToColumnMatrix(), @"matlab\LNorm.mat", "LNorm");
sw.Stop(); addText("Стовпчик вільних членів обчислений за: {0}.\r\n", sw.Elapsed.ToString());
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dmANorm = null;
sw.Restart();
dmANorm = (MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)dm.TransposeThisAndMultiply(dm); dm = null;
sw.Stop(); addText("Нормальна матриця коефіціэнтів обчислена за: {0}.\r\n", sw.Elapsed.ToString());
IOFunc.writeMatrixToMatLabFile(dmANorm, @"matlab\ANorm.mat", "ANorm");
//dmLNormal = (MathNet.Numerics.LinearAlgebra.Double.DenseVector)dmLNormal.Multiply(5e-8);
var x = dmANorm.Inverse();
var res = (MathNet.Numerics.LinearAlgebra.Double.DenseVector)x.Multiply(dmLNormal);
IOFunc.writeModeVectorlToTxtFile(res, elipsoid, @"matlab\_out.AL");
addText(@"Результат за методом A\L знайдено...");
x = null;
GravityModel gm_R = new GravityModel(gm);
gm_R.addDeltaCoef(res.ToArray()); res = null;
double[] h = GravityModel.getGeoidHeight(elipsoid, gm_R, greed);
double[] dg = GravityModel.getAnomaly(elipsoid, gm_R, greed);
IOFunc.writeGeoidHeightsAndAnomalysToTxt(greed, h, dg, elipsoid, @"output\result_AL.txt");
IOFunc.writeGravityModelToTxtFile(gm_R, @"output\model_AL.gcf");
sw.Restart();
addText(dmANorm.Rank().ToString() + "\r\n");
dmANorm = null;
dmLNormal = null;
sw.Stop(); addText("Невідомі знайдено за: {0}.\r\n", sw.Elapsed.ToString());
}
};
if (System.IO.File.Exists(SphericalHarmonicAnalyze.Properties.Settings.Default.inGravityModel))
{
tabControl1.SelectedTab = tabControl1.TabPages[1];
this.UseWaitCursor = true;
ts = new CancellationTokenSource();
ct = ts.Token;
tsk = Task.Factory.StartNew(fileProc, ct);
var setCur = Task.Factory.StartNew(() => { tsk.Wait(); this.UseWaitCursor = false; addText("Обчислення завершені!"); });
richTextBox1.SaveFile(@"output\zvit.rtf");
}
}
public double Determinant()
{
if (!IsMatrixQuadratic())
{
throw new Exception("Für die Berechnung der Determinante muss die Matrix quadratisch sein!");
}
//Math.net Library
MathNetLAGen.Matrix<double> netMatrix = new MathNetLA.Double.DenseMatrix(ToArray());
return netMatrix.Determinant();
}
public Vector3 ImageToWorld(double u, double v, double w, bool transposeRot, bool negateRot, bool invertRot, bool invertZ, float rotAngleX, float rotAngleY, float rotAngleZ)
{
double xprime = (u - ((float)(ImageWidth / 2))) / FocalLength;
double yprime = (v - ((float)(ImageHeight / 2))) / FocalLength;
//double xprime = (u) / FocalLength;
//double yprime = (v) / FocalLength;
if (invertZ)
{
w = -w;
}
double x = xprime * w;
double y = yprime * w;
double z = w;
Double[,] homoXYZ = new Double[3, 1];
homoXYZ[0, 0] = x;
homoXYZ[1, 0] = y;
homoXYZ[2, 0] = z;
Double[,] TranslationMatrix = new Double[3, 1];
TranslationMatrix[0, 0] = PositionVector.X;
TranslationMatrix[1, 0] = PositionVector.Y;
TranslationMatrix[2, 0] = PositionVector.Z;
Double[,] aRotationMatrix = new Double[3, 3];
aRotationMatrix[0, 0] = RotationMatrix.M11;
aRotationMatrix[0, 1] = RotationMatrix.M12;
aRotationMatrix[0, 2] = RotationMatrix.M13;
aRotationMatrix[1, 0] = RotationMatrix.M21;
aRotationMatrix[1, 1] = RotationMatrix.M22;
aRotationMatrix[1, 2] = RotationMatrix.M23;
aRotationMatrix[2, 0] = RotationMatrix.M31;
aRotationMatrix[2, 1] = RotationMatrix.M32;
aRotationMatrix[2, 2] = RotationMatrix.M33;
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseHomoXYZ = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(homoXYZ);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseTranslationMatrix = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(TranslationMatrix);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseRotationMatrix = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(aRotationMatrix);
if (transposeRot)
{
denseRotationMatrix = (MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)denseRotationMatrix.Transpose();
}
if (invertRot)
{
denseRotationMatrix = (MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)denseRotationMatrix.Inverse();
}
denseRotationMatrix = (MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)denseRotationMatrix.Inverse();
if (negateRot)
{
denseRotationMatrix = -denseRotationMatrix;
}
//Double[,] TranslationMatrix = new Double[3, 1];
//TranslationMatrix[0, 0] = TranslationVector.X;
//TranslationMatrix[1, 0] = TranslationVector.Y;
//TranslationMatrix[2, 0] = TranslationVector.Z;
//denseRotationMatrix = denseRotationMatrix.Transpose() *
//MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseWorldCoordinates = InverseRotationMatrix * (denseHomoXYZ - TranslationMatrix);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix denseWorldCoordinates = (MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)(denseRotationMatrix) * (denseHomoXYZ - denseTranslationMatrix);
float worldX = (float)denseWorldCoordinates[0, 0];
float worldY = (float)denseWorldCoordinates[1, 0];
float worldZ = (float)denseWorldCoordinates[2, 0];
//Microsoft.Xna.Framework.Matrix rotX;
//Microsoft.Xna.Framework.Matrix rotY;
//Microsoft.Xna.Framework.Matrix rotZ;
//Vector3 origin = new Vector3(0, 0, 0);
//Microsoft.Xna.Framework.Matrix.CreateRotationX((float)rotAngleX, out rotX);
//Microsoft.Xna.Framework.Matrix.CreateRotationY((float)rotAngleY, out rotY);
//Microsoft.Xna.Framework.Matrix.CreateRotationZ((float)rotAngleZ, out rotZ);
//Vector3 tempV = new Vector3((float)worldX, (float)worldY, (float)worldZ);
//tempV = Vector3.Transform(tempV, rotX);
//tempV = Vector3.Transform(tempV, rotY);
//tempV = Vector3.Transform(tempV, rotZ);
//worldX = tempV.X;
//worldY = tempV.Y;
//worldZ = tempV.Z;
Vector3 WorldCoodrinates = new Vector3(worldX, worldY, worldZ);
return(WorldCoodrinates);
}
public static void ReceiveMessage()
{
while (true)
{
try
{
byte[] data = new byte[64];
StringBuilder builder = new StringBuilder();
int bytes = 0;
do
{
bytes = stream.Read(data, 0, data.Length);
builder.Append(Encoding.Unicode.GetString(data, 0, bytes));
}while (stream.DataAvailable);
string message = builder.ToString();
serverMessage sm = JsonConvert.DeserializeObject <serverMessage>(message);
switch (sm.type)
{
case "messageerror":
Console.WriteLine("Ошибка, сообщение невозможно отправить: " + sm.data);
break;
case "usersonline":
string[] usersList = sm.data.Split('|');
Console.WriteLine("Список пользователей онлайн:");
for (int i = 1; i <= usersList.Length; i++)
{
Console.WriteLine(i.ToString() + ". " + usersList[i - 1]);
}
break;
case "rejectconnection":
Console.WriteLine("Невозможно установить соединение: " + sm.data);
break;
case "connections":
string[] onlineConnections = sm.data.Split('|');
if (onlineConnections[0] == "")
{
Console.WriteLine("Подключения отсутствуют");
break;
}
Console.WriteLine("Текущие подключения:");
for (int i = 0; i < onlineConnections.Length; i++)
{
Console.WriteLine(i + ". " + onlineConnections[i]);
}
break;
case "alreadydisconnected":
Console.WriteLine("Пользователь " + sm.data + " не подключен");
break;
case "removeconnection":
Console.WriteLine("Пользователь " + sm.data + " отключен");
break;
case "connectionremoved":
Console.WriteLine("Пользователь " + sm.data + " отключился");
break;
case "acceptconnection":
Console.WriteLine("Пользователь " + sm.data + " устанавливает соединение...");
clientMessage cm = new clientMessage()
{
type = "accept", data = sm.data
};
string json = JsonConvert.SerializeObject(cm);
Console.WriteLine("JSON : " + json);
sendMessage(json);
break;
case "accept":
string[] keys = sm.data.Split('|');
string mySecretKey = keys[1];
string companionPublicKey = keys[2];
string[] mscdataString = mySecretKey.Split(' ');
string[] cpkdataString = companionPublicKey.Split(' ');
double[] mscdata = new double[mscdataString.Length];
double[] cpkdata = new double[cpkdataString.Length];
for (int i = 0; i < mscdataString.Length; i++)
{
mscdata[i] = Int32.Parse(mscdataString[i]);
cpkdata[i] = Int32.Parse(cpkdataString[i]);
}
Matrix <double> msc = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(mscdata.Length, 1, mscdata);
Matrix <double> cpk = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(cpkdata.Length, 1, cpkdata);
//Console.WriteLine("MY SEC: " + msc.ToString());
//Console.WriteLine("IT PUB: " + cpk.ToString());
msc = msc.Transpose();
Matrix <double> sKey = msc.Multiply(cpk);
sKey = sKey.Modulus(50);
commonSecretKey = (int)sKey[0, 0];
//Console.WriteLine("RES: " + Convert.ToString(commonSecretKey));
string Digits = "123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz";
byte[] data123 = BitConverter.GetBytes(commonSecretKey);
BigInteger intData = 0;
for (int i = 0; i < data123.Length; i++)
{
intData = intData * 256 + data123[i];
}
string result = "";
while (intData > 0)
{
int remainder = (int)(intData % 58);
intData /= 58;
result = Digits[remainder] + result;
}
for (int i = 0; i < data123.Length && data123[i] == 0; i++)
{
result = '1' + result;
}
csk = result;
Console.WriteLine("СГЕНЕРИРОВАН СЕКРЕТНЫЙ КЛЮЧ: " + csk);
Console.WriteLine("Установлена сессия с пользователем " + sm.name);
RC4Cipher newCipher = new RC4Cipher(csk, 19);
session newSession = new session()
{
name = sm.name, cipher = newCipher
};
addSession(newSession);
break;
default:
string fp = sm.type.Split('|')[0];
string sp = sm.type.Split('|')[1];
session rs = GetSession(sp);
if (fp == "message")
{
Console.WriteLine(sp + "(шифрованное): " + sm.data);
string decodemessage = rs.cipher.encode(sm.data);
Console.WriteLine(sp + "(дешифрованное): " + decodemessage);
}
break;
}
}
catch (Exception e)
{
Console.WriteLine("Подключение прервано! " + e);
Console.ReadLine();
Disconnect();
}
}
}
private void button4_Click(object sender, EventArgs e)
{
Action fileProc = () =>
{
System.Diagnostics.Stopwatch sw = new System.Diagnostics.Stopwatch();
MathNet.Numerics.Control.LinearAlgebraProvider = new MathNet.Numerics.Algorithms.LinearAlgebra.Mkl.MklLinearAlgebraProvider();
MathNet.Numerics.Control.NumberOfParallelWorkerThreads = Environment.ProcessorCount;
addText("Обробка файлу вимірювань...\r\n");
double[][] SGG_data = null;
if (System.IO.File.Exists("sgg_data.bin"))
{
SGG_data = IOFunc.binLoad_SGG_data("sgg_data.bin");
}
else
{
SGG_data = Data.IOFunc.read_SGG_data(SphericalHarmonicAnalyze.Properties.Settings.Default.SGG_measures, new setProgressDel(addVal));
IOFunc.binwrite_SGG_data("sgg_data.bin", SGG_data);
}
addText("Дані вимірювань оброблено: {0} шт.\r\n", SGG_data.Length); Thread.Sleep(500);
ReferenceSystem elipsoid = new ReferenceSystem(ReferenceSystem.Default.TideFree);
elipsoid.gridParameters.cellSize = SphericalHarmonicAnalyze.Properties.Settings.Default.GridCellSize;
elipsoid.gridParameters.coLatitudeBounds = SphericalHarmonicAnalyze.Properties.Settings.Default.minCoLatitude;
elipsoid.maxDegree = SphericalHarmonicAnalyze.Properties.Settings.Default.modelMaxOrder;
int greedColumnsCount, greedRowsCount;
List<double[]> greed = MathFunc.generateGrid(elipsoid.gridParameters.cellSize, out greedColumnsCount, out greedRowsCount, elipsoid.gridParameters.coLatitudeBounds,180 - elipsoid.gridParameters.coLatitudeBounds);
addText("Сітку згенеровано: {0} комірок \r\n", greed.Count);
double avgR = MathFunc.getAvgRadius(SGG_data);
List<int>[] map = MathFunc.getMappingOfPoints(elipsoid, SGG_data, greed.ToArray(), greedRowsCount, greedColumnsCount, avgR); sw.Stop(); addText("Точки віднесено до комірок сітки за: {0}.\r\n", sw.Elapsed.ToString());
addText("Кількість клітинок сітки всього: {0}\r\n", greed.Count);
int res1 = 0; foreach (var item in map) { res1 += item.Count; } addText("Використано вимірів: {0}\r\nСер радіус: {1}\r\n", res1, avgR);
test.checkMap(SGG_data, map, greed, elipsoid);
List<int>[] newMap = null;
MathFunc.checkGreed(ref greed, map, out newMap);
addText("Кількість клітинок сітки, в яких присутні дані вимірювань: {0}\r\n", greed.Count);
map = newMap; newMap = null;
IOFunc.writeGreedToCsvFileWithMeasureCount(greed, map, "greed_new_map.txt");
double[] avgRadius; sw.Restart();
double[] regularisedValues = MathFunc.regularization(SGG_data, greed.ToArray(), map, out avgRadius); sw.Stop(); addText("Регуляризація (на основі сферичної відстані) виконана за: {0}.\r\n", sw.Elapsed.ToString());
IOFunc.writeGreedToCsvFileWithMeasureS(greed,regularisedValues, "greed_regular_grad.txt");
avgRadius[0] = Math.Round(avgRadius[0]);
elipsoid.satelliteSphere = avgRadius[0];
addText("Середній радіус: {0,10:0.000}.\r\nМінімальний радіус: {1,10:0.0000}\r\nМаксимальний радіус:{2,10:0.0000}\r\n", avgRadius[0], avgRadius[1], avgRadius[2]);
SGG_data = null; map = null;
int[][] t_nm = MathFunc.get_nm(elipsoid.maxDegree);
sw.Restart();
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(greed.Count, (MathFunc.getArraySize(elipsoid.maxDegree) - 3) * 2 - (elipsoid.maxDegree-1));
sw.Stop(); addText("Пам'ять для матриці коефіцієнтів виділено за: {0}.\r\n", sw.Elapsed.ToString());
sw.Restart();
int progress = 0;
//Обчислення елементів матриці
var p= Parallel.For(0, dm.RowCount, (i) =>
{
double[] line = MathFunc.getCoefMatrixLineKoop(elipsoid, elipsoid.maxDegree, t_nm, elipsoid.satelliteSphere, greed[i][0], greed[i][1]);
lock (dm)
{
dm.SetRow(i,line);
}
progress++;
if (progress / 100D == Math.Round(progress / 100D)) {addVal(progress, dm.RowCount, "Визначено");}
});
if (!p.IsCompleted) { throw new Exception("Parallel.For"); };
IOFunc.writeMatrixToMatLabFile(dm, @"matlab\A.mat","A");
sw.Stop();
richTextBox1.Invoke(new setProgressDel(addVal), new object[] { 0, dm.RowCount, "" });
addText("Матриця {0} на {1} ({2}MB) згенерована за: {3,10}\r\n", dm.RowCount, dm.ColumnCount, dm.ColumnCount * dm.RowCount * 8 / 1000000,sw.Elapsed.ToString()/* + "\r\nЗапис у файл...\r\n"*/);
if(true){
GravityModel gm08 = new GravityModel(elipsoid.maxDegree);
gm08.loadFromFile("GO_CONS_EGM_GCF_2.gfc", new setProgressDel(addVal));
MathNet.Numerics.LinearAlgebra.Double.DenseVector dmL = new MathNet.Numerics.LinearAlgebra.Double.DenseVector(gm08.getGradientForGrid(elipsoid,greed));//regularisedValues);
MathNet.Numerics.LinearAlgebra.Double.DenseVector dmL2;
GravityModel gm = new GravityModel(elipsoid.maxDegree);
if (radioButton1.Checked) {
sw.Restart();
gm.loadFromFile(SphericalHarmonicAnalyze.Properties.Settings.Default.inGravityModel, new setProgressDel(addVal));
sw.Stop(); addText("Вихідна модель завантажена за: {0}.\r\n", sw.Elapsed.ToString());
sw.Restart();
dmL2 = new MathNet.Numerics.LinearAlgebra.Double.DenseVector(gm.getGradientForGrid(elipsoid,greed));
sw.Stop(); addText("Градієнти за вихідною моделлю обчислені для сітки за: {0}.\r\n", sw.Elapsed.ToString());
}
else
{
sw.Restart();
gm = GravityModel.getNormalModel(elipsoid, elipsoid.maxDegree);
dmL2 = new MathNet.Numerics.LinearAlgebra.Double.DenseVector(gm.getGradientForGrid(elipsoid, greed));
sw.Stop(); addText("Нормальні градієнти обчислені для сітки за: {0}.\r\n", sw.Elapsed.ToString());
}
dmL = dmL - dmL2;
dmL2 = null;
IOFunc.writeMatrixToMatLabFile(dmL.ToColumnMatrix(), @"matlab\L.mat", "L");
sw.Restart();
MathNet.Numerics.LinearAlgebra.Double.DenseVector dmLNormal = null;
dmLNormal = (MathNet.Numerics.LinearAlgebra.Double.DenseVector)dm.TransposeThisAndMultiply(dmL);
dmL = null;
IOFunc.writeMatrixToMatLabFile(dmLNormal.ToColumnMatrix(), @"matlab\LNorm.mat", "LNorm");
sw.Stop(); addText("Стовпчик вільних членів обчислений за: {0}.\r\n", sw.Elapsed.ToString());
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dmANorm = null;
sw.Restart();
dmANorm = (MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)dm.TransposeThisAndMultiply(dm); dm = null;
sw.Stop(); addText("Нормальна матриця коефіціэнтів обчислена за: {0}.\r\n", sw.Elapsed.ToString());
IOFunc.writeMatrixToMatLabFile(dmANorm, @"matlab\ANorm.mat", "ANorm");
//dmLNormal = (MathNet.Numerics.LinearAlgebra.Double.DenseVector)dmLNormal.Multiply(5e-8);
var x = dmANorm.Inverse();
var res = (MathNet.Numerics.LinearAlgebra.Double.DenseVector)x.Multiply(dmLNormal);
IOFunc.writeModeVectorlToTxtFile(res, elipsoid, @"matlab\_out.AL");
addText(@"Результат за методом A\L знайдено...");
x = null;
GravityModel gm_R = new GravityModel(gm);
gm_R.addDeltaCoef(res.ToArray()); res = null;
double[] h = GravityModel.getGeoidHeight(elipsoid, gm_R, greed);
double[] dg = GravityModel.getAnomaly(elipsoid, gm_R, greed);
IOFunc.writeGeoidHeightsAndAnomalysToTxt(greed, h, dg, elipsoid, @"output\result_AL.txt");
IOFunc.writeGravityModelToTxtFile(gm_R, @"output\model_AL.gcf");
sw.Restart();
addText(dmANorm.Rank().ToString() + "\r\n");
dmANorm = null;
dmLNormal = null;
sw.Stop(); addText("Невідомі знайдено за: {0}.\r\n", sw.Elapsed.ToString());
}
};
if (System.IO.File.Exists(SphericalHarmonicAnalyze.Properties.Settings.Default.inGravityModel)) {
tabControl1.SelectedTab = tabControl1.TabPages[1];
this.UseWaitCursor = true;
ts = new CancellationTokenSource();
ct = ts.Token;
tsk = Task.Factory.StartNew(fileProc,ct);
var setCur = Task.Factory.StartNew(() => { tsk.Wait(); this.UseWaitCursor = false; addText("Обчислення завершені!"); });
richTextBox1.SaveFile(@"output\zvit.rtf");
}
}
public static MathNet.Numerics.LinearAlgebra.Double.DenseMatrix readDenceMatrixFromBinFile(string file,MainForm.setProgressDel d)
{
string[] meta = System.IO.File.ReadAllLines(file + ".metainfo");
System.IO.FileStream fs = new System.IO.FileStream(file,System.IO.FileMode.Open);
System.IO.BinaryReader br = new System.IO.BinaryReader(fs,System.Text.Encoding.Default);
int cols = int.Parse(meta[1]);
int rows = int.Parse(meta[3]);
MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(rows,cols);
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
dm[i, j] = br.ReadDouble();
}
d.Invoke(br.BaseStream.Position / br.BaseStream.Length, 1, "Завантаження");
}
br.Close();
fs.Close();
return dm;
}
public Matrix Inverse()
{
if (!IsMatrixQuadratic())
{
throw new Exception("Für eine Inverse Matrix muss diese quadratisch sein!");
}
//Math.net Library
MathNetLAGen.Matrix<double> netMatrix = new MathNetLA.Double.DenseMatrix(ToArray());
var inversedMatrix = new Matrix(netMatrix.Inverse().ToArray());
return inversedMatrix;
}
private void CalculateSuperimpositionTransformation_Mathnet(Vector3[] select1, Vector3[] select2,
out float[,] rotationMat, out float[] translationMat, out float error)
{
int rows = select1.Length;
var X = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(rows, 3);
var Y = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(rows, 3);
var weightArray = new double[rows];
for (int i = 0; i < rows; i++)
{
X[i, 0] = select1[i].X;
X[i, 1] = select1[i].Y;
X[i, 2] = select1[i].Z;
Y[i, 0] = select2[i].X;
Y[i, 1] = select2[i].Y;
Y[i, 2] = select2[i].Z;
if (i <= 11)
{
weightArray[i] = config.weights[i];
}
else
{
weightArray[i] = 1;
}
}
var sumColX = X.ColumnSums();
var mX = sumColX / rows;
var sumColY = Y.ColumnSums();
var mY = sumColY / rows;
var mXMatrix = Matrix <double> .Build.DenseOfColumnVectors(mX);
var mYMatrix = Matrix <double> .Build.DenseOfColumnVectors(mY);
for (int i = 1; i < rows; i++)
{
mXMatrix = mXMatrix.Append(Matrix <double> .Build.DenseOfColumnVectors(mX));
mYMatrix = mYMatrix.Append(Matrix <double> .Build.DenseOfColumnVectors(mY));
}
mXMatrix = mXMatrix.Transpose();
mYMatrix = mYMatrix.Transpose();
var X0 = X - mXMatrix;
var Y0 = Y - mYMatrix;
var normX = X0.FrobeniusNorm();
var normY = Y0.FrobeniusNorm();
var W = Matrix <double> .Build.DiagonalOfDiagonalArray(weightArray);
var A = X0.Transpose() * W * W * Y0;
var svd = A.Svd(true); // U, VT
var L = svd.U;
var D = svd.W;
var MT = svd.VT;
var T = (L * MT).ConjugateTranspose();
var traceTA = D.Diagonal().Sum();
var e = 1 + Math.Pow((normY / normX), 2) - 2 * traceTA * normY / normX;
var c = mX - mY * T;
error = (float)e;
translationMat = c.Map(x => (float)x).ToArray();
rotationMat = (T.Map(x => (float)x)).ToArray();
}
/// <summary>
///
/// </summary>
/// <param name="p"></param>
/// <param name="q"></param>
/// <returns></returns>
public static Matrix3D Rigid3D(IList<Vector3D> p, IList<Vector3D> q)
{
int n = p.Count;
double[] m = new double[9];
for (int i = 0; i < n; i++)
{
m[0] += p[i].X * q[i].X;
m[1] += p[i].X * q[i].Y;
m[2] += p[i].X * q[i].Z;
m[3] += p[i].Y * q[i].X;
m[4] += p[i].Y * q[i].Y;
m[5] += p[i].Y * q[i].Z;
m[6] += p[i].Z * q[i].X;
m[7] += p[i].Z * q[i].Y;
m[8] += p[i].Z * q[i].Z;
}
var M = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(3, 3, m);
//double[] S = new double[3];
//double[] U = new double[9];
//double[] VT = new double[9];
//mklSolver.SingularValueDecomposition(true, M.Data, 3, 3, S, U, VT);
//var RR = new Matrix3D()
//{
// M11 = U[0] * VT[0] + U[1] * VT[3] * U[2] * VT[6],
// M12 = U[0] * VT[1] + U[1] * VT[4] * U[2] * VT[7],
// M13 = U[0] * VT[2] + U[1] * VT[5] * U[2] * VT[8],
// M21 = U[3] * VT[0] + U[4] * VT[3] * U[5] * VT[6],
// M22 = U[3] * VT[1] + U[4] * VT[4] * U[5] * VT[7],
// M23 = U[3] * VT[2] + U[4] * VT[5] * U[5] * VT[8],
// M31 = U[6] * VT[0] + U[7] * VT[3] * U[8] * VT[6],
// M32 = U[6] * VT[1] + U[7] * VT[4] * U[8] * VT[7],
// M33 = U[6] * VT[2] + U[7] * VT[5] * U[8] * VT[8],
//};
//return RR;
// R = V*UT <==> RT = VT*U
var svd = M.Svd(true);
var RT = svd.U().Multiply(svd.VT());
var R = new Matrix3D()
{
M11 = RT[0, 0],
M12 = RT[1, 0],
M13 = RT[2, 0],
M21 = RT[0, 1],
M22 = RT[1, 1],
M23 = RT[2, 1],
M31 = RT[0, 2],
M32 = RT[1, 2],
M33 = RT[2, 2],
//M11 = RT[0, 0],
//M12 = RT[0, 1],
//M13 = RT[0, 2],
//M21 = RT[1, 0],
//M22 = RT[1, 1],
//M23 = RT[1, 2],
//M31 = RT[2, 0],
//M32 = RT[2, 1],
//M33 = RT[2, 2],
M44 = 1.0,
};
return R;
}
public void ConeGestureFinished()
{
ConeGestureInProgress = false;
GetComponent <LineRenderer>().enabled = false;
// Process cone gesture data here
// Start by converting (local) XYZ to TNB coordinates w.r.t the curve
var startIdx = coneGesturePositionData.Count / 10;
var endIdx = coneGesturePositionData.Count * 9 / 10;
var n = endIdx - startIdx;
double[] posData = new double[3 * n];
double[] velData = new double[3 * n];
double[] accData = new double[3 * n];
for (int i = startIdx; i < endIdx; ++i)
{
var pos = coneGesturePositionData[i];
var vel = coneGestureVelocityData[i];
var acc = coneGestureAccelerationData[i];
emissionCurve.GetClosestPoint(pos, out Vector3 closest, out int bezierIdx, out float param);
var tangent = emissionCurve.Tangent(bezierIdx, param);
var normal = emissionCurve.Normal(bezierIdx, param);
var binormal = Vector3.Cross(tangent, normal);
var delta = (pos - closest);
var time = emissionCurve.GetTimeFromSplineParam(bezierIdx, param);
// For points, first coordinate is time, and the other two given by dot product
pos = new Vector3(time, Vector3.Dot(delta, normal), Vector3.Dot(delta, binormal));
// For vectors, all three coordinates given by projecting to the new frame (dot product)
vel = new Vector3(Vector3.Dot(vel, tangent), Vector3.Dot(vel, normal), Vector3.Dot(vel, binormal));
acc = new Vector3(Vector3.Dot(acc, tangent), Vector3.Dot(acc, normal), Vector3.Dot(acc, binormal));
for (var j = 0; j < 3; ++j)
{
var idx = n * j + i - startIdx;
posData[idx] = pos[j];
velData[idx] = vel[j];
accData[idx] = acc[j];
}
}
// Perform SVD to get principal components
Matrix <double> P = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(n, 3, posData);
Matrix <double> V = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(n, 3, velData);
Matrix <double> A = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(n, 3, accData);
var accSvd = A.Svd(true);
var SA = accSvd.S;
var VA = accSvd.VT;
if (SA[0] / SA[1] > Globals.CONE_GESTURE_CLASSIFICATION_SINGULAR_VALUE_RATIO)
{
ProcessEmissionNoiseGesture(P, VA);
}
else
{
ProcessSpiralForceGesture(P, V);
}
Debug.Log("Delsecting because cone gesture finished");
animScript.Deselected();
}
