public void TestConvertParsleyToEmgu() { MathNet.Numerics.LinearAlgebra.Vector v = new MathNet.Numerics.LinearAlgebra.Vector(new double[] { 1.0f, 2.0f, 3.0f }); Emgu.CV.Structure.MCvPoint3D32f f = v.ToEmguF(); Assert.AreEqual(1.0, f.x); Assert.AreEqual(2.0, f.y); Assert.AreEqual(3.0, f.z); Emgu.CV.Structure.MCvPoint3D64f d = v.ToEmgu(); Assert.AreEqual(1.0, d.x); Assert.AreEqual(2.0, d.y); Assert.AreEqual(3.0, d.z); double[,] data = new double[2, 3] { { 1.0, 2.0, 3.0 }, { 4.0, 5.0, 6.0 } }; MathNet.Numerics.LinearAlgebra.Matrix m = MathNet.Numerics.LinearAlgebra.Matrix.Create(data); Emgu.CV.Matrix <double> m2 = m.ToEmgu(); Assert.AreEqual(data[0, 0], m2[0, 0]); Assert.AreEqual(data[0, 1], m2[0, 1]); Assert.AreEqual(data[0, 2], m2[0, 2]); Assert.AreEqual(data[1, 0], m2[1, 0]); Assert.AreEqual(data[1, 1], m2[1, 1]); Assert.AreEqual(data[1, 2], m2[1, 2]); }
public Vector(System.Collections.Generic.IEnumerable <int> enumerable) { InnerVector = MathNet.Numerics.LinearAlgebra.Vector <float> .Build.DenseOfEnumerable( System.Linq.Enumerable.Select(enumerable, (i => (float)i)) ); }
/// <summary> /// Convert MCvPoint3D32f to MathNet.Numerics.LinearAlgebra.Vector /// </summary> /// <param name="p">MCvPoint3D32f to convert</param> /// <returns>MathNet.Numerics.LinearAlgebra.Vector</returns> public static MathNet.Numerics.LinearAlgebra.Vector ToParsley(this Emgu.CV.Structure.MCvPoint3D32f p) { MathNet.Numerics.LinearAlgebra.Vector v = new MathNet.Numerics.LinearAlgebra.Vector(3); v[0] = p.x; v[1] = p.y; v[2] = p.z; return v; }
/// <summary> /// Convert System.Drawing.Point to MathNet.Numerics.LinearAlgebra.Vector /// </summary> /// <param name="p">System.Drawing.Point to convert</param> /// <returns>MathNet.Numerics.LinearAlgebra.Vector</returns> public static MathNet.Numerics.LinearAlgebra.Vector ToParsley(this System.Drawing.Point p) { MathNet.Numerics.LinearAlgebra.Vector v = new MathNet.Numerics.LinearAlgebra.Vector(2); v[0] = p.X; v[1] = p.Y; return(v); }
static void Main(string[] args) { Transform3D[] trns = new Transform3D[4]; MathNet.Numerics.LinearAlgebra.Matrix <double> identity = MathNet.Numerics.LinearAlgebra.Matrix <double> .Build.Dense(3, 3); identity[0, 0] = 1; identity[1, 1] = 1; identity[2, 2] = 1; MathNet.Numerics.LinearAlgebra.Vector <double> v1 = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(3); v1[0] = 0; MathNet.Numerics.LinearAlgebra.Vector <double> v2 = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(3); v2[0] = 9; MathNet.Numerics.LinearAlgebra.Vector <double> v3 = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(3); v3[0] = 10; MathNet.Numerics.LinearAlgebra.Vector <double> v4 = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(3); v4[0] = 11; trns[0] = new Transform3D(identity, v1); trns[1] = new Transform3D(identity, v2); trns[2] = new Transform3D(identity, v3); trns[3] = new Transform3D(identity, v4); Candidate.initSums(42, 3.14, 2.72); Density d = new Density(); // finder, we need an instance for certain complicated reason Transform3D solution = d.Find(trns); Console.WriteLine(solution); }
ToNonHomogeneous(this MathNet.Numerics.LinearAlgebra.Vector v) { MathNet.Numerics.LinearAlgebra.Vector r = new MathNet.Numerics.LinearAlgebra.Vector(v.Length - 1); for (int i = 0; i < v.Length - 1; ++i) { r[i] = v[i]; } return r; }
/// <summary> /// Convert MCvPoint3D32f to MathNet.Numerics.LinearAlgebra.Vector /// </summary> /// <param name="p">MCvPoint3D32f to convert</param> /// <returns>MathNet.Numerics.LinearAlgebra.Vector</returns> public static MathNet.Numerics.LinearAlgebra.Vector ToParsley(this Emgu.CV.Structure.MCvPoint3D32f p) { MathNet.Numerics.LinearAlgebra.Vector v = new MathNet.Numerics.LinearAlgebra.Vector(3); v[0] = p.x; v[1] = p.y; v[2] = p.z; return(v); }
public void TestConvertSystemToParsley() { System.Drawing.PointF p = new System.Drawing.PointF(1.0f, 2.0f); MathNet.Numerics.LinearAlgebra.Vector v = p.ToParsley(); Assert.AreEqual(1.0, v[0]); Assert.AreEqual(2.0, v[1]); }
ToNonHomogeneous(this MathNet.Numerics.LinearAlgebra.Vector v) { MathNet.Numerics.LinearAlgebra.Vector r = new MathNet.Numerics.LinearAlgebra.Vector(v.Length - 1); for (int i = 0; i < v.Length - 1; ++i) { r[i] = v[i]; } return(r); }
private static MathNet.Numerics.LinearAlgebra.Vector <double> Multiply2(MathNet.Numerics.LinearAlgebra.Matrix <double> matrix, MathNet.Numerics.LinearAlgebra.Vector <double> rightSide) { MathNet.Numerics.LinearAlgebra.VectorBuilder <double> build = MathNet.Numerics.LinearAlgebra.Vector <double> .Build; MathNet.Numerics.LinearAlgebra.Vector <double> ret = build.SameAs(matrix, rightSide, matrix.RowCount); Transformer.DoMultiply(matrix, rightSide, ret); return(ret); }
public DenseVector ToDense(MathNet.Numerics.LinearAlgebra.Vector <double> a) { DenseVector c = new DenseVector(a.Count); for (int i = 0; i < a.Count; i++) { c[i] = a[i]; } return(c); }
private static QuantityBase MultiplyVectorMatrix(QuantityBase left, QuantityBase right) { Quantity <MathNet.Numerics.LinearAlgebra.Vector <double> > leftQ = (Quantity <MathNet.Numerics.LinearAlgebra.Vector <double> >)left; Quantity <MathNet.Numerics.LinearAlgebra.Matrix <double> > rightQ = (Quantity <MathNet.Numerics.LinearAlgebra.Matrix <double> >)right; IUnit unit = MultiplyUnits(left, right); MathNet.Numerics.LinearAlgebra.Vector <double> vector = leftQ.Value * rightQ.Value; return(new Quantity <MathNet.Numerics.LinearAlgebra.Vector <double> >(vector, unit)); }
ToHomogeneous(this MathNet.Numerics.LinearAlgebra.Vector v, double w) { MathNet.Numerics.LinearAlgebra.Vector r = new MathNet.Numerics.LinearAlgebra.Vector(v.Length + 1); for (int i = 0; i < v.Length; ++i) { r[i] = v[i]; } r[r.Length - 1] = w; return r; }
ToHomogeneous(this MathNet.Numerics.LinearAlgebra.Vector v, double w) { MathNet.Numerics.LinearAlgebra.Vector r = new MathNet.Numerics.LinearAlgebra.Vector(v.Length + 1); for (int i = 0; i < v.Length; ++i) { r[i] = v[i]; } r[r.Length - 1] = w; return(r); }
private static void FormOutput(MathNet.Numerics.LinearAlgebra.Vector <double> solution, Equations.Nonlinear.NonlinearEquationDescription system, ref List <string> outputList) { for (int i = 0; i < solution.Count; i++) { outputList.Add(system.VariableNames[i] + " = " + solution[i].ToString()); } double[] x = solution.ToArray(); for (int i = 0; i < system.Equations.Count; i++) { outputList.Add($"F{i}(X) = {system.Equations[i].Execute(x)}"); } }
private void creatMatrixForLSI()//func for LSI { createWordsMap(); createDocsMap(); int rows = m_wordsMap.Count; int cols = m_docsMap.Count; double[,] A = new double[rows, cols]; double[,] q = new double[rows, 1]; foreach (string word in m_wordsMap.Keys) //create A matrix { long ptr = m_Dictionary[word]; string rec = findPostingRec(@"C:\Users\Daniel\Documents\Visual Studio 2015\Projects\IR\IR\bin\Debug\t\PostingF.txt", ptr); List <string> docsOfword = new List <string>(); string[] sRec = rec.Split(' '); for (int i = 0; i < sRec.Length; i++) { if (sRec[i].EndsWith("#")) { docsOfword.Add(sRec[i].Substring(0, sRec[i].Length - 1)); } } int Arow = m_wordsMap[word]; foreach (string doc in docsOfword) { int Acol = m_docsMap[doc]; A[Arow, Acol] = 1; } q[Arow, 0] = 1; //set q vector } var Amatrix = DenseMatrix.OfArray(A); var svd = Amatrix.Svd(true); var D_T = svd.VT; var T = svd.U; var S = svd.W; int k = 1; var D_Tk = D_T.SubMatrix(0, k, 0, D_T.ColumnCount); var Tk = T.SubMatrix(0, T.RowCount, 0, k); var Sk = S.SubMatrix(0, k, 0, k); var Sk_inver = Sk.Inverse(); m_Dk_T = D_Tk; m_Sk_invers = Sk_inver; m_Tk = Tk; var Qmatrix = DenseMatrix.OfArray(q); var q_T = Qmatrix.Transpose(); var qMatrix = q_T * m_Tk * m_Sk_invers; m_q = qMatrix.Row(0); }
public static void initSums(double xSize, double ySize, double zSize) { t0 = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(new double[] { xSize / 2, ySize / 2, zSize / 2 }); tpSum = new double[3]; // we only need the diagonal tpSum[0] = xSize * xSize * xSize * ySize * zSize / 12; tpSum[1] = xSize * ySize * ySize * ySize * zSize / 12; tpSum[2] = xSize * ySize * zSize * zSize * zSize / 12; dpsum = tpSum[0] + tpSum[1] + tpSum[2]; n = xSize * ySize * zSize; invn = 1.0 / n; Density.radius = Math.Sqrt(xSize * xSize + ySize * ySize + zSize * zSize) / 4; }
public static void PrintImage(MathNet.Numerics.LinearAlgebra.Vector <double> image, int row) { int column = image.Count / row; for (int y = 0; y < row; y++) { for (int x = 0; x < column; x++) { Console.Write(GrayScale[(int)((GrayScale.Length - 1) * (1 - image[y * column + x]))]); } Console.WriteLine(); } }
}//checked public static DenseMatrix getdiagboundary(DenseMatrix inFrame) { DenseMatrix output = new DenseMatrix(inFrame.RowCount, inFrame.ColumnCount); for (int diagIdx = -1 * inFrame.RowCount + 1; diagIdx <= inFrame.ColumnCount - 1; diagIdx++) { MathNet.Numerics.LinearAlgebra.Vector <float> tempDiag = diag(inFrame, diagIdx); DenseMatrix tempDiagSum = new DenseMatrix(1, tempDiag.Count); int count = 0; for (int ii = 0; ii < tempDiag.Count; ii++) { if (tempDiag[ii] == 1) { count = count + 1; tempDiagSum[0, ii] = count; } else { count = 0; } } if (diagIdx <= 0) { int offset = 0; //iterate through diagonal for (int i = -diagIdx; (i < inFrame.RowCount) && (offset < inFrame.ColumnCount); i++) { output[i, offset] = tempDiagSum[0, offset]; offset++; } } else { int offset = 0; for (int i = diagIdx; (i < inFrame.ColumnCount) && (offset < inFrame.RowCount); i++) { output[offset, i] = tempDiagSum[0, offset]; offset++; } } } return(output); }//checked
Vector3 computeNormal(MathNet.Numerics.LinearAlgebra.Vector <MathNet.Numerics.Complex> eigenVa, Matrix <float> eigenVe) { // Keep the smallest eigenvector as surface normal int smallestId = 0; float smallestValue = float.MaxValue; for (int j = 0; j < eigenVe.ColumnCount; j++) { float lambda = (float)eigenVa[j].Real; if (lambda < smallestValue) { smallestId = j; smallestValue = lambda; } } var normalVector = eigenVe.Column(smallestId); return(ToVector3(normalVector)); }
static List <Graphs.Vector> GetEigenVectorLayout(MathNet.Numerics.LinearAlgebra.Vector <double> x, MathNet.Numerics.LinearAlgebra.Vector <double> y) { var minX = double.MaxValue; var minY = double.MaxValue; var maxX = double.MinValue; var maxY = double.MinValue; for (int v = 0; v < x.Count; v++) { minX = Math.Min(minX, x[v]); minY = Math.Min(minY, y[v]); maxX = Math.Max(maxX, x[v]); maxY = Math.Max(maxY, y[v]); } var width = maxX - minX; var height = maxY - minY; return(Enumerable.Range(0, x.Count).Select(v => new Graphs.Vector(0.1 + 0.7 * (x[v] - minX) / width, 0.1 + 0.7 * (y[v] - minY) / height)).ToList()); }
}//checked /// <summary> /// return an array of diagonal values of input matrix given an offset /// </summary> /// <param name="inFrame"></param> /// <param name="diag_idx"></param> /// <returns></returns> public static MathNet.Numerics.LinearAlgebra.Vector <float> diag(DenseMatrix inFrame, int diag_idx) { ///Idea: Given offset, create new matrix from specified offset and get diagonal //Trying to mimic Matlab's function DenseMatrix newMatrix = null; if (diag_idx <= 0) { //Along length newMatrix = (DenseMatrix)inFrame.SubMatrix(-diag_idx, inFrame.RowCount + diag_idx, 0, inFrame.ColumnCount); } else if (diag_idx > 0) { newMatrix = (DenseMatrix)inFrame.SubMatrix(0, inFrame.RowCount, diag_idx, inFrame.ColumnCount - diag_idx); } MathNet.Numerics.LinearAlgebra.Vector <float> diagonals = newMatrix.Diagonal(); return(diagonals); } //checked
private static MathNet.Numerics.LinearAlgebra.Matrix <double> PseudoInverse2(MathNet.Numerics.LinearAlgebra.Double.Matrix matrix) { MathNet.Numerics.LinearAlgebra.Factorization.Svd <double> svd = MathNet.Numerics.LinearAlgebra.Double.Factorization.UserSvd.Create(matrix, true); MathNet.Numerics.LinearAlgebra.Matrix <double> w = svd.W; MathNet.Numerics.LinearAlgebra.Vector <double> s = svd.S; double tolerance = 008 * svd.L2Norm * System.Math.Pow(2, -53); for (int i = 0; i < s.Count; i++) { s[i] = s[i] < tolerance ? 0 : 1 / s[i]; } for (var i = 0; i < 008; i++) { double value = s.At(i); w.Storage.At(i, i, value); } MathNet.Numerics.LinearAlgebra.Matrix <double> ddd = svd.U * w * svd.VT; return(ddd.Transpose()); }
public void TestConvertParsleyToInterop() { MathNet.Numerics.LinearAlgebra.Vector v = new MathNet.Numerics.LinearAlgebra.Vector(new double[] { 1.0, 2.0, 3.0 }); double[] i = v.ToInterop(); Assert.AreEqual(1.0, i[0]); Assert.AreEqual(2.0, i[1]); Assert.AreEqual(3.0, i[2]); double[,] data = new double[2, 3] { { 1.0, 2.0, 3.0 }, { 4.0, 5.0, 6.0 } }; MathNet.Numerics.LinearAlgebra.Matrix m = MathNet.Numerics.LinearAlgebra.Matrix.Create(data); double[,] m2 = m.ToInterop(); Assert.AreEqual(data[0, 0], m2[0, 0]); Assert.AreEqual(data[0, 1], m2[0, 1]); Assert.AreEqual(data[0, 2], m2[0, 2]); Assert.AreEqual(data[1, 0], m2[1, 0]); Assert.AreEqual(data[1, 1], m2[1, 1]); Assert.AreEqual(data[1, 2], m2[1, 2]); }
public static Vector2 trilaterate2DLinear(List <BleNode> nodes, List <double> distances) { if (nodes.Count == 3 && distances.Count == 3) { MathNet.Numerics.LinearAlgebra.Vector <double> vA = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(new double[] { nodes[0].X, nodes[0].Y }); double[] b = { 0, 0 }; b[0] = 0.5 * (Math.Pow(distances[0], 2) - Math.Pow(distances[1], 2) + Math.Pow(getDistance(nodes[1], nodes[0]), 2)); b[1] = 0.5 * (Math.Pow(distances[0], 2) - Math.Pow(distances[2], 2) + Math.Pow(getDistance(nodes[2], nodes[0]), 2)); MathNet.Numerics.LinearAlgebra.Vector <double> vb = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(b); double[,] A = { { nodes[1].X - nodes[0].X, nodes[1].Y - nodes[0].Y }, { nodes[2].X - nodes[0].X, nodes[2].Y - nodes[0].Y } }; MathNet.Numerics.LinearAlgebra.Matrix <double> mA = MathNet.Numerics.LinearAlgebra.Matrix <double> .Build.DenseOfArray(A); MathNet.Numerics.LinearAlgebra.Matrix <double> mAT = mA.Transpose(); MathNet.Numerics.LinearAlgebra.Vector <double> x = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(2); double det = mA.Multiply(mAT).Determinant(); if (det > 0.1) { x = (mA.Transpose() * mA).Inverse() * (mA.Transpose() * vb); } else { x = (((mA.Multiply(mAT)).Inverse()).Multiply(mAT)).Multiply(vb); } x.Add(vA); double[] coordinates = x.ToArray(); Vector2 vector = new Vector2((float)coordinates[0], (float)coordinates[1]); return(vector); } return(new Vector2()); }
internal Transform3D toTransform3D() { MathNet.Numerics.LinearAlgebra.Matrix <double> r = MathNet.Numerics.LinearAlgebra.Matrix <double> .Build.Dense(3, 3); MathNet.Numerics.LinearAlgebra.Vector <double> tr = MathNet.Numerics.LinearAlgebra.Vector <double> .Build.Dense(3); r[0, 0] = rot[0]; r[0, 1] = rot[1]; r[0, 2] = rot[2]; r[1, 0] = rot[3]; r[1, 1] = rot[4]; r[1, 2] = rot[5]; r[2, 0] = rot[6]; r[2, 1] = rot[7]; r[2, 2] = rot[8]; var Rt0 = r * t0; tr[0] = t[0] - Rt0[0]; tr[1] = t[1] - Rt0[1]; tr[2] = t[2] - Rt0[2]; return(new Transform3D(r, tr)); }
public void TestConvertEmguToParsley() { Emgu.CV.Structure.MCvPoint3D32f p = new Emgu.CV.Structure.MCvPoint3D32f(1.0f, 2.0f, 3.0f); MathNet.Numerics.LinearAlgebra.Vector v = p.ToParsley(); Assert.AreEqual(1.0, v[0]); Assert.AreEqual(2.0, v[1]); Assert.AreEqual(3.0, v[2]); double[,] data = new double[2, 3] { { 1.0, 2.0, 3.0 }, { 4.0, 5.0, 6.0 } }; Emgu.CV.Matrix <double> m = new Emgu.CV.Matrix <double>(data); MathNet.Numerics.LinearAlgebra.Matrix m2 = m.ToParsley(); Assert.AreEqual(data[0, 0], m2[0, 0]); Assert.AreEqual(data[0, 1], m2[0, 1]); Assert.AreEqual(data[0, 2], m2[0, 2]); Assert.AreEqual(data[1, 0], m2[1, 0]); Assert.AreEqual(data[1, 1], m2[1, 1]); Assert.AreEqual(data[1, 2], m2[1, 2]); }
/// <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] )); }
public void TestConvertParsleyToInterop() { MathNet.Numerics.LinearAlgebra.Vector v = new MathNet.Numerics.LinearAlgebra.Vector(new double[] { 1.0, 2.0, 3.0 }); double[] i = v.ToInterop(); Assert.AreEqual(1.0, i[0]); Assert.AreEqual(2.0, i[1]); Assert.AreEqual(3.0, i[2]); double[,] data = new double[2, 3] { { 1.0, 2.0, 3.0 }, { 4.0, 5.0, 6.0 } }; MathNet.Numerics.LinearAlgebra.Matrix m = MathNet.Numerics.LinearAlgebra.Matrix.Create(data); double[,] m2 = m.ToInterop(); Assert.AreEqual(data[0, 0], m2[0, 0]); Assert.AreEqual(data[0, 1], m2[0, 1]); Assert.AreEqual(data[0, 2], m2[0, 2]); Assert.AreEqual(data[1, 0], m2[1, 0]); Assert.AreEqual(data[1, 1], m2[1, 1]); Assert.AreEqual(data[1, 2], m2[1, 2]); }
public void TestConvertParsleyToEmgu() { MathNet.Numerics.LinearAlgebra.Vector v = new MathNet.Numerics.LinearAlgebra.Vector(new double[] { 1.0f, 2.0f, 3.0f }); Emgu.CV.Structure.MCvPoint3D32f f = v.ToEmguF(); Assert.AreEqual(1.0, f.x); Assert.AreEqual(2.0, f.y); Assert.AreEqual(3.0, f.z); Emgu.CV.Structure.MCvPoint3D64f d = v.ToEmgu(); Assert.AreEqual(1.0, d.x); Assert.AreEqual(2.0, d.y); Assert.AreEqual(3.0, d.z); double[,] data = new double[2, 3] { { 1.0, 2.0, 3.0 }, { 4.0, 5.0, 6.0 } }; MathNet.Numerics.LinearAlgebra.Matrix m = MathNet.Numerics.LinearAlgebra.Matrix.Create(data); Emgu.CV.Matrix<double> m2 = m.ToEmgu(); Assert.AreEqual(data[0, 0], m2[0, 0]); Assert.AreEqual(data[0, 1], m2[0, 1]); Assert.AreEqual(data[0, 2], m2[0, 2]); Assert.AreEqual(data[1, 0], m2[1, 0]); Assert.AreEqual(data[1, 1], m2[1, 1]); Assert.AreEqual(data[1, 2], m2[1, 2]); }
/// <summary> /// Given a vector x, returns the derivative of x, elementwise /// </summary> /// <param name="x">the input vector x</param> /// <returns> elementwise application of the derivative of the relu function = x > 0 ? 1 : 0.01</returns> public MathNet.Numerics.LinearAlgebra.Vector <double> CalculateDerivative(MathNet.Numerics.LinearAlgebra.Vector <double> x) { return(x.Map(s => s > 0 ? 1 : 0.01)); }
/// <summary> /// This is the method that actually does the work. /// </summary> /// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param> protected override void SolveInstance(IGH_DataAccess DA) { //Input Matrix mL = null; DA.GetData(0, ref mL); //size of n x n matrix int n = mL.RowCount; //Number of eigenvalues/vectors to extract int eigsCount = n; if (Params.Input[1].SourceCount > 0) { DA.GetData(1, ref eigsCount); } if (eigsCount > n) { eigsCount = n; } else if (eigsCount < 1) { eigsCount = 1; } //Run calculation toggle bool run = false; DA.GetData(2, ref run); //Calculate //Create lists to store data List <double> eigenValuesOutput = new List <double>(); DataTree <double> eigenVectorsOutput = new DataTree <double>(); Matrix eigenVectorMatrixOutput = null; if (run && mL != null) { //Create matrix for MathNet eigendecomposition var matrix = MathNet.Numerics.LinearAlgebra.Matrix <double> .Build.Dense(n, n); for (int i = 0; i < n; i++) { for (int j = 0; j < n; j++) { matrix[i, j] = mL[i, j]; } } //Eigendecomposition var evd = matrix.Evd(MathNet.Numerics.LinearAlgebra.Symmetricity.Symmetric); MathNet.Numerics.LinearAlgebra.Vector <Complex> eigsValComplex = evd.EigenValues; MathNet.Numerics.LinearAlgebra.Vector <Double> eigsValReal = eigsValComplex.Map(c => c.Real); MathNet.Numerics.LinearAlgebra.Matrix <double> eigsVec = evd.EigenVectors; //Convert data back to GH types //Eigenvalue list for (int i = 0; i < eigsCount; i++) { eigenValuesOutput.Add(eigsValReal[i]); } //Eigenvectors eigenVectorMatrixOutput = new Matrix(n, eigsCount); for (int i = 0; i < n; i++) { for (int j = 0; j < eigsCount; j++) { double val = eigsVec[i, j]; GH_Path path = new GH_Path(j); eigenVectorsOutput.Add(val, path); eigenVectorMatrixOutput[i, j] = val; } } } //Output DA.SetDataList(0, eigenValuesOutput); DA.SetDataTree(1, eigenVectorsOutput); DA.SetData(2, eigenVectorMatrixOutput); }
/// <summary> /// Convert System.Drawing.Point to MathNet.Numerics.LinearAlgebra.Vector /// </summary> /// <param name="p">System.Drawing.Point to convert</param> /// <returns>MathNet.Numerics.LinearAlgebra.Vector</returns> public static MathNet.Numerics.LinearAlgebra.Vector ToParsley(this System.Drawing.Point p) { MathNet.Numerics.LinearAlgebra.Vector v = new MathNet.Numerics.LinearAlgebra.Vector(2); v[0] = p.X; v[1] = p.Y; return v; }
public Vector() { InnerVector = null; }
/// <summary> /// Given an input vector x, this function computes and returns max(x,0.01x) elementwise /// </summary> /// <param name="x">the inout vector x</param> /// <returns> max(x,0.01x) </returns> public MathNet.Numerics.LinearAlgebra.Vector <double> CalculateActivation(MathNet.Numerics.LinearAlgebra.Vector <double> x) { return(x.Map(s => s > 0 ? s : 0.01 * s)); }
public static Vector3 ToVector(this MathNet.Numerics.LinearAlgebra.Vector <double> vector) => new Vector3((float)vector[0], (float)vector[1], (float)vector[2]);