public static Vector PageRank(MathNet.Numerics.LinearAlgebra.Matrix<double> A, MathNet.Numerics.LinearAlgebra.Vector<double> z)
{
Vector pi;
var inversematrix = (SparseMatrix.CreateIdentity(z.Count) - beta * A).Inverse();
pi = (Vector)(inversematrix * (1 - beta) * z);
return pi;
}
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 double Intersect(MathNet.Numerics.LinearAlgebra.Generic.Vector<double> p0,
MathNet.Numerics.LinearAlgebra.Generic.Vector<double> l0,
MathNet.Numerics.LinearAlgebra.Generic.Vector<double> n,
MathNet.Numerics.LinearAlgebra.Generic.Vector<double> l)
{
return (p0 - l0).DotProduct(n) / l.DotProduct(n);
}
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 float[][] InverseTransform(PointF[] points, MathNet.Numerics.LinearAlgebra.Generic.Matrix<float> irt)
{
var undistorted = Intrinsic.Undistort(points, null, null);
var ids = new int[undistorted.Length];
for (var i = 0; i < ids.Length; i++) ids[i] = i;
var threes = ids.Select(i => new MathNet.Numerics.LinearAlgebra.Single.DenseVector(new float[] { undistorted[i].X, undistorted[i].Y, 1, 1 })).ToArray();
var tt = threes.Select(v => irt.Multiply(v)).ToArray();
return tt.Select(v => new float[] { (v[0] / v[3]), (v[1] / v[3]), (v[2] / v[3]) }).ToArray();
}
/// <summary>
/// Transforms the given 3D vector point using the given transformation matrix.
/// </summary>
/// <param name="TMatrix"> The 4x4 transformation matrix containing rotational and translational matrix. </param>
/// <param name="w"> Determines the scaling parameter for the translation: 0... neglect translation; 1... include translation. </param>
/// <param name="PointVector"> The IEnumerable object of type Vector (e.g. List, array,... of 3D point vectors). </param>
/// <returns> Transformed Points as IEnumerable of type Vector. </returns>
public static IEnumerable<MathNet.Numerics.LinearAlgebra.Vector> TransformVectorToVector(MathNet.Numerics.LinearAlgebra.Matrix TMatrix, double w,
IEnumerable<MathNet.Numerics.LinearAlgebra.Vector> PointVector)
{
foreach(MathNet.Numerics.LinearAlgebra.Vector v in PointVector)
{
//convert vector to a column matrix
MathNet.Numerics.LinearAlgebra.Matrix colMatrix = new MathNet.Numerics.LinearAlgebra.Matrix(4, 1);
colMatrix[3, 0] = w;
colMatrix.SetMatrix(0, 2, 0, 0, v.ToColumnMatrix());
//perform transformation of the point - column vector is extracted after the multiplication
yield return (TMatrix.Multiply(colMatrix)).GetColumnVector(0).ToNonHomogeneous();
}
}
/// <summary>
/// Transforms the given 3D vector point using the given transformation matrix.
/// </summary>
/// <param name="TMatrix"> The 4x4 transformation matrix containing rotational and translational matrix. </param>
/// <param name="w"> Determines the scaling parameter for the translation: 0... neglect translation; 1... include translation.</param>
/// <param name="PointVector"> The IEnumerable object of type Vector (e.g. List, array,... of 3D point vectors). </param>
/// <returns> Transformed Points as IEnumerable of Emgu type MCvPoint3D32f. </returns>
public static IEnumerable<Emgu.CV.Structure.MCvPoint3D32f> TransformVectorToEmgu(MathNet.Numerics.LinearAlgebra.Matrix TMatrix, double w,
IEnumerable<MathNet.Numerics.LinearAlgebra.Vector> PointVector)
{
foreach (MathNet.Numerics.LinearAlgebra.Vector v in PointVector)
{
//convert vector to a column matrix
MathNet.Numerics.LinearAlgebra.Matrix colMatrix = new MathNet.Numerics.LinearAlgebra.Matrix(4, 1);
colMatrix[3, 0] = w;
colMatrix.SetMatrix(0, 2, 0, 0, v.ToColumnMatrix());
//perform transformation of the point - column vector is extracted after the multiplication
//the resulting vector is converted to the Emgu type MCvPoint3D32f using ToEmguF().
yield return (TMatrix.Multiply(colMatrix)).GetColumnVector(0).ToNonHomogeneous().ToEmguF();
}
}
public override MathNet.Numerics.Complex32 voltage(Node referenceNode, MathNet.Numerics.Complex32? W = null)
{
Complex32 v = InputNodes[0].Voltage;
if (InputNodes.Count > 1)
{
//si no esta una entrada a tierra hay que hacer la diferencia
v -= InputNodes[1].Voltage;
}
v = v * new Complex32((float)gain, 0);
if (referenceNode == Nodes[0])
return v;
else if (referenceNode == Nodes[1])
return -v;
else
return Complex32.NaN;
}
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 double DistanceTo(MathNet.Numerics.LinearAlgebra.Vector x) {
return Math.Abs(_plane.SignedDistanceTo(x));
}
public static void writeMatrixToMatLabFile(MathNet.Numerics.LinearAlgebra.Generic.Matrix<double> matrix, string file,string name)
{
MathNet.Numerics.LinearAlgebra.IO.MatlabMatrixWriter wr = new MathNet.Numerics.LinearAlgebra.IO.MatlabMatrixWriter(file);
wr.WriteMatrix(matrix, name);
wr.Close();
}
protected override MathNet.Symbolics.Signal SubtractSignalCore(MathNet.Symbolics.Signal subtrahend)
{
return Std.Subtract(this, subtrahend);
}
internal void PrintTriangles(Vector<double> x,
List<int[]> triangles,
MathNet.Spatial.Euclidean.Point3D oRoot,
MathNet.Spatial.Euclidean.UnitVector3D vDir1,
MathNet.Spatial.Euclidean.UnitVector3D vDir2,
int iTrans,
MathNet.Spatial.Euclidean.Point3D oTrans,
bool just2D= false)
{
Part oPart = oPartDoc.Part;
HybridShapeFactory hsf = (HybridShapeFactory)oPart.HybridShapeFactory;
HybridBody hb1;
try {
hb1=(HybridBody)oPart.HybridBodies.GetItem("Blank Calculator Result");
hb1.get_Name();
oSel.Clear();
oSel.Add(hb1);
oSel.Delete();
} catch (Exception) {}
hb1 = (HybridBody)oPart.HybridBodies.Add();
hb1.set_Name("Blank Calculator Result");
//CATIA.RefreshDisplay = false;
//CATIA.Interactive = false;
List<Reference> RsltPoints = new List<Reference>();
MathNet.Spatial.Euclidean.CoordinateSystem Axis = new MathNet.Spatial.Euclidean.CoordinateSystem(oRoot, vDir1, vDir2, vDir1.CrossProduct(vDir2));
if (just2D) {
for (int i = 0; i < x.Count / 2; i++) {
MathNet.Spatial.Euclidean.Point3D PtMath = new MathNet.Spatial.Euclidean.Point3D(new double[] { x[i * 2], x[i * 2 + 1], 0 });
Point PTCat = hsf.AddNewPointCoord(PtMath.X, PtMath.Y, PtMath.Z);
PTCat.Compute();
RsltPoints.Add(oPart.CreateReferenceFromObject(PTCat));
}
} else {
MathNet.Spatial.Euclidean.Point3D PtMath = new MathNet.Spatial.Euclidean.Point3D(new double[] { x[iTrans * 2], x[iTrans * 2 + 1], 0 });
PtMath = Axis.TransformToCoordSys(PtMath);
double[] vecTranslation = new double[] { oTrans.X - PtMath.X, oTrans.Y - PtMath.Y, oTrans.Z - PtMath.Z };
for (int i = 0; i < x.Count / 2; i++) {
PtMath = new MathNet.Spatial.Euclidean.Point3D(new double[] { x[i * 2], x[i * 2 + 1], 0 });
PtMath = Axis.TransformToCoordSys(PtMath);
Point PTCat = hsf.AddNewPointCoord(PtMath.X + vecTranslation[0], PtMath.Y + vecTranslation[1], PtMath.Z + vecTranslation[2]);
PTCat.Compute();
RsltPoints.Add(oPart.CreateReferenceFromObject(PTCat));
}
}
foreach (int[] item in triangles) {
Line lUp = hsf.AddNewLinePtPt(RsltPoints[item[0]], RsltPoints[item[1]]);
lUp.Compute();
hb1.AppendHybridShape(lUp);
Line l0 = hsf.AddNewLinePtPt(RsltPoints[item[1]], RsltPoints[item[2]]);
l0.Compute();
hb1.AppendHybridShape(l0);
Line lDown = hsf.AddNewLinePtPt(RsltPoints[item[2]], RsltPoints[item[0]]);
lDown.Compute();
hb1.AppendHybridShape(lDown);
}
//CATIA.RefreshDisplay = true;
//CATIA.Interactive = true;
}
void CompoundArchitecture_SignalValueChanged(object sender, MathNet.Symbolics.Backend.Events.SignalEventArgs e)
{
int idx = inputSignals.IndexOf(e.Signal);
system.PushInputValue(idx, inputSignals[idx].Value);
}
protected override MathNet.Symbolics.Signal MultiplySignalCore(MathNet.Symbolics.Signal multiplier)
{
return Std.Multiply(this, multiplier);
}
protected abstract void ReregisterArchitecture(MathNet.Symbolics.Port oldPort, MathNet.Symbolics.Port newPort);
public static void writeVectorToFile(MathNet.Numerics.LinearAlgebra.Double.DenseVector vector, 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 = vector.Values;
System.IO.TextWriter tr = new System.IO.StreamWriter(file + ".metainfo");
tr.WriteLine("Items count");
tr.WriteLine(vector.Count);
tr.Flush();
tr.Close();
int len = arr.Length;
vector = 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();
}
/// <summary>
/// True if the signal depends on a specified port.
/// </summary>
public override bool DependsOn(MathNet.Symbolics.Port port)
{
return Service<IScanner>.Instance.ExistsPort(this, port.Equals, true);
}
protected void SetPort(MathNet.Symbolics.Port port)
{
if(port == null) throw new ArgumentNullException("port");
_isInstance = true;
_port = port;
}
/// <summary>
/// True if the signal depends on a specified signal, and thus posting a new value to the specified signal may influence this signal's value.
/// </summary>
public override bool DependsOn(MathNet.Symbolics.Signal signal)
{
return Service<IScanner>.Instance.ExistsSignal(this, signal.Equals, true);
}
/// <remarks>needs to be called as soon as soon as it becomes connected through its driven port to an output signal -> run with each output signal as source parameter.</remarks>
public int AddCycles(MathNet.Symbolics.Signal source, int tag)
{
if(this == source)
{
_cycleCount++;
return 1;
}
if(IsSourceSignal || !IsDriven)
return 0;
else
{
int ret = 0;
if(!((IPort_CycleAnalysis)_drivenByPort).TagWasTagged(tag)) //was not tagged with this tag before.
{
foreach(Signal item in _drivenByPort.InputSignals)
if(item != null)
ret += item.AddCycles(source, tag);
((IPort_CycleAnalysis)_drivenByPort).DeTag(tag);
}
_cycleCount += ret;
return ret;
}
}
void system_OutputValueChanged(object sender, MathNet.Symbolics.Backend.Events.IndexedSignalEventArgs e)
{
outputSignals[e.Index].PostNewValue(e.Signal.Value);
}
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());
}
protected override MathNet.Symbolics.Signal AddSignalCore(MathNet.Symbolics.Signal summand)
{
return Std.Add(this, summand);
}
public static void writeModeVectorlToTxtFile(MathNet.Numerics.LinearAlgebra.Double.DenseVector dm, ReferenceSystem rs, string file)
{
System.IO.File.WriteAllText(file, matrixToString((MathNet.Numerics.LinearAlgebra.Double.DenseMatrix)dm.ToColumnMatrix(), rs), System.Text.Encoding.Default);
}
protected override MathNet.Symbolics.Signal DivideSignalCore(MathNet.Symbolics.Signal divisor)
{
return Std.Divide(this, divisor);
}
public static void writeVectorToTxtFile(MathNet.Numerics.LinearAlgebra.Double.DenseVector dm, string file)
{
StringBuilder sb = new StringBuilder();
foreach (var item in dm.Values)
{
sb.AppendFormat("{0,5:0.0000000000000e+00}\r\n", item);
}
System.IO.File.WriteAllText(file, sb.ToString());
}
public bool RebindToPortIfSupported(MathNet.Symbolics.Port newPort)
{
if(newPort == null) throw new ArgumentNullException("newPort");
if(newPort.Equals(_port))
return true;
if(SupportsPort(newPort))
{
MathNet.Symbolics.Port oldPort = _port;
UnregisterArchitecture();
_port = newPort;
ReregisterArchitecture(oldPort, newPort);
return true;
}
return false;
}
void ISignal_Drive.DriveSignal(MathNet.Symbolics.Port source, int outputIndex)
{
if(IsDriven)
Service<IMediator>.Instance.NotifyPortDrivesSignalNoLonger(this, _drivenByPort, _drivenByPort.OutputSignals.IndexOf(this));
_drivenByPort = source;
_isSourceSignal = false;
_properties.ValidatePropertiesAfterDrive(this);
Service<IMediator>.Instance.NotifyPortDrivesSignal(this, source, outputIndex);
}
/// <remarks>needs to be called as soon as soon as it becomes disconnected through its driven port to an output signal -> run with each output signal as source parameter.</remarks>
public int RemoveCycles(MathNet.Symbolics.Signal source, int tag)
{
if(this == source)
{
System.Diagnostics.Debug.Assert(_cycleCount >= 1);
_cycleCount--;
return 1;
}
if(IsSourceSignal || !IsDriven)
return 0;
else
{
int ret = 0;
if(!((IPort_CycleAnalysis)_drivenByPort).TagWasTagged(tag)) //was not tagged with this tag before.
{
foreach(Signal item in _drivenByPort.InputSignals)
ret += item.RemoveCycles(source, tag);
((IPort_CycleAnalysis)_drivenByPort).DeTag(tag);
}
System.Diagnostics.Debug.Assert(_cycleCount >= ret);
_cycleCount -= ret;
return ret;
}
}
