private void CalculateCepstrum()
{
answer = new MySeries[spec.Length];
Complex[] buff = new Complex[Setting.FrameLength];
for (int frame = 0; frame < answer.Length; frame++)
{
SetLabelValue(frame * 100 / answer.Length);
SetBarValue(frame * 100 / answer.Length);
for (int i = 0; i < Setting.FrameLength; i++)
{
buff[i] = Math.Log(1 + spec[frame].data[i]);
}
FFT.Backward(buff, FourierTransformScaling.NoScaling);
answer[frame] = new MySeries();
answer[frame].begin = (frame + 1) * Setting.FrameLength / 2;
answer[frame].end = answer[frame].begin + Setting.FrameLength - 1;
answer[frame].data = new float[Setting.FrameLength];
for (int i = 0; i < Setting.FrameLength; i++)
{
answer[frame].data[i] = (float)Complex.Abs(buff[i]);
}
}
SetLabelValue(100);
SetBarValue(100);
this.Invoke(new Action(this.Close));
}
private void CalculateSpec()
{
answer = new MySeries[sample.end * 2 / Setting.FrameLength - 1];
Complex[] buff = new Complex[Setting.FrameLength];
for (int frame = 0; frame < answer.Length; frame++)
{
SetLabelValue(frame * 100 / answer.Length);
SetBarValue(frame * 100 / answer.Length);
for (int i = 0; i < Setting.FrameLength; i++)
{
buff[i] = sample.data[frame * Setting.FrameLength / 2 + i];
}
HammingWindow.Use(buff);
FFT.Forward(buff, FourierTransformScaling.ForwardScaling);
answer[frame] = new MySeries();
answer[frame].begin = (frame + 1) * Setting.FrameLength / 2;
answer[frame].end = answer[frame].begin + Setting.FrameLength - 1;
answer[frame].data = new float[Setting.FrameLength];
for (int i = 0; i < Setting.FrameLength; i++)
{
answer[frame].data[i] = (float)Complex.Abs(buff[i]);
}
}
SetLabelValue(100);
SetBarValue(100);
this.Invoke(new Action(this.Close));
}
public static bool VectorEquals(Vector <Complex> v, Complex[] y, double eps)
{
var x = Util.GetData(v);
for (int i = 0; i < v.Count; i++)
{
if (Complex.Abs(x[i] - y[i]) > eps)
{
return(false);
}
}
return(true);
}
public static double Abs(this Complex self)
{
return(Complex.Abs(self));
}
/// <summary>
/// Prints eigenvalues and eigenvectors of nonsymmetric generalized eigen-problems.
/// </summary>
public static void General(SparseMatrix A, SparseMatrix B, SpectraResult result, bool shift, bool cshift = false)
{
if (!EnsureSuccess(result))
{
return;
}
int n = A.RowCount;
int nconv = result.ConvergedEigenValues;
Console.WriteLine();
Console.WriteLine("Testing ARPACK++ class ARluNonSymGenEig");
Console.WriteLine("Real nonsymmetric generalized eigenvalue problem: A*x - lambda*B*x");
Console.WriteLine(!shift ? "Regular mode" :
(cshift ? "Shift and invert mode (using the imaginary part of OP)" : "Shift and invert mode (using the real part of OP)"));
Console.WriteLine();
Console.WriteLine("Dimension of the system : " + n);
Console.WriteLine("Number of 'requested' eigenvalues : " + result.Count);
Console.WriteLine("Number of 'converged' eigenvalues : " + nconv);
Console.WriteLine("Number of Arnoldi vectors generated: " + result.ArnoldiCount);
Console.WriteLine("Number of iterations taken : " + result.IterationsTaken);
Console.WriteLine();
var evals = result.EigenValues;
var evecs = result.EigenVectors;
// Printing eigenvalues.
Console.WriteLine("Eigenvalues:");
for (int i = 0; i < nconv; i++)
{
Console.WriteLine(" lambda[" + (i + 1) + "]: " + evals[i]);
}
Console.WriteLine();
if (evecs != null)
{
// Printing the residual norm || A*x - lambda*B*x ||
// for the nconv accurately computed eigenvectors.
var x = new Complex[n];
var y = new Complex[n];
var r = new double[nconv]; // residuals
for (int i = 0; i < nconv; i++)
{
var lambda = evals[i];
evecs.Column(i, x);
CVector.Copy(x, y);
// y = B*x
B.Multiply(x, y);
// y = A*x - lambda*B*x
A.Multiply(1.0, x, -lambda, y);
r[i] = CVector.Norm(y) / Complex.Abs(lambda);
}
for (int i = 0; i < nconv; i++)
{
Console.WriteLine("||A*x(" + i + ") - lambda(" + i + ")*B*x(" + i + ")||: " + r[i]);
}
Console.WriteLine();
}
}
/// <summary>
/// Prints eigenvalues and eigenvectors of complex generalized eigen-problems.
/// </summary>
public static void Print(SparseMatrix A, SparseMatrix B, ArpackResult result, bool shift)
{
int n = A.RowCount;
int nconv = result.ConvergedEigenValues;
Console.WriteLine();
Console.WriteLine("Testing ARPACK++ class ARluCompGenEig");
Console.WriteLine("Complex generalized eigenvalue problem: A*x - lambda*B*x");
Console.WriteLine(shift ? "Shift and invert mode" : "Regular mode");
Console.WriteLine();
Console.WriteLine("Dimension of the system : " + n);
Console.WriteLine("Number of 'requested' eigenvalues : " + result.Count);
Console.WriteLine("Number of 'converged' eigenvalues : " + nconv);
Console.WriteLine("Number of Arnoldi vectors generated: " + result.ArnoldiCount);
Console.WriteLine("Number of iterations taken : " + result.IterationsTaken);
Console.WriteLine();
var evals = result.EigenValues;
var evecs = result.EigenVectors;
// Printing eigenvalues.
Console.WriteLine("Eigenvalues:");
for (int i = 0; i < nconv; i++)
{
Console.WriteLine(" lambda[" + (i + 1) + "]: " + evals[i]);
}
Console.WriteLine();
if (evecs != null)
{
// Printing the residual norm || A*x - lambda*B*x ||
// for the nconv accurately computed eigenvectors.
var x = new Complex[n];
var y = new Complex[n];
var r = new double[nconv]; // residuals
for (int i = 0; i < nconv; i++)
{
var lambda = evals[i];
evecs.Column(i, x);
Vector.Copy(x, y);
// y = B*x
B.Multiply(x, y);
// y = A*x - lambda*B*x
A.Multiply(1.0, x, -lambda, y);
r[i] = Vector.Norm(y) / Complex.Abs(lambda);
}
for (int i = 0; i < nconv; i++)
{
Console.WriteLine("||A*x(" + (i + 1) + ") - lambda(" + (i + 1) + ")*B*x(" + (i + 1) + ")||: " + r[i]);
}
Console.WriteLine();
}
}
