/// <summary>
/// This function computes the the multiplication of the transpose of the trajectory matrix H by an arbitrary vector v, i.e. H' * v.
/// Since the trajectory matrix is a Hankel matrix, using the Discrete Fourier Transform,
/// the multiplication is carried out in O(N.log(N)) instead of O(N^2), wheere N is the series length.
/// For details, refer to Algorithm 3 in http://arxiv.org/pdf/0911.4498.pdf.
/// </summary>
/// <param name="vector">The input vector</param>
/// <param name="result">The output vector allocated by the caller</param>
/// <param name="add">Whether the multiplication result should be added to the current value in result</param>
/// <param name="srcIndex">The starting index for the vector argument</param>
/// <param name="dstIndex">The starting index for the result</param>
private void FftMultiplyTranspose(Single[] vector, Single[] result, bool add = false, int srcIndex = 0, int dstIndex = 0)
{
_ectx.Assert(srcIndex >= 0);
_ectx.Assert(dstIndex >= 0);
_ectx.Assert(Utils.Size(vector) >= _windowSize + srcIndex);
_ectx.Assert(Utils.Size(result) >= _k + dstIndex);
int i;
// Computing the FFT of the trajectory matrix
if (!_isSeriesFftCached)
{
CacheInputSeriesFft();
}
// Computing the FFT of the input vector
for (i = 0; i < _k - 1; ++i)
{
_inputRe[i] = 0;
}
for (i = _k - 1; i < _seriesLength; ++i)
{
_inputRe[i] = vector[_seriesLength - i - 1 + srcIndex];
}
FftUtils.ComputeForwardFft(_inputRe, _allZerosIm, _outputRe, _outputIm, _inputRe.Length);
// Computing the element-by-element product in the Fourier space
double re;
double im;
for (i = 0; i < _seriesLength; ++i)
{
re = _outputRe[i];
im = _outputIm[i];
_outputRe[i] = _cachedSeriesFftRe[i] * re - _cachedSeriesFftIm[i] * im;
_outputIm[i] = _cachedSeriesFftRe[i] * im + _cachedSeriesFftIm[i] * re;
}
// Computing the inverse FFT of the result
FftUtils.ComputeBackwardFft(_outputRe, _outputIm, _outputRe, _outputIm, _inputRe.Length);
// Generating the output
if (add)
{
for (i = 0; i < _k; ++i)
{
result[i + dstIndex] += RoundUpToReal(_outputRe[_windowSize - 1 + i], _outputIm[_windowSize - 1 + i]);
}
}
else
{
for (i = 0; i < _k; ++i)
{
result[i + dstIndex] = RoundUpToReal(_outputRe[_windowSize - 1 + i], _outputIm[_windowSize - 1 + i]);
}
}
}
private protected override sealed void SpectralResidual(Single input, FixedSizeQueue <Single> data, ref VBufferEditor <double> result)
{
// Step 1: Get backadd wave
List <Single> backAddList = BackAdd(data);
// Step 2: FFT transformation
int length = backAddList.Count;
float[] fftRe = new float[length];
float[] fftIm = new float[length];
FftUtils.ComputeForwardFft(backAddList.ToArray(), Enumerable.Repeat(0.0f, length).ToArray(), fftRe, fftIm, length);
// Step 3: Calculate mags of FFT
List <Single> magList = new List <Single>();
for (int i = 0; i < length; ++i)
{
magList.Add(MathUtils.Sqrt((fftRe[i] * fftRe[i] + fftIm[i] * fftIm[i])));
}
// Step 4: Calculate spectral
List <Single> magLogList = magList.Select(x => x != 0 ? MathUtils.Log(x) : 0).ToList();
List <Single> filteredLogList = AverageFilter(magLogList, Parent.AvergingWindowSize);
List <Single> spectralList = new List <Single>();
for (int i = 0; i < magLogList.Count; ++i)
{
spectralList.Add(MathUtils.ExpSlow(magLogList[i] - filteredLogList[i]));
}
// Step 5: IFFT transformation
float[] transRe = new float[length];
float[] transIm = new float[length];
for (int i = 0; i < length; ++i)
{
if (magLogList[i] != 0)
{
transRe[i] = fftRe[i] * spectralList[i] / magList[i];
transIm[i] = fftIm[i] * spectralList[i] / magList[i];
}
else
{
transRe[i] = 0;
transIm[i] = 0;
}
}
float[] ifftRe = new float[length];
float[] ifftIm = new float[length];
FftUtils.ComputeBackwardFft(transRe, transIm, ifftRe, ifftIm, length);
// Step 6: Calculate mag and ave_mag of IFFT
List <Single> ifftMagList = new List <Single>();
for (int i = 0; i < length; ++i)
{
ifftMagList.Add(MathUtils.Sqrt((ifftRe[i] * ifftRe[i] + ifftIm[i] * ifftIm[i])));
}
List <Single> filteredIfftMagList = AverageFilter(ifftMagList, Parent.JudgementWindowSize);
// Step 7: Calculate score and set result
var score = CalculateSocre(ifftMagList[data.Count - 1], filteredIfftMagList[data.Count - 1]);
score /= 10.0f;
result.Values[1] = score;
score = Math.Min(score, 1);
score = Math.Max(score, 0);
var detres = score > Parent.AlertThreshold ? 1 : 0;
result.Values[0] = detres;
var mag = ifftMagList[data.Count - 1];
result.Values[2] = mag;
}
/// <summary>
/// This function computes the efficient Hankelization of the matrix sigma * u * v' using Fast Fourier Transform in in O((L + K) * log(L + K)).
/// For details, refer to Algorithm 4 in http://arxiv.org/pdf/0911.4498.pdf.
/// </summary>
/// <param name="u">The u vector</param>
/// <param name="v">The v vector</param>
/// <param name="sigma">The scalar coefficient</param>
/// <param name="result">The output series</param>
/// <param name="add">Whether the hankelization result should be added to the current value in result</param>
/// <param name="uIndex">The starting index for the u vector argument</param>
/// <param name="vIndex">The starting index for the v vector argument</param>
/// <param name="dstIndex">The starting index for the result</param>
/// <param name="start">The staring index of the series to be reconstructed (by default zero)</param>
/// <param name="end">The ending index of the series to be reconstructed (by default series length)</param>
private void FftRankOneHankelization(Single[] u, Single[] v, Single sigma, Single[] result, bool add = false,
int uIndex = 0, int vIndex = 0, int dstIndex = 0, int?start = null, int?end = null)
{
int s;
int e;
int us;
int ue;
int vs;
int ve;
int i;
s = start ?? 0;
e = end ?? _seriesLength - 1;
ComputeBoundryIndices(s, e, out us, out ue, out vs, out ve);
_ectx.Assert(0 <= ue && ue < _windowSize);
_ectx.Assert(0 <= us && us <= ue);
_ectx.Assert(0 <= ve && ve < _k);
_ectx.Assert(0 <= vs && vs <= ve);
var len = e - s + 1;
_ectx.Assert(uIndex >= 0);
_ectx.Assert(vIndex >= 0);
_ectx.Assert(dstIndex >= 0);
_ectx.Assert(Utils.Size(u) >= _windowSize + uIndex);
_ectx.Assert(Utils.Size(v) >= _k + vIndex);
_ectx.Assert(Utils.Size(result) >= len + dstIndex);
_ectx.Assert(!Single.IsNaN(sigma));
_ectx.Assert(!Single.IsInfinity(sigma));
if (!_isSeriesFftCached)
{
CacheInputSeriesFft();
}
// Computing the FFT of u
for (i = us; i <= ue; ++i)
{
_inputRe[i - us] = u[i + uIndex];
}
for (i = ue + 1; i < len + us; ++i)
{
_inputRe[i - us] = 0;
}
FftUtils.ComputeForwardFft(_inputRe, _allZerosIm, _outputRe, _outputIm, len);
// Computing the FFT of v
for (i = vs; i <= ve; ++i)
{
_inputRe[i - vs] = v[i + vIndex];
}
for (i = ve + 1; i < len + vs; ++i)
{
_inputRe[i - vs] = 0;
}
FftUtils.ComputeForwardFft(_inputRe, _allZerosIm, _inputRe, _allZerosIm, len);
// Computing the element-by-element product in the Fourier space
double re;
double im;
for (i = 0; i < len; ++i)
{
re = _outputRe[i];
im = _outputIm[i];
_outputRe[i] = _inputRe[i] * re - _allZerosIm[i] * im;
_outputIm[i] = _inputRe[i] * im + _allZerosIm[i] * re;
}
// Setting _allZerosIm to 0's again
for (i = 0; i < _seriesLength; ++i)
{
_allZerosIm[i] = 0;
}
// Computing the inverse FFT of the result
FftUtils.ComputeBackwardFft(_outputRe, _outputIm, _outputRe, _outputIm, len);
// Generating the output
int a = Math.Min(ue - us + 1, ve - vs + 1);
if (add)
{
for (i = 0; i < a; ++i)
{
result[i + dstIndex] += RoundUpToReal(_outputRe[i], _outputIm[i], sigma / (i + 1));
}
for (i = a; i < len - a + 1; ++i)
{
result[i + dstIndex] += RoundUpToReal(_outputRe[i], _outputIm[i], sigma / a);
}
for (i = len - a + 1; i < len; ++i)
{
result[i + dstIndex] += RoundUpToReal(_outputRe[i], _outputIm[i], sigma / (len - i));
}
}
else
{
for (i = 0; i < a; ++i)
{
result[i + dstIndex] = RoundUpToReal(_outputRe[i], _outputIm[i], sigma / (i + 1));
}
for (i = a; i < len - a + 1; ++i)
{
result[i + dstIndex] = RoundUpToReal(_outputRe[i], _outputIm[i], sigma / a);
}
for (i = len - a + 1; i < len; ++i)
{
result[i + dstIndex] = RoundUpToReal(_outputRe[i], _outputIm[i], sigma / (len - i));
}
}
}