/// <summary>
/// Computes the EState for each atom in molecule
/// </summary>
/// <returns></returns>
private double[] CalculateEStateIndices()
{
var numAtoms = this.Mol.NumAtoms();
var Is = new double[numAtoms].Zero().ToArray();
for (var i = 1; i <= numAtoms; i++)
{
var atom = this.Mol.GetAtom(i);
var degree = atom.GetValence();
if (degree > 0)
{
var dv = atom.NOuterShellElectrons() - atom.TotalNumHydrogens();
var N = atom.PrincipleQuantumNumber();
Is[i - 1] = (4 / (N * N) * dv + 1) / (double)degree;
}
}
var distances = this.DistanceMatrix.Add(1);
var accum = new double[numAtoms].Zero().ToArray();
for (var i = 0; i < numAtoms; i++)
{
for (var j = i + 1; j < numAtoms; j++)
{
var p = distances.Get(i, j);
if (p < FloydWarshall.MaxValue)
{
var temp = (Is[i] - Is[j]) / (p * p);
accum[i] += temp;
accum[j] -= temp;
}
}
}
return(Is.Add(accum).ToArray());
}
public void Matrix_StandardMatrixAdditionTest()
{
var matrix1 = new double[3, 2];
matrix1[0, 0] = 1;
matrix1[0, 1] = 1;
matrix1[1, 0] = 2;
matrix1[1, 1] = 0;
matrix1[2, 0] = 0;
matrix1[2, 1] = 3;
var matrix2 = new double[3, 2];
matrix2[0, 0] = 0;
matrix2[0, 1] = 1;
matrix2[1, 0] = 1;
matrix2[1, 1] = 0;
matrix2[2, 0] = 2;
matrix2[2, 1] = 3;
var answerMatrix = new double[3, 2];
answerMatrix[0, 0] = 1;
answerMatrix[0, 1] = 2;
answerMatrix[1, 0] = 3;
answerMatrix[1, 1] = 0;
answerMatrix[2, 0] = 2;
answerMatrix[2, 1] = 6;
var resultingMatrix = matrix1.Add(matrix2);
resultingMatrix.ShouldEqualWithinTolerance(answerMatrix);
}
public void MatrixAdd_Add_Vectors_Different_Lengths()
{
var v1 = new double[] { 2, 3, 5, 10 };
var v2 = new double[] { 1, 1 };
v1.Add(v2);
}
public static void GenerateValues(int howMany, double meanA, double meanB, double meanC, double[,] R)
{
TestChol();
var disitrubtion = new NormalDistribution(0, Math.Sqrt(1));
var mean = new double[] { disitrubtion.InverseDistributionFunction(meanA), disitrubtion.InverseDistributionFunction(meanB), disitrubtion.InverseDistributionFunction(meanC) };
var randomValues = disitrubtion.Generate(howMany * 3);
var aSamples = new double[howMany];
var bSamples = new double[howMany];
var cSamples = new double[howMany];
int randomValueCount = 0;
for (int i = 0; i < howMany; i++)
{
//generating one sample
var z = new[] { randomValues[randomValueCount++], randomValues[randomValueCount++], randomValues[randomValueCount++] };
var product = z.Dot(R);
var y = mean.Add(product);
var samples = new double[] { y[0] > 0 ? 1 : 0, y[1] > 0 ? 1 : 0, y[2] > 0 ? 1 : 0 };
aSamples[i] = samples[0];
bSamples[i] = samples[1];
cSamples[i] = samples[2];
Trace.WriteLine($"{aSamples[i]}, {bSamples[i]}, {cSamples[i]}");
}
var resultCorrAb = CalculateCorrelation(aSamples, bSamples);
var resultCorrAc = CalculateCorrelation(aSamples, cSamples);
var resultCorrBc = CalculateCorrelation(bSamples, cSamples);
}
public static double[] OpenSimplex2DOctaves(
int seed,
int size, int offsetX, int offsetY, double scale, double initialFrequency,
int octaves, int octaveOffsetScale, double amplitudePerOctave, double freqPerOctave)
{
var random = new Random(seed);
var data = new double[size * size];
var currentAmplitude = 1d;
var currentFrequency = initialFrequency;
var maxNoise = 0d;
var octaveOffsets = new Vector2[octaves];
for (var octaveIndex = 0; octaveIndex < octaves; octaveIndex++)
{
octaveOffsets[octaveIndex] = new Vector2(
random.Next(-octaveOffsetScale, octaveOffsetScale) + offsetX,
random.Next(-octaveOffsetScale, octaveOffsetScale) + offsetY);
data.Add(OpenSimplex2D(seed, octaveOffsets[octaveIndex].X, octaveOffsets[octaveIndex].Y, size, size, scale, currentFrequency, currentAmplitude));
maxNoise += currentAmplitude;
currentAmplitude *= amplitudePerOctave;
currentFrequency *= freqPerOctave;
}
return(data.Mul(1d / maxNoise));
}
public static double[] StochasticGradientAscent(double[,] Features, double[,] classLabels, int numIter = 1000)
{
int m = Features.GetLength(0);
int n = Features.GetLength(1);
double[,] weights = new double[n, 1];
for (int i = 0; i < n; i++)
{
weights[i, 0] = 1;
}
for (int i = 0; i < numIter; i++)
{
for (int j = 0; j < m; j++)
{
double alpha = 4 / (1.0 + i + j) + WALKSTEP;
double h = 0.0;
double[,] temp = new double[n, 1];
for (int k = 0; k < n; k++)
{
h += Features[j, k] * weights[k, 0];
temp[k, 0] = Features[j, k];
}
double error = classLabels[j, 0] - Sigmoid(h);
weights = weights.Add(temp.ScalarMultiply(error * alpha));
}
}
double[] result = new double[n];
for (int i = 0; i < n; i++)
{
result[i] = weights[i, 0];
}
return(result);
}
public void TrainByPseudoInverse(int[,] testData)
{
testData = testData.Multiply(2).Subtract(1); // macierz -1 i 1
int patternCount = testData.Rows(); //liczba wzorców
neuronCount = testData.Columns(); //liczba neuronów we wzorcu
double[,] W = new double[neuronCount, neuronCount]; //inicjalizacja macierzy wag 64x64
for (int row = 0; row < patternCount; row++)
{
double[,] x = testData.Get(row, row + 1, 0, neuronCount).Transpose().Convert(i => (double)i);
var x1 = W.Dot(x).Subtract(x);
var x1t = x1.Transpose();
var licznik = x1.Dot(x1t);
double mianownik = x.TransposeAndDot(x).Subtract(x.Transpose().Dot(W).Dot(x))[0, 0];
W = W.Add(licznik.Divide(mianownik));
}
weights = W;
trained = true;
}
public static double[][] InvertU(this double[][] upper)
{
if (upper is null)
{
throw new ArgumentNullException(nameof(upper));
}
double[][] identity = new double[upper.Length][];
double[][] diagonalInverse = new double[upper.Length][];
double[][] strictUpper = new double[upper.Length][];
for (int i = 0; i < identity.Length; i++)
{
identity[i] = new double[upper[i].Length];
diagonalInverse[i] = new double[upper[i].Length];
strictUpper[i] = new double[upper[i].Length];
for (int j = 0; j < identity[i].Length; j++)
{
if (i == j)
{
identity[i][j] = 1.0;
diagonalInverse[i][j] = 1.0 / upper[i][j];
}
else if (i < j)
{
strictUpper[i][j] = upper[i][j];
}
}
}
return(identity.Add(strictUpper).InvertLu().MatrixProduct(diagonalInverse));
}
public void AddShouldAddIfMaxIsNotReached()
{
double add = 10;
double value = 10;
value.Add(add).Should().Be(20);
}
public static double[] StochasticGradientAscent(double[,] Features, double[,] classLabels)
{
int m = Features.GetLength(0);
int n = Features.GetLength(1);
double[,] weights = new double[n, 1];
for (int i = 0; i < n; i++)
{
weights[i, 0] = 1;
}
for (int i = 0; i < m; i++)
{
double h = 0.0;
double[,] temp = new double[n, 1];
for (int j = 0; j < n; j++)
{
h += Features[i, j] * weights[j, 0];
temp[j, 0] = Features[i, j];
}
double error = classLabels[i, 0] - Sigmoid(h);
weights = weights.Add(temp.ScalarMultiply(error * WALKSTEP));
}
double[] result = new double[n];
for (int i = 0; i < n; i++)
{
result[i] = weights[i, 0];
}
return(result);
}
public static double[] GradAscent(double[,] Features, double[,] classLabels)
{
int m = Features.GetLength(0);
int n = Features.GetLength(1);
double[,] weights = new double[n, 1];
for (int i = 0; i < n; i++)
{
weights[i, 0] = 1;
}
for (int i = 0; i < REPEATCOUNT; i++)
{
double[,] h = Sigmoid(Features.Multiply(weights));
double[,] error = classLabels.Substract(h);
weights = weights.Add(Features.Transpose().ScalarMultiply(WALKSTEP).Multiply(error));
if (i > 9900)
{
Console.WriteLine(weights[0, 0] + "\t" + weights[1, 0] + "\t" + weights[2, 0]);
}
}
double[] result = new double[n];
for (int i = 0; i < n; i++)
{
result[i] = weights[i, 0];
}
return(result);
}
private double[] CalculatePredictionValue <TDistribution>(TDistribution emission, double[][] trainingSet) where TDistribution : IDistribution
{
var result = new double[trainingSet[0].Length];
if (typeof(TDistribution).Name == "Mixture`1")
{
switch (typeof(TDistribution).GetGenericArguments()[0].FullName)
{
case "TA.Statistics.Distributions.IMultivariateDistribution":
var e = emission as Mixture <IMultivariateDistribution>;
for (var i = 0; i < e.Components.Length; i++)
{
result = result.Add(e.Components[i].Mean.Product(e.Coefficients[i]));
}
result = e.Mean;
break;
}
}
else
{
switch (typeof(TDistribution).FullName)
{
case "TA.Statistics.Distributions.IMultivariateDistribution":
result = ((IMultivariateDistribution)emission).Mean;
break;
}
}
return(result);
}
/// <summary>
/// Generates a normal distribution noise signal of the specified Volts RMS.
/// </summary>
/// <param name="amplitudeVrms"></param>
/// <param name="points"></param>
/// <param name="dcV"></param>
/// <returns>double[] array</returns>
public static double[] NoiseRms(double amplitudeVrms, UInt32 points, double dcV = 0.0)
{
double[] rval = new double[points];
// Make an n length noise vector
rval = Noise(points, amplitudeVrms);
rval = rval.Add(dcV);
return(rval);
}
public double[][] Estimate(MuEstimationParameters <TDistribution> parameters)
{
if (_muMultivariate != null)
{
return(_muMultivariate);
}
try
{
_muMultivariate = new double[parameters.Model.N][];
var K = parameters.Observations[0].Dimention; // Number of dimentions
var T = parameters.Observations.Count;
for (var n = 0; n < parameters.Model.N; n++)
{
var mean = new double[K];
var nK = 0d;
for (var t = 0; t < T; t++)
{
if (parameters.Model.Normalized)
{
nK += LogExtention.eExp(parameters.Gamma[t][n]);
mean = mean.Add(parameters.Observations[t].Value.Product(LogExtention.eExp(parameters.Gamma[t][n])));
}
else
{
nK += parameters.Gamma[t][n];
mean = mean.Add(parameters.Observations[t].Value.Product(parameters.Gamma[t][n]));
}
}
_muMultivariate[n] = mean.Product(1 / nK);
Debug.WriteLine(string.Format("HMM State {0} : Mu {1}", n, new Vector(_muMultivariate[n])));
}
}
catch (Exception)
{
for (var n = 0; n < parameters.Model.N; n++)
{
Debug.WriteLine(string.Format("HMM State {0} : Mu {1}", n, new Vector(_muMultivariate[n])));
}
throw;
}
return(_muMultivariate);
}
public double[][,] Estimate(SigmaEstimationParameters <TDistribution, double[][]> parameters)
{
if (_sigmaMultivariate != null)
{
return(_sigmaMultivariate);
}
try
{
_sigmaMultivariate = new double[parameters.Model.N][, ];
var K = parameters.Observations[0].Dimention;
var T = parameters.Observations.Count;
for (var n = 0; n < parameters.Model.N; n++)
{
var covariance = new double[K, K];
var nK = 0d;
for (var t = 0; t < T; t++)
{
var x = parameters.Observations[t].Value;
var z = x.Substruct(parameters.Mean[n]);
var m = z.OuterProduct(z);
if (parameters.Model.Normalized)
{
nK += LogExtention.eExp(parameters.Gamma[t][n]);
m = m.Product(LogExtention.eExp(parameters.Gamma[t][n]));
}
else
{
nK += parameters.Gamma[t][n];
m = m.Product(parameters.Gamma[t][n]);
}
covariance = covariance.Add(m);
}
_sigmaMultivariate[n] = covariance.Product(1 / nK);
var matrix = new Matrix(_sigmaMultivariate[n]);
if (!matrix.PositiviDefinite)
{
_sigmaMultivariate[n] = matrix.ConvertToPositiveDefinite();
Debug.WriteLine("HMM State {0} Sigma is not Positive Definite. Converting.", n);
Debug.WriteLine("{0}", matrix);
}
Debug.WriteLine("HMM State {0} Sigma : {1}", n, new Matrix(_sigmaMultivariate[n]));
}
}
catch (Exception)
{
for (var n = 0; n < parameters.Model.N; n++)
{
Debug.WriteLine("HMM State {0} Sigma : {1}", n, new Matrix(_sigmaMultivariate[n]));
}
throw;
}
return(_sigmaMultivariate);
}
public void ArrayExtentions_Add()
{
var arr = new double[] { 3.2, 4.5, 7, 9.0 };
var symbol = 7.67;
var result = arr.Add(symbol);
Assert.AreEqual(5, result.Length);
Assert.AreEqual(symbol, result[arr.Length]);
}
/// <summary>
/// Merge a list of gaussian components into a one big gaussian
/// that tries to approximate as much as possible the behaviour
/// of the original mixture.
/// </summary>
/// <param name="components">List of other gaussian components.</param>
/// <returns>Merged gaussian.</returns>
public static Gaussian Merge(List <Gaussian> components)
{
if (components == null || components.Count == 0)
{
return(null);
}
Gaussian first = components[0];
double weight = 0.0;
double[] mean = new double[first.Mean.Length];
double[][] covariance = MatrixExtensions.Zero(first.Mean.Length);
// // merged gaussian follows the rules
// // w = sum of (wi)
// // m = sum of (wi mi) / w
// // P = sum of (wi (Pi + (mi - m) (mi - m)^T)) / w
//
// // equivalent form:
// foreach (Gaussian component in components) {
// weight += component.Weight;
// mean = mean.Add(component.Weight.Multiply(component.Mean));
// }
//
// mean = mean.Divide(weight);
//
// foreach (Gaussian component in components) {
// double[] diff = component.Mean.Subtract(mean);
// covariance = covariance.Add(component.Weight.Multiply(
// component.Covariance.Add(diff.OuterProduct(diff).ToArray())));
// }
//
// covariance = covariance.Divide(weight);
foreach (Gaussian component in components)
{
double w = component.Weight;
double[] m = component.Mean;
double[][] cov = component.Covariance;
weight += w;
mean = mean.Add(w.Multiply(m));
covariance = covariance.Add(w.Multiply(cov.Add(m.OuterProduct(m).ToArray())));
}
if (weight < 1e-15)
{
return(new Gaussian(first.Mean, Util.InfiniteCovariance(first.Mean.Length), 0.0));
}
mean = mean.Divide(weight);
covariance = covariance.Divide(weight).Subtract(mean.OuterProduct(mean).ToArray());
return(new Gaussian(mean, covariance, weight));
}
public double[, ][,] Estimate(MixtureSigmaEstimationParameters <TDistribution> parameters)
{
if (_sigma != null)
{
return(_sigma);
}
try
{
_sigma = new double[parameters.Model.N, parameters.L][, ];
for (var i = 0; i < parameters.Model.N; i++)
{
for (var l = 0; l < parameters.L; l++)
{
var denominator = 0.0d;
var nominator = new double[parameters.Observations[0].Dimention, parameters.Observations[0].Dimention];
for (var t = 0; t < parameters.Observations.Count; t++)
{
// TODO : weights here
var weight = GetWeightValue(t, parameters.ObservationWeights);
var gammaComponents = (parameters.Model.Normalized) ? LogExtention.eExp(parameters.GammaComponents[t][i, l]) : parameters.GammaComponents[t][i, l];
var x = parameters.Observations[t].Value;
var z = x.Substruct(parameters.Mu[i, l]);
var m = z.OuterProduct(z);
m = m.Product(weight * gammaComponents);
denominator += weight * gammaComponents;
nominator = nominator.Add(m);
}
_sigma[i, l] = nominator.Product(1 / denominator);
var matrix = new Matrix(_sigma[i, l]);
if (!matrix.PositiviDefinite)
{
_sigma[i, l] = matrix.ConvertToPositiveDefinite();
Debug.WriteLine("HMM State [{0},{1}] Sigma is not Positive Definite. Converting.", i, l);
Debug.WriteLine("{0}", matrix);
}
}
}
}
catch (Exception)
{
for (var i = 0; i < parameters.Model.N; i++)
{
for (var l = 0; l < parameters.L; l++)
{
Debug.WriteLine("Mixture Sigma [{0},{1}] : {2}", i, l, new Matrix(_sigma[i, l]));
}
}
throw;
}
return(_sigma);
}
public static double GetMahalanobisDistance(this KDReading _reading, KDReading[] userReadings)
{
int vectorSize = _reading.LetterMeasurements.Length,
setSize = userReadings.Length;
double[] v1 = Array.ConvertAll(_reading.LetterMeasurements, item => (double)item);
var vectors = userReadings.Select(reading => reading.LetterMeasurements);
var meanVector = vectors.Select(vector => Array.ConvertAll(vector, val => (double)val)).Mean();
var vectorOfDistancesFromMean = v1.Zip(meanVector, (a, b) => a - b);
double[,] matrixOfDistancesFromMean = new double[1, vectorSize],
matrixOfDistancesFromMeanTransposed = new double[vectorSize, 1];
for (int i = 0; i < vectorSize; i++)
{
matrixOfDistancesFromMean[0, i] = matrixOfDistancesFromMeanTransposed[i, 0] = vectorOfDistancesFromMean.ElementAt(i);
}
var covarianceMatrix = new double[vectorSize, vectorSize];
covarianceMatrix.Initialize();
for (int i = 0; i < setSize; i++)
{
var sample = Array.ConvertAll(userReadings[i].LetterMeasurements, val => (double)val);
var sampleDistanceFromMean = sample.Zip(meanVector, (a, b) => a - b);
double[,] matrixSampleDistanceFromMean = new double[1, vectorSize],
matrixSampleDistanceFromMeanTransposed = new double[vectorSize, 1];
for (int j = 0; j < vectorSize; j++)
{
matrixSampleDistanceFromMean[0, j] = matrixSampleDistanceFromMeanTransposed[j, 0] = sampleDistanceFromMean.ElementAt(j);
}
var product = matrixSampleDistanceFromMeanTransposed.Dot(matrixSampleDistanceFromMean);
covarianceMatrix = covarianceMatrix.Add(product);
}
covarianceMatrix = covarianceMatrix.Divide(setSize);
try
{
covarianceMatrix = covarianceMatrix.Inverse();
}
catch (Exception)
{
covarianceMatrix = covarianceMatrix.PseudoInverse();
}
var outputMatrix = matrixOfDistancesFromMean.Dot(covarianceMatrix).Dot(matrixOfDistancesFromMeanTransposed);
return(Math.Sqrt(outputMatrix[0, 0]));
}
private static double[] CalculateMean(double[][] gamma, double[][] observations, int k)
{
var K = observations[0].Length;
var N = observations.Length;
var mean = new double[K];
var nK = gamma[k].Sum();
for (var n = 0; n < N; n++)
{
mean = mean.Add(observations[n].Product(gamma[k][n]));
}
return(mean.Product(1 / nK));
}
/// <summary>
/// Calculate an weighted average of the form Sum(Exp(weight) * vector) / Sum(Exp(weight))
/// as numerically stable as possible.
/// </summary>
/// <param name="vectors">Array of vectors.</param>
/// <param name="weights">Log-weights for the average.</param>
/// <param name="begin">First index to consider.</param>
/// <param name="end">Last+1 index to consider.</param>
/// <returns>Tempered average of the vectors.</returns>
public static double[] TemperedAverage(this double[][] vectors, double[] weights, int begin, int end)
{
if (vectors.Length != weights.Length)
{
throw new ArgumentException("There must be exactly one weight per vector");
}
if (begin < 0)
{
throw new ArgumentException("Begin index must be greater or equal to zero");
}
if (end > vectors.Length)
{
throw new ArgumentException("End index must be less or equal to the vector size");
}
if (vectors.Length == 0)
{
return(new double[0]);
}
double max = double.NegativeInfinity;
double[] value = new double[vectors[0].Length];
for (int i = begin; i < end; i++)
{
max = Math.Max(max, weights[i]);
}
if (double.IsNegativeInfinity(max))
{
return(value);
}
for (int i = begin; i < end; i++)
{
weights[i] = Math.Exp(weights[i] - max);
}
weights = weights.Normalize();
for (int i = begin; i < end; i++)
{
value = value.Add(weights[i].Multiply(vectors[i]));
}
return(value);
}
/// <summary>
/// Calculates incremental running average.
/// </summary>
/// <param name="data">Sample data.</param>
/// <param name="onCalculated">
/// Action callback which fires on each element addition.
/// <para>Parameters are: (index, incremental average).</para>
/// </param>
public static void RunningAverageIncremental(this IList <double[]> data, Action <int, double[]> onCalculated)
{
var dim = data.First().Length;
double[] avg = new double[dim];
for (int i = 0; i < data.Count; i++)
{
var item = data[i];
avg = avg.Add(UpdateAverageIncremental(avg, i, item));
onCalculated(i, avg);
}
}
public void NoiseAnalysis()
{
// Setup parameters to generate noise test signal of 5 nVrms / rt-Hz
double amplitude = 5.0e-9;
UInt32 length = 1000; double samplingRate = 2000;
// Generate window & calculate Scale Factor for NOISE!
double[] wCoefs = mdsplib.DSP.Window.Coefficients(mdsplib.DSP.Window.Type.Hamming, length);
double wScaleFactor = mdsplib.DSP.Window.ScaleFactor.Noise(wCoefs, samplingRate);
// Instantiate & Initialize a new DFT
DFT dft = new DFT();
dft.Initialize(length);
// Average the noise 'N' times
Int32 N = 1000;
double[] noiseSum = new double[(length / 2) + 1];
for (Int32 i = 0; i < N; i++)
{
// Generate the noise signal & apply window
double[] inputSignal = mdsplib.DSP.Generate.NoisePsd(amplitude, samplingRate, length);
inputSignal = inputSignal.Multiply(wCoefs);
// DFT the noise -> Convert -> Sum
Complex[] cSpectrum = dft.Execute(inputSignal);
double[] mag2 = cSpectrum.MagnitudeSquared();
noiseSum = (N == 0) ? noiseSum : noiseSum = noiseSum.Add(mag2);
}
// Calculate Average, convert to magnitude format
// See text for the reasons to use Mag^2 format.
double[] averageNoise = noiseSum.Divide(N);
double[] lmSpectrum = averageNoise.Magnitude();
// Properly scale the spectrum for the added window
lmSpectrum = lmSpectrum.Multiply(wScaleFactor);
// For plotting on an XY Scatter plot generate the X Axis frequency Span
double[] freqSpan = Util.FFT.FrequencySpan(samplingRate, length);
// At this point a XY Scatter plot can be generated from,
// X axis => freqSpan
// Y axis => lmSpectrum as a Power Spectral Density Value
// Extra Credit - Analyze Plot Data Ignoring the first and last 20 Bins
// Average value should be what we generated = 5.0e-9 Vrms / rt-Hz
double averageValue = mdsplib.DSP.Analyze.FindMean(lmSpectrum, 20, 20);
}
public void MatrixAdd_Add_Vectors()
{
var v1 = new double[] { 2, 3, 5, 10 };
var v2 = new double[] { 1, 1, 1, 1 };
var actual = v1.Add(v2);
var expected = new double[] { 3, 4, 6, 11 };
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.1);
}
}
static void Main(string[] args)
{
"Hello World.".Print();
"Hello World.".Excite();
Person person = new Person();
person.Fill().Print();
double initialNumber = 4.00;
double chainNumber = initialNumber.Add(4).Subtract(2).MultiplyBy(8).DivideBy(3);
Console.WriteLine($"\nThe calculated number is { chainNumber }");
}
public static double[] InverseNoOverlap(List <Complex[]> stft, UInt32 wlen, UInt32 fftPad)
{
int wlength = stft.First().Length;
int totalLength = stft.Count() * (int)wlen;
var waveout = new double[totalLength];
uint offset = 0;
foreach (var spectrum in stft)
{
double[] slice = spectrum.iFFT().RealPart();
waveout = waveout.Add(slice.Slice(0, wlen), offset);
offset += wlen;
}
return(waveout);
}
public static double[] Inverse(List <Complex[]> stft, UInt32 wlen, UInt32 fftPad)
{
int wlength = stft.First().Length;
int totalLength = (stft.Count() + 1) * wlength / 2;
var waveout = new double[totalLength];
uint offset = 0;
foreach (var spectrum in stft)
{
double[] slice = spectrum.iFFT().RealPart();
waveout = waveout.Add(slice, offset);
offset += (uint)(slice.Length / 2);
}
return(waveout);
}
/// <summary>
/// Calculates incremental and decremental running average.
/// </summary>
/// <param name="data">Sample data.</param>
/// <param name="onCalculated">
/// Action callback which fires on each element addition-removal.
/// <para>Parameters are: (index, incremental average, decremental average).</para>
/// </param>
public static void RunningAverageIncDec(this IList <double[]> data, Action <int, double[], double[]> onCalculated)
{
var dim = data.First().Length;
var avgInc = new double[dim];
var avgDec = data.Average();
for (int i = 0; i < data.Count; i++)
{
var item = data[i];
avgInc = avgInc.Add(UpdateAverageIncremental(avgInc, i, item));
avgDec = avgDec.Subtract(UpdateAverageDecremental(avgDec, data.Count - i, item));
onCalculated(i, avgInc, avgDec);
}
}
/// <summary>
/// Calculates incremental running average and variance.
/// </summary>
/// <param name="data">Sample data.</param>
/// <param name="onCalculated">
/// Action callback which fires on each element addition.
/// <para>Parameters are: (index, incremental average, incremental variance).</para>
/// </param>
public static void RunningVarianceIncremental(this IList <double[]> data, Action <int, double[], double> onCalculated)
{
var dim = data.First().Length;
double[] avg = new double[dim];
double varianceSum = 0d;
for (int i = 0; i < data.Count; i++)
{
var item = data[i];
var prevAvg = avg;
avg = avg.Add(RunningAverage.UpdateAverageIncremental(avg, i, item));
varianceSum += UpdateVarianceIncremental(prevAvg, avg, varianceSum, i, item);
onCalculated(i, avg, varianceSum);
}
}
public double[, ][] Estimate(MixtureCoefficientEstimationParameters <TDistribution> parameters)
{
if (_mu != null)
{
return(_mu);
}
try
{
_mu = new double[parameters.Model.N, parameters.L][];
for (var i = 0; i < parameters.Model.N; i++)
{
for (var l = 0; l < parameters.L; l++)
{
var denominator = 0.0d;
var nominator = new double[parameters.Observations[0].Dimention];
for (var t = 0; t < parameters.Observations.Count; t++)
{
// TODO : weights here
var weight = GetWeightValue(t, parameters.ObservationWeights);
var x = parameters.Observations[t].Value;
var gamma = (parameters.Model.Normalized) ? LogExtention.eExp(parameters.GammaComponents[t][i, l])
: parameters.GammaComponents[t][i, l];
denominator += weight * gamma;
x = x.Product(gamma * weight);
nominator = nominator.Add(x);
}
_mu[i, l] = nominator.Product(1 / denominator);
}
}
}
catch (Exception)
{
for (var i = 0; i < parameters.Model.N; i++)
{
for (var l = 0; l < parameters.L; l++)
{
Debug.WriteLine("Mixture Mu [{0},{1}] : {2}", i, l, new Vector(_mu[i, l]));
}
}
throw;
}
return(_mu);
}
