public static double[] NormalDistribution(double[] x)
{
var mean = WaveMath.Mean(x);
var deviation = WaveMath.StandardDeviation(x);
var samples = new List <KeyValuePair <int, double> >();
var result = MemoryPool.Pool.New <double>(x.Length);
for (var i = 0; i < x.Length; i++)
{
var norm = WaveMath.ProbabilityDensityFunction(x[i], mean, deviation);
samples.Add(new KeyValuePair <int, double>(i, norm));
result[i] = norm;
}
return(samples.OrderBy(it => it.Value).Select(it => it.Value).ToArray());
}
/// <summary>
/// Estimates the disturbances in the signal based on the normal distribution of the details coefficients
/// </summary>
/// <param name="threshold">The higher the threshold, the higher the tolerance in flutuations on energy of the details</param>
/// <param name="minimunDistance">Minimun distance between disturbances to consider a new one</param>
/// <returns></returns>
public List <Disturbance> GetDisturbances(double threshold = 0.1, int minimunDistance = 3)
{
threshold = 1 - threshold;
var disturbances = new List <Disturbance>();
var mean = WaveMath.Mean(Details);
var deviation = WaveMath.StandardDeviation(Details);
var samples = new List <KeyValuePair <int, double> >();
var min = double.MaxValue;
var max = double.MinValue;
for (var i = 0; i < Details.Length; i++)
{
var sample = Details[i];
var norm = WaveMath.ProbabilityDensityFunction(sample, mean, deviation);
if (double.IsNaN(norm))
{
continue;
}
if (norm < min)
{
min = norm;
}
if (norm > max)
{
max = norm;
}
samples.Add(new KeyValuePair <int, double>(i, norm));
}
//ajusta a escala da distribuição normal para 0..1, removendo os valores maiores que threshold
for (var i = samples.Count - 1; i >= 0; i--)
{
var scaledNorm = WaveMath.Scale(samples[i].Value, min, max, 0, 1);
samples[i] = new KeyValuePair <int, double>(i, scaledNorm);
if (scaledNorm > threshold)
{
samples.RemoveAt(i);
}
else
{
samples[i] = new KeyValuePair <int, double>(i, scaledNorm);
}
}
int?start = null;
var startIndex = 0;
for (var i = 0; i < samples.Count; i++)
{
if (start == null)
{
start = i;
startIndex = samples[i].Key;
}
else if (samples[i].Key - samples[i - 1].Key >= minimunDistance || i == samples.Count - 1)
{
disturbances.Add(new Disturbance(startIndex, samples[i - 1].Key, Details.Length, Signal.Samples.Length));
if (i < samples.Count - 1)
{
start = i;
startIndex = samples[i].Key;
}
else
{
disturbances.Add(new Disturbance(samples[i].Key, samples[i].Key, Details.Length, Signal.Samples.Length));
}
}
}
if (disturbances.Count > 1 && (disturbances[disturbances.Count - 1].Finish - disturbances[disturbances.Count - 2].Finish) < minimunDistance)
{
disturbances[disturbances.Count - 2] = new Disturbance(disturbances[disturbances.Count - 2].Start, disturbances[disturbances.Count - 1].Finish, Details.Length, Signal.Samples.Length);
disturbances.RemoveAt(disturbances.Count - 1);
}
return(disturbances);
}
