StableNoiseSource(
double exponent,
double skewness
)
{
_distribution = new StableDistribution(0.0, 1.0, exponent, skewness);
}
private void TestContinuousDistributionShape(
IContinuousGenerator distribution,
double min,
double max,
double[] expectedShape,
double expectedUnderflow,
double expectedOverflow,
int avgSamplesPerBucket,
double absoluteAccuracy,
string message)
{
DistributionShape shape = DistributionShape.CreateMinMax(expectedShape.Length, min, max);
int sampleCount = expectedShape.Length * avgSamplesPerBucket;
for (int i = 0; i < sampleCount; i++)
{
shape.Push(distribution.NextDouble());
}
double scale = 1.0 / (avgSamplesPerBucket * expectedShape.Length);
Assert.That(shape.Underflow * scale, Is.EqualTo(expectedUnderflow).Within(absoluteAccuracy), message + " Underflow");
Assert.That(shape.Overflow * scale, Is.EqualTo(expectedOverflow).Within(absoluteAccuracy), message + " Overflow");
for (int i = 0; i < expectedShape.Length; i++)
{
Assert.That(shape[i] * scale, Is.EqualTo(expectedShape[i]).Within(absoluteAccuracy), message + " Bucket " + i);
}
}
WhiteGaussianNoiseSource(
double mean,
double standardDeviation
)
{
// assuming the default random source is white
_distribution = new NormalDistribution(mean, standardDeviation);
}
/// <summary>
/// Create a gaussian noise source with normally distributed amplites.
/// </summary>
/// <param name="mean">mu-parameter of the normal distribution</param>
/// <param name="standardDeviation">sigma-parameter of the normal distribution</param>
public WhiteGaussianNoiseSource(
double mean,
double standardDeviation
)
{
// assuming the default random source is white
_distribution = new NormalDistribution(mean, standardDeviation);
}
/// <summary>
/// Create a skew alpha stable noise source.
/// </summary>
/// <param name="location">mu-parameter of the stable distribution</param>
/// <param name="scale">c-parameter of the stable distribution</param>
/// <param name="exponent">alpha-parameter of the stable distribution</param>
/// <param name="skewness">beta-parameter of the stable distribution</param>
public StableNoiseSource(
double location,
double scale,
double exponent,
double skewness
)
{
_distribution = new StableDistribution(location, scale, exponent, skewness);
}
StableNoiseSource(
double location,
double scale,
double exponent,
double skewness
)
{
_distribution = new StableDistribution(location, scale, exponent, skewness);
}
/// <summary>
/// Create a gaussian noise source with normally distributed amplitudes.
/// </summary>
/// <param name="uniformWhiteRandomSource">Uniform white random source.</param>
/// <param name="mean">mu-parameter of the normal distribution</param>
/// <param name="standardDeviation">sigma-parameter of the normal distribution</param>
public WhiteGaussianNoiseSource(
RandomSource uniformWhiteRandomSource,
double mean,
double standardDeviation
)
{
NormalDistribution gaussian = new NormalDistribution(uniformWhiteRandomSource);
gaussian.SetDistributionParameters(mean, standardDeviation);
_distribution = gaussian;
}
WhiteGaussianNoiseSource(
RandomSource uniformWhiteRandomSource,
double mean,
double standardDeviation
)
{
NormalDistribution gaussian = new NormalDistribution(uniformWhiteRandomSource);
gaussian.SetDistributionParameters(mean, standardDeviation);
_distribution = gaussian;
}
/// <summary>
/// Create a skew alpha stable noise source.
/// </summary>
/// <param name="uniformWhiteRandomSource">Uniform white random source.</param>
/// <param name="location">mu-parameter of the stable distribution</param>
/// <param name="scale">c-parameter of the stable distribution</param>
/// <param name="exponent">alpha-parameter of the stable distribution</param>
/// <param name="skewness">beta-parameter of the stable distribution</param>
public StableNoiseSource(
RandomSource uniformWhiteRandomSource,
double location,
double scale,
double exponent,
double skewness
)
{
StableDistribution stable = new StableDistribution(uniformWhiteRandomSource);
stable.SetDistributionParameters(location, scale, exponent, skewness);
_distribution = stable;
}
Random(
int n,
IContinuousGenerator randomDistribution
)
{
double[] data = new double[n];
for (int i = 0; i < data.Length; i++)
{
data[i] = randomDistribution.NextDouble();
}
return(new Vector(data));
}
StableNoiseSource(
RandomSource uniformWhiteRandomSource,
double location,
double scale,
double exponent,
double skewness
)
{
StableDistribution stable = new StableDistribution(uniformWhiteRandomSource);
stable.SetDistributionParameters(location, scale, exponent, skewness);
_distribution = stable;
}
Random(
int n,
IContinuousGenerator randomDistribution)
{
Complex[] data = new Complex[n];
for (int i = 0; i < data.Length; i++)
{
data[i] = Complex.Random(
randomDistribution,
randomDistribution);
}
return(new ComplexVector(data));
}
GeneratorSource(
IContinuousGenerator generator
)
{
_generator = generator;
}
WhiteGaussianNoiseSource()
{
// assuming the default random source is white
_distribution = new StandardDistribution();
}
/// <summary>
/// Create a gaussian noise source with standard distributed amplitudes.
/// </summary>
public WhiteGaussianNoiseSource()
{
// assuming the default random source is white
_distribution = new StandardDistribution();
}
/// <summary>
/// Create a skew alpha stable noise source.
/// </summary>
/// <param name="exponent">alpha-parameter of the stable distribution</param>
/// <param name="skewness">beta-parameter of the stable distribution</param>
public StableNoiseSource(
double exponent,
double skewness
)
{
_distribution = new StableDistribution(0.0, 1.0, exponent, skewness);
}
private void TestContinuousDistributionShape(
IContinuousGenerator distribution,
double min,
double max,
double[] expectedShape,
double expectedUnderflow,
double expectedOverflow,
int avgSamplesPerBucket,
double absoluteAccuracy,
string message)
{
DistributionShape shape = DistributionShape.CreateMinMax(expectedShape.Length, min, max);
int sampleCount = expectedShape.Length * avgSamplesPerBucket;
for(int i = 0; i < sampleCount; i++)
{
shape.Push(distribution.NextDouble());
}
double scale = 1.0 / (avgSamplesPerBucket * expectedShape.Length);
Assert.That(shape.Underflow * scale, Is.EqualTo(expectedUnderflow).Within(absoluteAccuracy), message + " Underflow");
Assert.That(shape.Overflow * scale, Is.EqualTo(expectedOverflow).Within(absoluteAccuracy), message + " Overflow");
for(int i = 0; i < expectedShape.Length; i++)
{
Assert.That(shape[i] * scale, Is.EqualTo(expectedShape[i]).Within(absoluteAccuracy), message + " Bucket " + i);
}
}
public void Setup()
{
_random = new StableDistribution(0.0, 1.0, 0.5, 0.75);
_formatter = new BinaryFormatter();
}
/// <summary>
/// Create a sample source from a continuous generator.
/// </summary>
public GeneratorSource(
IContinuousGenerator generator
)
{
_generator = generator;
}
