/// Create an instance of the receiver with parameters estimated from
/// the given histogram using best guesses. This method can be used to
/// find the initial values for a fit.
/// @param h DhbScientificCurves.Histogram
public UniformDistribution(Histogram h)
{
_b = h.StandardDeviation * 1.73205080756888; // sqrt(12)/2
double c = h.Average;
_a = c - _b;
_b += c;
}
/// Create an instance of the receiver with parameters estimated from
/// the given histogram using best guesses. This method can be used to
/// find the initial values for a fit.
/// @param h DhbScientificCurves.Histogram
public TriangularDistribution(Histogram h)
{
_b = h.StandardDeviation * 1.73205080756888; // sqrt(12)/2
_c = h.Average;
_a = _c - _b;
_b += _c;
}
/// Create an instance of the receiver with parameters estimated from the
/// given histogram using best guesses. This method can be used to
/// find the initial values for a fit.
/// @param h DhbScientificCurves.Histogram
/// @exception ArgumentOutOfRangeException when no suitable parameter can be found.
public FisherTippettDistribution(Histogram h)
{
double beta = h.StandardDeviation;
if (beta < 0)
throw new ArgumentOutOfRangeException("Histogram has vanishing standard deviation");
beta *= System.Math.Sqrt(6) / System.Math.PI;
DefineParameters(h.Average - 0.5772156649 * beta, beta);
}
/// Create an instance of the receiver with parameters estimated from
/// the given histogram using best guesses. This method can be used to
/// find the initial values for a fit.
/// @param h DhbScientificCurves.Histogram
/// @exception ArgumentOutOfRangeException
/// when no suitable parameter can be found.
public BetaDistribution(Histogram h)
{
if (h.Minimum < 0 || h.Maximum > 1)
throw new ArgumentOutOfRangeException("Beta distribution is only defined over [0,1]");
double average = h.Average;
double variance = h.Variance;
double a = ((1 - average) / variance - 1) * average;
if (a <= 0)
throw new ArgumentOutOfRangeException("Negative shape parameter");
double b = (1 / average - 1) * a;
if (b <= 0)
throw new ArgumentOutOfRangeException("Negative shape parameter");
DefineParameters(a, b);
}
/// Create an instance of the receiver with parameters estimated from
/// the given histogram using best guesses. This method can be used to
/// find the initial values for a fit.
/// @param h DhbScientificCurves.Histogram
/// @exception ArgumentOutOfRangeException
/// when no suitable parameter can be found.
public WeibullDistribution(Histogram h)
{
if (h.Minimum < 0)
throw new ArgumentOutOfRangeException(
"Weibull distribution is only defined for non-negative values");
double average = h.Average;
if (average <= 0)
throw new ArgumentOutOfRangeException(
"Weibull distribution must have a non-negative average");
double xMin = (h.Minimum + average) * 0.5;
double accMin = System.Math.Log(-System.Math.Log(1 - h.CountsUpTo(xMin) / h.TotalCount));
double xMax = (h.Maximum + average) * 0.5;
double accMax = System.Math.Log(-System.Math.Log(1 - h.CountsUpTo(xMax) / h.TotalCount));
double delta = accMax - accMin;
xMin = System.Math.Log(xMin);
xMax = System.Math.Log(xMax);
DefineParameters(delta / (xMax - xMin),
System.Math.Exp((accMax * xMin - accMin * xMax) / delta));
}
/// Create an instance of the receiver with parameters estimated from
/// the given histogram using best guesses. This method can be used to
/// find the initial values for a fit.
/// @param h Histogram
public CauchyDistribution(Histogram h)
: this(h.Average,
4 * h.Variance /System.Math.Sqrt(System.Math.PI *( h.Minimum
* h.Minimum +h.Maximum * h.Maximum ) ))
{
}
//TODO: this constructor is mentioned in section 9.8.3 but it's not part of Listing 9.24 code
public HistogrammedDistribution(Histogram histogram)
{
_histogram = histogram;
}
/// Create an instance of the receiver with parameters estimated from
/// the given histogram using best guesses. This method can be used to
/// find the initial values for a fit.
/// @param h DhbScientificCurves.Histogram
public LaplaceDistribution(Histogram h)
: this(h.Average, System.Math.Sqrt( 0.5 * h.Variance))
{
}
/// @return double t-test confidence level with data accumulated
/// in the supplied histogram.
/// @param h DhbScientificCurves.Histogram
public double TConfidenceLevel(Histogram h)
{
return _moments.TConfidenceLevel(h._moments);
}
