/// <summary>
/// Creates a multi-dimensional Wishart with given shape and with
/// a rate matrix which is set to a scaled identity matrix
/// </summary>
/// <param name="dimension">The dimension</param>
/// <param name="shape">The shape parameter</param>
/// <param name="rate">Used to scale the identity matrix</param>
/// <returns>A new Wishart distribution</returns>
public static Wishart FromShapeAndRate(int dimension, double shape, double rate)
{
//return FromShapeAndRate(shape, PositiveDefiniteMatrix.IdentityScaledBy(dimension,rate));
Wishart result = new Wishart(dimension);
result.Shape = shape;
result.Rate.SetToIdentityScaledBy(rate);
return(result);
}
/// <summary>
/// Override of the Equals method
/// </summary>
/// <param name="thatd">The instance to compare to</param>
/// <returns>True if the two distributions are the same in value, false otherwise</returns>
/// <exclude/>
public override bool Equals(object thatd)
{
Wishart that = thatd as Wishart;
if (that == null)
{
return(false);
}
return(MaxDiff(that) == 0.0);
}
/// <summary>
/// Sets this Wishart instance to have the parameter values of another Wishart instance
/// </summary>
/// <param name="that">The other Wishart</param>
public void SetTo(Wishart that)
{
if (object.ReferenceEquals(that, null))
{
SetToUniform();
}
else
{
rate.SetTo(that.rate);
shape = that.shape;
}
}
/// <summary>
/// Sets the parameters to represent the product of two Wisharts.
/// </summary>
/// <param name="g1">The first Wishart. May refer to <c>this</c>.</param>
/// <param name="g2">The second Wishart. May refer to <c>this</c>.</param>
/// <remarks>
/// The result may not be proper. No error is thrown in this case.
/// </remarks>
public void SetToProduct(Wishart g1, Wishart g2)
{
if (g1.IsPointMass)
{
Point = g1.Point;
return;
}
if (g2.IsPointMass)
{
Point = g2.Point;
return;
}
shape = g1.shape + g2.shape - 0.5 * (Dimension + 1);
rate.SetToSum(g1.rate, g2.rate);
}
/// <summary>
/// The maximum difference between the parameters of this Wishart
/// and that Wishart
/// </summary>
/// <param name="thatd">That Wishart</param>
/// <returns>The maximum difference</returns>
public double MaxDiff(object thatd)
{
Wishart that = thatd as Wishart;
if (that == null)
{
return(Double.PositiveInfinity);
}
double max = rate.MaxDiff(that.rate);
double diff = MMath.AbsDiff(shape, that.shape);
if (diff > max)
{
max = diff;
}
return(max);
}
/// <summary>
/// Gets the log-integral of the product of this Wishart with another Wishart
/// </summary>
/// <param name="that">The other Wishart</param>
/// <returns>The log inner product</returns>
public double GetLogAverageOf(Wishart that)
{
if (IsPointMass)
{
return(that.GetLogProb(Point));
}
else if (that.IsPointMass)
{
return(GetLogProb(that.Point));
}
else
{
Wishart product = this * that;
//if (!product.IsProper()) throw new ArgumentException("The product is improper.");
return(product.GetLogNormalizer() - this.GetLogNormalizer() - that.GetLogNormalizer());
}
}
/// <summary>
/// Get the integral of this distribution times another distribution raised to a power.
/// </summary>
/// <param name="that"></param>
/// <param name="power"></param>
/// <returns></returns>
public double GetLogAverageOfPower(Wishart that, double power)
{
if (IsPointMass)
{
return(power * that.GetLogProb(Point));
}
else if (that.IsPointMass)
{
if (power < 0)
{
throw new DivideByZeroException("The exponent is negative and the distribution is a point mass");
}
return(this.GetLogProb(that.Point));
}
else
{
var product = this * (that ^ power);
return(product.GetLogNormalizer() - this.GetLogNormalizer() - power * that.GetLogNormalizer());
}
}
/// <summary>
/// The expected logarithm of that distribution under this distribution.
/// </summary>
/// <param name="that">The distribution to take the logarithm of.</param>
/// <returns><c>sum_x this.Evaluate(x)*Math.Log(that.Evaluate(x))</c></returns>
/// <remarks>This is also known as the cross entropy.</remarks>
public double GetAverageLog(Wishart that)
{
if (that.IsPointMass)
{
if (this.IsPointMass && (this.Point == that.Point))
{
return(0.0);
}
else
{
return(Double.NegativeInfinity);
}
}
else
{
// that is not a point mass.
double result = (that.Shape - 0.5 * (that.Dimension + 1)) * this.GetMeanLogDeterminant();
result -= Matrix.TraceOfProduct(this.GetMean(), that.Rate);
result -= that.GetLogNormalizer();
return(result);
}
}
public static double ProbEqualLn(IEnumerable <Wishart> dists)
{
// are any of them point masses?
PositiveDefiniteMatrix point = null;
foreach (Wishart dist in dists)
{
if (dist.IsPointMass)
{
point = dist.Point;
break;
}
}
if (point != null)
{
// one is a point mass
double sum = 0.0;
foreach (Wishart dist in dists)
{
sum += dist.EvaluateLn(point);
}
return(sum);
}
else
{
double sum = 0.0;
int dimension = 0;
foreach (Wishart dist in dists)
{
dimension = dist.Dimension;
sum += dist.Normalizer();
}
Wishart prod = new Wishart(dimension);
Util.SetToProduct(prod, dists);
sum -= prod.Normalizer();
return(sum);
}
}
/// <summary>
/// Weighted mixture distribution for two Wisharts
/// </summary>
/// <param name="weight1">First weight</param>
/// <param name="dist1">First Wishart</param>
/// <param name="weight2">Second weight</param>
/// <param name="dist2">Second Wishart</param>
public void SetToSum(double weight1, Wishart dist1, double weight2, Wishart dist2)
{
SetTo(WeightedSum <Wishart>(this, Dimension, weight1, dist1, weight2, dist2));
}
