/// <summary>
/// Creates a new SparseGP which the product of two other SparseGPs
/// </summary>
/// <param name="a">First SparseGP</param>
/// <param name="b">Second SparseGP</param>
/// <returns>Result</returns>
public static SparseGP operator *(SparseGP a, SparseGP b)
{
SparseGP result = new SparseGP(a.FixedParameters);
result.SetToProduct(a, b);
return(result);
}
/// <summary>
/// Max difference between two sparse GPs - used for
/// convergence testing
/// </summary>
/// <param name="thatd">That sparse GP which will be compared to this sparse GP</param>
/// <returns></returns>
public double MaxDiff(object thatd)
{
SparseGP that = thatd as SparseGP;
// Prior mean and kernel references should be the same
// Low rank lists are ignored
if (that == null ||
this.FixedParameters != that.FixedParameters ||
this.IncludePrior != that.IncludePrior ||
this.IsPointMass != that.IsPointMass ||
this.IsUniform() != that.IsUniform())
{
return(double.PositiveInfinity);
}
if (this.IsUniform() && that.IsUniform())
{
return(0.0);
}
if (this.IsPointMass)
{
// both point masses
if (this.Point is Diffable)
{
return(((Diffable)this.Point).MaxDiff(that.Point));
}
else
{
return((this.Point == that.Point) ? 0.0 : double.PositiveInfinity);
}
}
return(this.InducingDist.MaxDiff(that.InducingDist));
}
/// <summary>
/// Creates a new SparseGP which the ratio of two other SparseGPs
/// </summary>
/// <param name="numerator">numerator SparseGP</param>
/// <param name="denominator">denominator SparseGP</param>
/// <returns>Result</returns>
public static SparseGP operator /(SparseGP numerator, SparseGP denominator)
{
SparseGP result = new SparseGP(numerator.FixedParameters);
result.SetToRatio(numerator, denominator);
return(result);
}
/// <summary>
/// Creates a sparse GP point mass - i.e. all the mass is at a given function
/// </summary>
/// <param name="sgpf"></param>
/// <param name="value"></param>
/// <returns></returns>
public static SparseGP PointMass(SparseGPFixed sgpf, IFunction value)
{
SparseGP sgp = new SparseGP(sgpf, false);
sgp.Point = value;
return(sgp);
}
/// <summary>
/// Sets this instance to the product of two sparse GPs.
/// </summary>
/// <param name="a">Sparse GP</param>
/// <param name="b">Sparse GP</param>
public void SetToProduct(SparseGP a, SparseGP b)
{
if (a.FixedParameters != b.FixedParameters)
{
throw new ArgumentException("SparseGPs do not have the same FixedParameters. a.FixedParameters = " + a.FixedParameters + ", b.FixedParameters = " +
b.FixedParameters);
}
FixedParameters = a.FixedParameters;
if (a.IncludePrior && b.IncludePrior)
{
throw new ArgumentException("Both SparseGPs include the prior. Cannot multiply.");
}
IncludePrior = a.IncludePrior || b.IncludePrior;
if (a.IsPointMass)
{
if (b.IsPointMass && !a.Point.Equals(b.Point))
{
throw new AllZeroException();
}
Point = a.Point;
}
else if (b.IsPointMass)
{
Point = b.Point;
}
else
{
InducingDist.SetToProduct(a.InducingDist, b.InducingDist);
pointFunc = null;
ClearCachedValues();
}
}
/// <summary>
/// Sets this SparseGP distribution to the weighted sum of two other such distributions
/// </summary>
/// <param name="weight1"></param>
/// <param name="value1"></param>
/// <param name="weight2"></param>
/// <param name="value2"></param>
/// <remarks>Not yet implemented</remarks>
public void SetToSum(double weight1, SparseGP value1, double weight2, SparseGP value2)
{
if (value1.FixedParameters != value2.FixedParameters)
{
throw new ArgumentException("SparseGPs do not have the same FixedParameters. a.FixedParameters = " + value1.FixedParameters + ", b.FixedParameters = " +
value2.FixedParameters);
}
FixedParameters = value1.FixedParameters;
if (value1.IncludePrior != value2.IncludePrior)
{
throw new ArgumentException("One Sparse GP includes a prior, the other does not. Cannot add.");
}
IncludePrior = value1.IncludePrior;
InducingDist.SetToSum(weight1, value1.InducingDist, weight2, value2.InducingDist);
// The only time the result is a point mass is if both sources are the same point mass
if (InducingDist.IsPointMass)
{
pointFunc = value1.pointFunc;
}
else
{
pointFunc = null;
}
ClearCachedValues();
}
/// <summary>
/// k(x)
/// </summary>
public double K_x_x(SparseGP sgpb)
{
if (double.IsNaN(kxx))
{
kxx = sgpb.FixedParameters.Prior.Variance(Xi);
}
return(kxx);
}
/// <summary>
/// K(B,x). This is a calculated Vector maintained
/// by the class
/// </summary>
public Vector K_B_x(SparseGP sgpb)
{
if (kBx == null)
{
if (Xi != null)
{
kBx = sgpb.FixedParameters.KernelOf_X_B(Xi);
}
}
return(kBx);
}
/// <summary>
/// p = Inv(K(B,B)) * K(B,x). This is a calculated Vector maintained
/// by the class
/// </summary>
public Vector P(SparseGP sgpb)
{
if (pvec == null)
{
Vector KBx = K_B_x(sgpb);
if (KBx != null)
{
pvec = sgpb.FixedParameters.InvKernelOf_B_B * KBx;
}
}
return(pvec);
}
/// <summary>
/// Sets this instance to the ratio of two sparse GPs.
/// </summary>
/// <param name="numerator">Sparse GP</param>
/// <param name="denominator">Sparse GP</param>
/// <param name="forceProper"></param>
public void SetToRatio(SparseGP numerator, SparseGP denominator, bool forceProper = false)
{
if (numerator.FixedParameters != denominator.FixedParameters)
{
throw new ArgumentException("SparseGPs do not have the same FixedParameters. numerator.FixedParameters = " + numerator.FixedParameters +
", denominator.FixedParameters = " + denominator.FixedParameters);
}
FixedParameters = numerator.FixedParameters;
if (numerator.IncludePrior)
{
IncludePrior = !denominator.IncludePrior;
}
else if (denominator.IncludePrior)
{
throw new ArgumentException("Only the denominator includes the prior. Cannot divide.");
}
else
{
IncludePrior = false; // neither include the prior
}
if (numerator.IsPointMass)
{
if (denominator.IsPointMass)
{
if (numerator.Point.Equals(denominator.Point))
{
SetToUniform();
}
else
{
throw new DivideByZeroException();
}
}
else
{
Point = numerator.Point;
}
}
else if (denominator.IsPointMass)
{
throw new DivideByZeroException();
}
else
{
// neither is point mass
InducingDist.SetToRatio(numerator.InducingDist, denominator.InducingDist, forceProper);
pointFunc = null;
ClearCachedValues();
}
}
/// <summary>
/// Sets one sparse GP to another. Everything is copied
/// except the FixedParameters and the lsit of rank 1 potentials
/// which are referenced.
/// </summary>
/// <param name="that">The sparse GP to copy</param>
public void SetTo(SparseGP that)
{
fixedParameters = that.FixedParameters;
InducingDist.SetTo(that.InducingDist);
IncludePrior = that.IncludePrior;
pointFunc = that.pointFunc;
if (that.alpha != null)
{
alpha = Vector.Copy(that.alpha);
}
else
{
alpha = null;
}
if (that.beta != null)
{
beta = new PositiveDefiniteMatrix(that.beta as Matrix);
}
else
{
beta = null;
}
if (that.meanB != null)
{
meanB = Vector.Copy(that.meanB);
}
else
{
meanB = null;
}
if (that.varBB != null)
{
varBB = new PositiveDefiniteMatrix(that.varBB as Matrix);
}
else
{
varBB = null;
}
}
/// <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(SparseGP that, double power)
{
throw new NotImplementedException();
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
{
}
}
#pragma warning disable 162
#endif
/// <summary>
/// Gets the log of the integral of the product of this SparseGP and that SparseGP
/// </summary>
/// <param name="that"></param>
/// <returns></returns>
public double GetLogAverageOf(SparseGP that)
{
if (this.FixedParameters != that.FixedParameters)
{
throw new ArgumentException("SparseGPs do not have the same FixedParameters. this.FixedParameters = " + this.FixedParameters + ", that.FixedParameters = " +
that.FixedParameters);
}
if (this.IncludePrior && that.IncludePrior)
{
throw new ArgumentException("Both SparseGPs include the prior");
}
if (that.IsPointMass)
{
return(GetLogProb(that.Point));
}
if (this.IsPointMass)
{
return(that.GetLogProb(this.Point));
}
if (this.IncludePrior && !that.IncludePrior)
{
// gBB is the distribution of the function on the basis
VectorGaussian gBB;
if (true)
{
gBB = new VectorGaussian(InducingDist.Dimension);
gBB.Precision.SetToSum(FixedParameters.InvKernelOf_B_B, InducingDist.Precision);
gBB.MeanTimesPrecision.SetTo(InducingDist.MeanTimesPrecision); // since prior has zero mean
}
else
{
// equivalent but slower
gBB = VectorGaussian.FromMeanAndVariance(Mean_B, Var_B_B);
}
return(gBB.GetLogAverageOf(that.InducingDist));
}
if (!this.IncludePrior && that.IncludePrior)
{
return(that.GetLogAverageOf(this));
}
throw new NotImplementedException();
}
/// <summary>
/// Sets this sparse GP the the power of another sparse GP
/// </summary>
/// <param name="dist"></param>
/// <param name="exponent"></param>
public void SetToPower(SparseGP dist, double exponent)
{
if (exponent == 1.0)
{
SetTo(dist);
}
else
{
FixedParameters = dist.FixedParameters;
if (exponent == 0.0)
{
SetToUniform();
}
else if (dist.IsPointMass)
{
if (exponent < 0)
{
throw new DivideByZeroException("The exponent is negative and the distribution is a point mass");
}
else
{
Point = dist.Point;
}
}
else if (dist.IncludePrior)
{
throw new ArgumentException("Cannot raise prior to a power.");
}
else
{
IncludePrior = dist.IncludePrior;
InducingDist.SetToPower(dist.InducingDist, exponent);
pointFunc = null;
ClearCachedValues();
}
}
}
/// <summary>
/// Copy constructor
/// </summary>
/// <param name="that"></param>
public SparseGP(SparseGP that)
{
InducingDist = new VectorGaussian(that.FixedParameters.NumberBasisPoints);
SetTo(that);
}
#pragma warning restore 162
#endif
/// <summary>
/// The expected logarithm of that distribution under this distribution
/// </summary>
/// <param name="that"></param>
/// <returns></returns>
/// <remarks>Not yet implemented</remarks>
public double GetAverageLog(SparseGP that)
{
throw new NotImplementedException();
}