public static double AverageLogFactor(
Vector SampleMean,
double scaling,
GPBuffer Buffer)
{
return(VectorGaussianScaledPrecisionOp.AverageLogFactor(SampleMean, scaling, Buffer.ESamplePrecisionSample, Buffer.PrecisionMeanLogDet));
}
public static VectorGaussian SampleAverageLogarithm(double scaling, GPBuffer Buffer, VectorGaussian result)
{
result.Precision.SetTo(Buffer.Precision);
if (scaling != 1.0)
{
result.Precision.Scale(scaling);
}
return(result);
}
/// <summary>
/// Uses the KernelOptimiser class to optimise the hypers given the current variational posterior
/// on the function values (which has mean SampleMean and covariance SampleCovariance)
/// </summary>
public static GPBuffer BufferHelper(int[] hypersToOptimise, GPBuffer Buffer, Vector[] x, Vector SampleMean, PositiveDefiniteMatrix SampleVariance, Gamma scaling)
{
if (SampleMean.All(o => o == 0.0))
{
Buffer.Precision = Utils.GramMatrix(Buffer.kernel, x).Inverse();
}
else
{
//Console.WriteLine(Utils.KernelToArray(Buffer.kernel).Select(o => o.ToString()).Aggregate((p, q) => p + " " + q));
var helper = new KernelOptimiser(settings);
helper.kernel = Buffer.kernel;
helper.xData = x;
helper.hypersToOptimise = hypersToOptimise;
helper.Optimise((prec, gradK, gradientVector) =>
helperFunction(prec, gradK, gradientVector, scaling, SampleMean,
SampleVariance), ref Buffer.Precision);
Buffer.ESamplePrecisionSample = VectorGaussianScaledPrecisionOp.ESamplePrecisionSample(SampleMean, SampleVariance, Buffer.Precision);
Buffer.PrecisionMeanLogDet = VectorGaussianScaledPrecisionOp.PrecisionMeanLogDet(Buffer.Precision);
//Console.WriteLine(Utils.KernelToArray(Buffer.kernel).Select(o => o.ToString()).Aggregate((p, q) => p + " " + q));
rememberKernel = Buffer.kernel;
}
return(Buffer);
}
public static GPBuffer Buffer(int[] hypersToOptimise, GPBuffer Buffer, Vector[] x, [SkipIfUniform] Vector SampleMean, [SkipIfUniform] PositiveDefiniteMatrix SampleVariance, double scaling)
{
return(BufferHelper(hypersToOptimise, Buffer, x, SampleMean, SampleVariance, Gamma.PointMass(scaling)));
}
public static Gamma ScalingAverageLogarithm([Fresh] GPBuffer Buffer) //SampleVector SampleMean, [Fresh, SkipIfUniform] PositiveDefiniteMatrix SampleVariance, )
{
return(Gamma.FromNatural(.5 * Buffer.Precision.Rows, .5 * Buffer.ESamplePrecisionSample /*(SampleMean, SampleVariance, Precision)*/));
}
/// <summary>
/// VMP message to 'sample'
/// </summary>
/// <param name="Mean">Constant value for 'mean'.</param>
/// <param name="Precision">Constant value for 'precision'.</param>
/// <param name="result">Modified to contain the outgoing message</param>
/// <returns><paramref name="result"/></returns>
/// <remarks><para>
/// The outgoing message is the factor viewed as a function of 'sample' conditioned on the given values.
/// </para></remarks>
public static VectorGaussian SampleAverageLogarithm([SkipIfUniform] Gamma scaling, GPBuffer Buffer, VectorGaussian result)
{
result.Precision.SetTo(Buffer.Precision);
result.Precision.Scale(scaling.GetMean());
return(result);
}
