internal void GaussianOpQ_Timing()
{
Gaussian X, Mean;
Gamma Precision;
Gamma q;
int n = 1;
X = Gaussian.FromNatural(3.9112579392580757, 11.631097473681082);
Mean = Gaussian.FromNatural(10.449696977834144, 5.5617978202886995);
Precision = Gamma.FromShapeAndRate(1.0112702817305146, 0.026480506235719053);
q = Gamma.FromMeanAndVariance(1, 1);
Stopwatch watch = new Stopwatch();
watch.Start();
for (int i = 0; i < n; i++)
{
GaussianOp_Laplace.Q(X, Mean, Precision, q);
}
watch.Stop();
Console.WriteLine("Q = {0}", watch.ElapsedTicks);
watch.Restart();
for (int i = 0; i < n; i++)
{
GaussianOp_Laplace.Q_Slow(X, Mean, Precision);
}
watch.Stop();
Console.WriteLine("Q2 = {0}", watch.ElapsedTicks);
}
internal void StudentIsPositiveTest4()
{
double shape = 1;
Gamma precPrior = Gamma.FromShapeAndRate(shape, shape);
// mean=-1 causes improper messages
double mean = -1;
Gaussian meanPrior = Gaussian.PointMass(mean);
double evExpected;
Gaussian xExpected = StudentIsPositiveExact(mean, precPrior, out evExpected);
GaussianOp.ForceProper = false;
GaussianOp_Laplace.modified = true;
GaussianOp_Laplace.modified2 = true;
Gaussian xF = Gaussian.Uniform();
Gaussian xB = Gaussian.Uniform();
Gamma q = GaussianOp_Laplace.QInit();
double r0 = 0.38;
r0 = 0.1;
for (int iter = 0; iter < 20; iter++)
{
q = GaussianOp_Laplace.Q(xB, meanPrior, precPrior, q);
//xF = GaussianOp_Laplace.SampleAverageConditional(xB, meanPrior, precPrior, q);
xF = Gaussian.FromMeanAndPrecision(mean, r0);
xB = IsPositiveOp.XAverageConditional(true, xF);
Console.WriteLine("xF = {0} xB = {1}", xF, xB);
}
Console.WriteLine("x = {0} should be {1}", xF * xB, xExpected);
double[] precs = EpTests.linspace(1e-3, 5, 100);
double[] evTrue = new double[precs.Length];
double[] evApprox = new double[precs.Length];
double[] evApprox2 = new double[precs.Length];
//r0 = q.GetMean();
double sum = 0, sum2 = 0;
for (int i = 0; i < precs.Length; i++)
{
double r = precs[i];
Gaussian xFt = Gaussian.FromMeanAndPrecision(mean, r);
evTrue[i] = IsPositiveOp.LogAverageFactor(true, xFt) + precPrior.GetLogProb(r);
evApprox[i] = IsPositiveOp.LogAverageFactor(true, xF) + precPrior.GetLogProb(r) + xB.GetLogAverageOf(xFt) - xB.GetLogAverageOf(xF);
evApprox2[i] = IsPositiveOp.LogAverageFactor(true, xF) + precPrior.GetLogProb(r0) + q.GetLogProb(r) - q.GetLogProb(r0);
sum += System.Math.Exp(evApprox[i]);
sum2 += System.Math.Exp(evApprox2[i]);
}
Console.WriteLine("r0 = {0}: {1} {2} {3}", r0, sum, sum2, q.GetVariance() + System.Math.Pow(r0 - q.GetMean(), 2));
//TODO: change path for cross platform using
using (var writer = new MatlabWriter(@"..\..\..\Tests\student.mat"))
{
writer.Write("z", evTrue);
writer.Write("z2", evApprox);
writer.Write("z3", evApprox2);
writer.Write("precs", precs);
}
}