// Commented out since this test is very slow and does not have any assertions
//[Fact]
internal void GaussianFromMeanAndVarianceVaryTruePrec()
{
double mean = 2.0;
var Nvar = Variable.Observed <int>(50);
var n = new Range(Nvar);
var noisePrecision = 10;
var variancePriors = new double[] { 1, 5, 10 }.Select(i => Gamma.FromShapeAndRate(i, i)).ToArray();
var varPriorVar = Variable.Observed(new Gamma());
var variance_EP = Variable <double> .Random(varPriorVar);
var x_ep = GaussianFromMeanAndVarianceSampleSizeModel(variance_EP, n, true, noisePrecision);
var ie_EP = new InferenceEngine();
ie_EP.ShowWarnings = false;
ie_EP.ShowProgress = false;
var ev = Variable.Bernoulli(.5);
var modelBlock = Variable.If(ev);
var variance_MC = Variable <double> .Random(varPriorVar);
var x_mc = GaussianFromMeanAndVarianceSampleSizeModel(variance_MC, n, true, noisePrecision);
modelBlock.CloseBlock();
var ie_MC = new InferenceEngine();
ie_MC.ShowWarnings = false;
ie_MC.ShowProgress = false;
var precision_VMP = Variable.GammaFromShapeAndRate(1, 1);
var x_vmp = GaussianFromMeanAndVarianceSampleSizeModel(precision_VMP, n, false, noisePrecision);
var ie_VMP = new InferenceEngine(new VariationalMessagePassing());
ie_VMP.ShowWarnings = false;
ie_VMP.ShowProgress = false;
var ie_EP_prec = new InferenceEngine();
ie_EP_prec.ShowWarnings = false;
ie_EP_prec.ShowProgress = false;
Console.WriteLine("var " + variancePriors.Select(i => "EP" + i.Shape).Aggregate((i, j) => i + " " + j) + " "
+ variancePriors.Select(i => "MC" + i.Shape).Aggregate((i, j) => i + " " + j) + " EPp VMP EMP");
for (int j = 0; j < 10; j++)
{
Rand.Restart(2);
//int N = (int)Math.Pow(10, j + 1);
double variance = System.Math.Exp(-5 + j);
var data = Enumerable.Range(0, Nvar.ObservedValue).Select(i => Gaussian.Sample(mean, 1.0 / variance) + Gaussian.Sample(0, noisePrecision)).ToArray();
x_ep.ObservedValue = data;
x_mc.ObservedValue = data;
x_vmp.ObservedValue = data;
var epMeans = variancePriors.Select(i =>
{
double res = double.NaN;
varPriorVar.ObservedValue = i;
try
{
res = ie_EP.Infer <Gamma>(variance_EP).GetMean();
}
catch
{
}
;
return(res);
}).ToArray();
var mcMeans = variancePriors.Select(p =>
{
double varianceSample = 2;
var ge = new GammaEstimator();
varPriorVar.ObservedValue = p;
Converter <double, double> f = vari =>
{
variance_MC.ObservedValue = vari;
return(ie_MC.Infer <Bernoulli>(ev).LogOdds);
};
int burnin = 1000, thin = 5, numSamples = 1000;
var samples = new double[numSamples];
for (int i = 0; i < burnin; i++)
{
varianceSample = NonconjugateVMP2Tests.SliceSampleUnivariate(varianceSample, f, lower_bound: 0);
}
for (int i = 0; i < numSamples; i++)
{
for (int k = 0; k < thin; k++)
{
varianceSample = NonconjugateVMP2Tests.SliceSampleUnivariate(varianceSample, f, lower_bound: 0);
}
samples[i] = varianceSample;
ge.Add(varianceSample);
}
return(samples.Sum() / numSamples);
}).ToArray();
double epMean2 = double.NaN;
try
{
epMean2 = ie_EP_prec.Infer <Gamma>(precision_VMP).GetMeanInverse();
}
catch
{
}
;
double vmpMean = ie_VMP.Infer <Gamma>(precision_VMP).GetMeanInverse();
var empiricalMean = data.Sum() / data.Length;
var empVar = data.Sum(o => o * o) / data.Length - empiricalMean * empiricalMean;
Console.Write("{0:G3} ", variance);
foreach (var i in epMeans)
{
Console.Write("{0:G3} ", i);
}
foreach (var i in mcMeans)
{
Console.Write("{0:G3} ", i);
}
Console.WriteLine("{0:G3} {1:G3} {2:G3}", epMean2, vmpMean, empVar);
}
}
// Commented out since this test is very slow and does not have any assertions
//[Fact]
internal void GaussianFromMeanAndVarianceVaryNoise()
{
double mean = 2.0, variance = 2.0;
var Nvar = Variable.Observed <int>(1);
var n = new Range(Nvar);
var noisePrecision = Variable.Array <double>(n);
var variance_EP = Variable.GammaFromShapeAndRate(1, 1);
var x_ep = GaussianPlusNoiseModel(variance_EP, n, true, noisePrecision);
var ie_EP = new InferenceEngine();
ie_EP.ShowWarnings = false;
ie_EP.ShowProgress = false;
var ev = Variable.Bernoulli(.5);
var modelBlock = Variable.If(ev);
var variance_MC = Variable.GammaFromShapeAndRate(1, 1);
var x_mc = GaussianPlusNoiseModel(variance_MC, n, true, noisePrecision);
modelBlock.CloseBlock();
var ie_MC = new InferenceEngine();
ie_MC.ShowWarnings = false;
ie_MC.ShowProgress = false;
var precision_VMP = Variable.GammaFromShapeAndRate(1, 1);
var x_vmp = GaussianPlusNoiseModel(precision_VMP, n, false, noisePrecision);
var ie_VMP = new InferenceEngine(new VariationalMessagePassing());
ie_VMP.ShowWarnings = false;
ie_VMP.ShowProgress = false;
Console.WriteLine("N EP MC VMP EMP");
for (int j = 0; j < 10; j++)
{
Rand.Restart(2);
//int N = (int)Math.Pow(10, j + 1);
int N = 10 * (j + 1);
var noiseP = Enumerable.Range(0, N).Select(i =>
{
if (i % 3 == 0)
{
return(.1);
}
if (i % 3 == 1)
{
return(1);
}
else
{
return(10);
}
}).ToArray();
var data = noiseP.Select(i => Gaussian.Sample(mean, 1.0 / variance) + Gaussian.Sample(0, i)).ToArray();
Nvar.ObservedValue = N;
noisePrecision.ObservedValue = noiseP;
x_ep.ObservedValue = data;
x_mc.ObservedValue = data;
x_vmp.ObservedValue = data;
double epMean = double.NaN;
try
{
epMean = ie_EP.Infer <Gamma>(variance_EP).GetMean();
}
catch
{
}
;
double vmpMean = ie_VMP.Infer <Gamma>(precision_VMP).GetMeanInverse();
double varianceSample = 2;
var ge = new GammaEstimator();
Converter <double, double> f = vari =>
{
variance_MC.ObservedValue = vari;
return(ie_MC.Infer <Bernoulli>(ev).LogOdds);
};
int burnin = 1000, thin = 5, numSamples = 1000;
var samples = new double[numSamples];
for (int i = 0; i < burnin; i++)
{
varianceSample = NonconjugateVMP2Tests.SliceSampleUnivariate(varianceSample, f, lower_bound: 0);
}
for (int i = 0; i < numSamples; i++)
{
for (int k = 0; k < thin; k++)
{
varianceSample = NonconjugateVMP2Tests.SliceSampleUnivariate(varianceSample, f, lower_bound: 0);
}
samples[i] = varianceSample;
ge.Add(varianceSample);
}
double mcMean = samples.Sum() / numSamples;
var empiricalMean = data.Sum() / data.Length;
var empVar = data.Sum(o => o * o) / data.Length - empiricalMean * empiricalMean;
Console.WriteLine(N + " " + epMean + " " + mcMean + " " + vmpMean + " " + empVar);
}
}
