public double Sample()
{
if (IsPointMass)
{
return(Point);
}
else if (!IsProper())
{
throw new ImproperDistributionException(this);
}
else
{
return(Rand.Beta(TrueCount, FalseCount));
}
}
public static double Sample(double trueCount, double falseCount)
{
return(Rand.Beta(trueCount, falseCount));
}
public static double BetaFromMeanAndTotalCount(double mean, double totalCount)
{
return(Rand.Beta(mean * totalCount, (1 - mean) * totalCount));
}
public static double Sample(double trueCount, double falseCount, IPolyrand random = null)
{
return(Rand.Beta(trueCount, falseCount, random));
}
internal void BetaRegression()
{
int P = 8;
double[] b = new double[P];
for (int p = 0; p < P; p++)
{
b[p] = Rand.Beta(1, 1);
}
int N = 100;
double[][] X = new double[N][];
//Gaussian[][] softX = new Gaussian[N][];
double[] Y = new double[N];
for (int n = 0; n < N; n++)
{
X[n] = new double[P];
//softX[n] = new Gaussian[P];
Y[n] = 0;
for (int p = 0; p < P; p++)
{
X[n][p] = Rand.Normal();
//softX[n][p] = new Gaussian(X[n][p], 1e-4);
Y[n] += X[n][p] * b[p];
}
}
Variable <bool> evidence = Variable.Bernoulli(0.5).Named("evidence");
IfBlock block = Variable.If(evidence);
Range dim = new Range(P).Named("P");
Range item = new Range(N).Named("N");
VariableArray <double> w = Variable.Array <double>(dim).Named("w");
w[dim] = Variable.Beta(1, 1).ForEach(dim);
var x = Variable.Array(Variable.Array <double>(dim), item).Named("x");
var softXvar = Variable.Array(Variable.Array <double>(dim), item).Named("softx");
softXvar.ObservedValue = X;
x[item][dim] = Variable.GaussianFromMeanAndPrecision(softXvar[item][dim], 1e4);
var wx = Variable.Array(Variable.Array <double>(dim), item).Named("wx");
wx[item][dim] = x[item][dim] * w[dim];
var sum = Variable.Array <double>(item).Named("sum");
sum[item] = Variable.Sum(wx[item]);
var prec = Variable.GammaFromShapeAndRate(.1, .1).Named("Noise");
var y = Variable.Array <double>(item).Named("y");
y[item] = Variable.GaussianFromMeanAndPrecision(sum[item], prec);
block.CloseBlock();
y.ObservedValue = Y;
InferenceEngine engine = new InferenceEngine(new VariationalMessagePassing());
var ca = engine.GetCompiledInferenceAlgorithm(evidence, w);
ca.Reset();
double oldLogEvidence = double.NegativeInfinity;
for (int i = 0; i < 1000; i++)
{
ca.Update(1);
double logEvidence1 = ca.Marginal <Bernoulli>(evidence.NameInGeneratedCode).LogOdds;
Console.WriteLine(logEvidence1);
if (i > 20 && System.Math.Abs(logEvidence1 - oldLogEvidence) < 0.01)
{
break;
}
oldLogEvidence = logEvidence1;
}
DistributionArray <Beta> wInferred = ca.Marginal <DistributionArray <Beta> >(w.NameInGeneratedCode);
for (int p = 0; p < P; p++)
{
Console.WriteLine("w[{0}] = {1} +/- {2} should be {3}",
p, wInferred[p].GetMean(), System.Math.Sqrt(wInferred[p].GetVariance()), b[p]);
}
}
