public double GenerateSample()
{
// generate a single deviate of Qm/Qr
double[] delta = new double[K + 1];
for (int i = 0; i < K + 1; i++)
{
delta[i] = zeta * rnorm.NextDouble();
}
double[] v = new double[K + 1];
for (int i = 0; i < K + 1; i++)
{
v[i] = sigma * rnorm.NextDouble();
}
double[] w = new double[N - K - 1];
for (int i = 0; i < N - K - 1; i++)
{
w[i] = sigma * rnorm.NextDouble();
}
double num = 0;
for (int i = 0; i < K + 1; i++)
{
num += Math.Pow(v[i] + delta[i], 2);
}
double denom = 0;
for (int i = 0; i < K + 1; i++)
{
denom += Math.Pow(delta[i], 2);
}
for (int i = 0; i < N - K - 1; i++)
{
denom += Math.Pow(w[i], 2);
}
return(num / denom);
}
private double AnnealStep(double objectiveValue, ref double[] theta)
{
// the argument is the function value at the current position
if (ofd == null)
{
return(Double.NaN);
}
// generates a single step in the optimization
lastAccepted = false;
// generate new point in parameter space
double[] thetaNew = theta.Clone() as double[];
for (int i = 0; i < scale.Length; i++)
{
thetaNew[i] += scale[i] * zRand.NextDouble();
}
// compute function at new point
double newObjectiveValue = ofd(thetaNew);
// decide whether to accept new point
if (newObjectiveValue > objectiveValue)
{
lastAccepted = true;
accepted++;
}
else if (Temperature > 0)
{
double U = uRand.NextDouble();
if (Temperature * Math.Log(U) <= newObjectiveValue - objectiveValue)
{
lastAccepted = true;
accepted++;
}
else
{
lastAccepted = false;
}
}
else
{
lastAccepted = false;
}
if (lastAccepted)
{
theta = thetaNew;
return(newObjectiveValue);
}
return(objectiveValue);
}
public override MCMCProposal Proposal(double[] theta)
{
if (theta.Length != dim)
{
throw new ArgumentException("The length of the parameter vector is inconsistent with previous lengths.");
}
double[] newTheta = theta.Clone() as double[];
int i = duDist.Next();
newTheta[i] = theta[i] + sigma[i] * gDist.NextDouble();
return(new MCMCProposal()
{
Theta = newTheta, LogRatio = 0
});
}
//private void mcmcButton_Click(object sender, RoutedEventArgs e)
//{
// // initialize rng
// Troschuetz.Random.Generators.MT19937Generator mt = new Troschuetz.Random.Generators.MT19937Generator();
// Troschuetz.Random.Distributions.Continuous.NormalDistribution z = new Troschuetz.Random.Distributions.Continuous.NormalDistribution(mt, 0, 1);
// // make data
// double alpha = 0;
// double beta = 0.333;
// int n = 10;
// double tau = 0.1;
// double[] x = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
// double[] y = new double[n];
// for (int i = 0; i < n; i++)
// {
// y[i] = alpha + beta * x[i] + Math.Sqrt(tau) * z.NextDouble();
// }
// List<double> estimates = linearRegression(x, y);
// TestStatistics ts = new TestStatistics();
// ts.LoadData(x, y);
// MCMC mcmc = new MCMC();
// //SimpleSequentialGaussianProposal ssgp = new SimpleSequentialGaussianProposal(new double[] { 0.1, 0.1, 0.1 });
// //SimpleSimultaneousGaussianProposal sgp = new SimpleSimultaneousGaussianProposal(new double[] { 0.01, 0.01, 0.01 });
// SimpleSimultaneousGaussianProposal sgp = new SimpleSimultaneousGaussianProposal(new double[] { 0.01, 0.01, 0.01 });
// int burnin = 0;
// int total = 1000;
// mcmc.Initialize(ts.LogLikelihoodLR, ts.LogPriorLR, sgp.Proposal, 3, total, burnin);
// double tauInit = Math.Log(estimates[2]);
// double[] pars = new double[]{estimates[0],estimates[1],tauInit};
// mcmc.MCMCRun(pars);
//}
private void mcmcButton_Click(object sender, RoutedEventArgs e)
{
// initialize rng
Troschuetz.Random.Generators.MT19937Generator mt = new Troschuetz.Random.Generators.MT19937Generator();
Troschuetz.Random.Distributions.Continuous.NormalDistribution z = new Troschuetz.Random.Distributions.Continuous.NormalDistribution(mt, 0, 1);
// make data
double alpha = 0;
double beta = 0.333;
int n = 10;
double tau = 0.1;
double[] x = new double[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };
double[] y = new double[n];
for (int i = 0; i < n; i++)
{
y[i] = alpha + beta * x[i] + Math.Sqrt(tau) * z.NextDouble();
}
List <double> estimates = linearRegression(x, y);
TestStatistics ts = new TestStatistics();
ts.LoadData(x, y);
double tauInit = Math.Log(estimates[2]);
double[] pars = new double[] { estimates[0], estimates[1], tauInit };
int burnin = 50000;
int total = 50000;
// 1 parameter per auxiliary chain
int numSubModels = 3;
AuxiliaryChain[] submodel = new AuxiliaryChain[] { new AuxiliaryChain(), new AuxiliaryChain(), new AuxiliaryChain() };
SimpleSimultaneousGaussianProposal[] gp = new SimpleSimultaneousGaussianProposal[]
{ new SimpleSimultaneousGaussianProposal(new double[] { 0.01 }),
new SimpleSimultaneousGaussianProposal(new double[] { 0.01 }),
new SimpleSimultaneousGaussianProposal(new double[] { 0.01 }) };
ParallelHierarchicalSampler mother = new ParallelHierarchicalSampler(total, burnin, numSubModels, 3);
mother.Initialize(ts.LogLikelihoodLR, ts.LogPriorLR, pars);
for (int i = 0; i < numSubModels; i++)
{
mother.mcmc[i].Initialize(ts.LogLikelihoodLR, ts.LogPriorLR, gp[i].Proposal, new int[] { i }, mother.Mother);
}
////// all 3 parameters in the auxiliary chains
//int numSubModels = 3;
//AuxiliaryChain[] submodel = new AuxiliaryChain[] { new AuxiliaryChain(), new AuxiliaryChain(), new AuxiliaryChain() };
//SimpleSimultaneousGaussianProposal[] gp = new SimpleSimultaneousGaussianProposal[]
// { new SimpleSimultaneousGaussianProposal(new double[] { 0.01, 0.01, 0.01 }),
// new SimpleSimultaneousGaussianProposal(new double[] { 0.01, 0.01, 0.01 }),
// new SimpleSimultaneousGaussianProposal(new double[] { 0.01, 0.01, 0.01 }) };
//ParallelHierarchicalSampler mother = new ParallelHierarchicalSampler(total, burnin, numSubModels, 3);
//mother.Initialize(ts.LogLikelihoodLR, ts.LogPriorLR, pars);
//for (int i = 0; i < numSubModels; i++)
//{
// mother.mcmc[i].Initialize(ts.LogLikelihoodLR, ts.LogPriorLR, gp[i].Proposal, new int[] { 0, 1, 2 }, mother.state);
//}
//// single chain
//int numSubModels = 1;
//AuxiliaryChain[] submodel = new AuxiliaryChain[] { new AuxiliaryChain() };
//SimpleSimultaneousGaussianProposal[] gp = new SimpleSimultaneousGaussianProposal[] { new SimpleSimultaneousGaussianProposal(new double[] { 0.01 }) };
//ParallelHierarchicalSampler mother = new ParallelHierarchicalSampler(total, burnin, numSubModels, 3);
//mother.Initialize(ts.LogLikelihoodLR, ts.LogPriorLR, pars);
//mother.mcmc[0].Initialize(ts.LogLikelihoodLR, ts.LogPriorLR, gp[0].Proposal, new int[] { 0, 1, 2 }, mother.state);
mother.Run();
}