public void RandomSourceTest()
{
var hybrid = new HybridMC(new double[] { 0 }, x => _normal.DensityLn(x[0]), 10, 0.1);
Assert.IsNotNull(hybrid.RandomSource);
hybrid.RandomSource = new System.Random(0);
Assert.IsNotNull(hybrid.RandomSource);
}
public void RandomSourceTest()
{
var hybrid = new HybridMC(new double[] { 0 }, x => _normal.DensityLn(x[0]), 10, 0.1);
Assert.IsNotNull(hybrid.RandomSource);
hybrid.RandomSource = new System.Random(0);
Assert.IsNotNull(hybrid.RandomSource);
}
public void RandomSourceTest()
{
HybridMC Hybrid = new HybridMC(new double[1] {
0
}, x => normal.DensityLn(x[0]), 10, 0.1);
Assert.IsNotNull(Hybrid.RandomSource);
Hybrid.RandomSource = new System.Random();
Assert.IsNotNull(Hybrid.RandomSource);
}
public void SampleTest()
{
//Variances and Means
double[] Sdv = new double[2];
double[] Mean = new double[2];
for (int i = 0; i < 2; i++)
{
Sdv[i] = RandomVar();
Mean[i] = 5 * rnd.NextDouble();
}
//Cross Variance
double rho = 1.0 - RandomVar();
DensityLn <double[]> pdfLn = new DensityLn <double[]>(x => LogDen(x, Sdv, Mean, rho));
HybridMC Hybrid = new HybridMC(new double[2] {
0, 0
}, pdfLn, 10, 0.1, 1000);
Hybrid.BurnInterval = 0;
int SampleSize = 10000;
double[][] Sample = Hybrid.Sample(SampleSize);
double[][] NewSamples = ArrangeSamples(SampleSize, Sample);
double[] Convergence = new double[2];
double[] SampleMean = new double[2];
double[] SampleSdv = new double[2];
for (int i = 0; i < 2; i++)
{
Convergence[i] = 1 / Math.Sqrt(MCMCDiagnostics.EffectiveSize(Sample, x => x[i]));
DescriptiveStatistics Stats = new DescriptiveStatistics(NewSamples[i]);
SampleMean[i] = Stats.Mean;
SampleSdv[i] = Stats.StandardDeviation;
}
double SampleRho = Correlation.Pearson(NewSamples[0], NewSamples[1]);
for (int i = 0; i < 2; i++)
{
string index = i.ToString();
Assert.AreEqual(Mean[i], SampleMean[i], 10 * Convergence[i], index + "Mean");
Assert.AreEqual(SampleSdv[i] * SampleSdv[i], Sdv[i] * Sdv[i], 10 * Convergence[i], index + "Standard Deviation");
}
double ConvergenceRho = 1 / Math.Sqrt(MCMCDiagnostics.EffectiveSize(Sample, x => (x[0] - SampleMean[0]) * (x[1] - SampleMean[1])));
Assert.AreEqual(SampleRho * SampleSdv[0] * SampleSdv[1], rho * Sdv[0] * Sdv[1], 10 * ConvergenceRho, "Rho");
}
public void SampleTest(double[] sdv, double[] mean, double rho, int seed)
{
var pdfLn = new DensityLn <double[]>(x => LogDen(x, sdv, mean, rho));
var hybrid = new HybridMC(new double[2] {
0, 0
}, pdfLn, 10, 0.1, 1000, new double[] { 1, 1 }, new System.Random(seed))
{
BurnInterval = 0
};
const int sampleSize = 10000;
double[][] sample = hybrid.Sample(sampleSize);
double[][] newSamples = ArrangeSamples(sampleSize, sample);
var convergence = new double[2];
var sampleMean = new double[2];
var sampleSdv = new double[2];
for (int i = 0; i < 2; i++)
{
convergence[i] = 1 / Math.Sqrt(MCMCDiagnostics.EffectiveSize(sample, x => x[i]));
var stats = new DescriptiveStatistics(newSamples[i]);
sampleMean[i] = stats.Mean;
sampleSdv[i] = stats.StandardDeviation;
}
double sampleRho = Correlation.Pearson(newSamples[0], newSamples[1]);
for (int i = 0; i < 2; i++)
{
string index = i.ToString();
Assert.AreEqual(mean[i], sampleMean[i], 10 * convergence[i], index + "Mean");
Assert.AreEqual(sampleSdv[i] * sampleSdv[i], sdv[i] * sdv[i], 10 * convergence[i], index + "Standard Deviation");
}
double convergenceRho = 1 / Math.Sqrt(MCMCDiagnostics.EffectiveSize(sample, x => (x[0] - sampleMean[0]) * (x[1] - sampleMean[1])));
Assert.AreEqual(sampleRho * sampleSdv[0] * sampleSdv[1], rho * sdv[0] * sdv[1], 10 * convergenceRho, "Rho");
}
public void SampleTest(double[] sdv, double[] mean, double rho, int seed)
{
var pdfLn = new DensityLn<double[]>(x => LogDen(x, sdv, mean, rho));
var hybrid = new HybridMC(new double[] { 0, 0 }, pdfLn, 10, 0.1, 1000, new double[] { 1, 1 }, new System.Random(seed))
{
BurnInterval = 0
};
const int sampleSize = 10000;
double[][] sample = hybrid.Sample(sampleSize);
double[][] newSamples = ArrangeSamples(sampleSize, sample);
var convergence = new double[2];
var sampleMean = new double[2];
var sampleSdv = new double[2];
for (int i = 0; i < 2; i++)
{
// ReSharper disable once AccessToModifiedClosure
convergence[i] = 1/Math.Sqrt(MCMCDiagnostics.EffectiveSize(sample, x => x[i]));
var stats = new DescriptiveStatistics(newSamples[i]);
sampleMean[i] = stats.Mean;
sampleSdv[i] = stats.StandardDeviation;
}
double sampleRho = Correlation.Pearson(newSamples[0], newSamples[1]);
for (int i = 0; i < 2; i++)
{
string index = i.ToString(CultureInfo.InvariantCulture);
Assert.AreEqual(mean[i], sampleMean[i], 10*convergence[i], index + "Mean");
Assert.AreEqual(sampleSdv[i]*sampleSdv[i], sdv[i]*sdv[i], 10*convergence[i], index + "Standard Deviation");
}
double convergenceRho = 1/Math.Sqrt(MCMCDiagnostics.EffectiveSize(sample, x => (x[0] - sampleMean[0])*(x[1] - sampleMean[1])));
Assert.AreEqual(sampleRho*sampleSdv[0]*sampleSdv[1], rho*sdv[0]*sdv[1], 10*convergenceRho, "Rho");
}
public void SampleTest()
{
//Variances and Means
double[] Sdv = new double[2];
double[] Mean = new double[2];
for (int i = 0; i < 2; i++)
{
Sdv[i] = RandomVar();
Mean[i] = 5 * rnd.NextDouble();
}
//Cross Variance
double rho = 1.0 - RandomVar();
DensityLn<double[]> pdfLn = new DensityLn<double[]>(x => LogDen(x, Sdv, Mean, rho));
HybridMC Hybrid = new HybridMC(new double[2] { 0, 0 }, pdfLn, 10, 0.1, 1000);
Hybrid.BurnInterval = 0;
int SampleSize = 10000;
double[][] Sample = Hybrid.Sample(SampleSize);
double[][] NewSamples = ArrangeSamples(SampleSize, Sample);
double[] Convergence = new double[2];
double[] SampleMean = new double[2];
double[] SampleSdv = new double[2];
for (int i = 0; i < 2; i++)
{
Convergence[i] = 1 / Math.Sqrt(MCMCDiagnostics.EffectiveSize(Sample,x=>x[i]));
DescriptiveStatistics Stats = new DescriptiveStatistics(NewSamples[i]);
SampleMean[i] = Stats.Mean;
SampleSdv[i] = Stats.StandardDeviation;
}
double SampleRho = Correlation.Pearson(NewSamples[0], NewSamples[1]);
for (int i = 0; i < 2; i++)
{
string index = i.ToString();
Assert.AreEqual(Mean[i], SampleMean[i], 10 * Convergence[i], index + "Mean");
Assert.AreEqual(SampleSdv[i] * SampleSdv[i], Sdv[i] * Sdv[i], 10 * Convergence[i], index + "Standard Deviation");
}
double ConvergenceRho=1/Math.Sqrt(MCMCDiagnostics.EffectiveSize(Sample,x=>(x[0]-SampleMean[0])*(x[1]-SampleMean[1])));
Assert.AreEqual(SampleRho*SampleSdv[0]*SampleSdv[1], rho*Sdv[0]*Sdv[1], 10 * ConvergenceRho, "Rho");
}
public void RandomSourceTest()
{
HybridMC Hybrid = new HybridMC(new double[1] { 0 }, x => normal.DensityLn(x[0]), 10, 0.1);
Assert.IsNotNull(Hybrid.RandomSource);
Hybrid.RandomSource = new System.Random();
Assert.IsNotNull(Hybrid.RandomSource);
}
