public void GaussianOpMean()
{
Gaussian result = new Gaussian();
Gaussian X0 = Gaussian.FromMeanAndVariance(7, 1.0 / 3);
Gaussian Mean0 = Gaussian.FromMeanAndVariance(3, 0.5);
Gamma Precision0 = Gamma.FromShapeAndScale(3, 3);
// Unknown precision
Gamma Precision = Gamma.FromShapeAndScale(3, 3);
Gaussian X = X0;
Gaussian Mean = Mean0;
// in matlab: test_t_msg
result = GaussianOp.MeanAverageConditional_slow(X, Mean, Precision);
Console.WriteLine(result);
Assert.True(GaussianOp.MeanAverageConditional_slow(X, Mean, Precision).MaxDiff(new Gaussian(-9.9121, -4.5998)) < 1e-0);
X = Gaussian.FromMeanAndVariance(1, 2);
Mean = Gaussian.PointMass(0);
Precision = Gamma.FromShapeAndRate(3, 1);
Gaussian xPostExpected = Gaussian.FromMeanAndVariance(0.178378819440295, 0.365796599498963);
Console.WriteLine(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision) * X);
Assert.True(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision).MaxDiff(xPostExpected / X) < 5e-7);
Console.WriteLine(GaussianOp_Slow.SampleAverageConditional(X, Mean, Precision) * X);
Assert.True(GaussianOp_Slow.SampleAverageConditional(X, Mean, Precision).MaxDiff(xPostExpected / X) < 5e-7);
}
private void GaussianOpX2(Gaussian mean, Gamma precision)
{
Gaussian sample, result, result2;
sample = Gaussian.PointMass(2);
result = GaussianOp.SampleAverageConditional_slow(sample, mean, precision);
result2 = GaussianOp_Slow.SampleAverageConditional(sample, mean, precision);
Console.WriteLine("{0}: {1} {2}", sample, result, result2);
Assert.True(result.MaxDiff(result2) < 1e-8);
double prevDiff = double.PositiveInfinity;
for (int i = 8; i < 30; i++)
{
double v = System.Math.Pow(0.1, i);
sample = Gaussian.FromMeanAndVariance(2, v);
result2 = GaussianOp_Slow.SampleAverageConditional(sample, mean, precision);
double diff = result.MaxDiff(result2);
Console.WriteLine("{0}: {1} diff={2}", sample, result2, diff.ToString("g4"));
Assert.True(diff <= prevDiff || diff < 1e-6);
result2 = GaussianOp.SampleAverageConditional_slow(sample, mean, precision);
diff = result.MaxDiff(result2);
Console.WriteLine("{0}: {1} diff={2}", sample, result2, diff.ToString("g4"));
Assert.True(diff <= prevDiff || diff < 1e-6);
prevDiff = diff;
}
}
public static Gaussian FindxB(Gaussian xB, Gaussian meanPrior, Gamma precPrior, Gaussian xF)
{
Gaussian xB3 = IsPositiveOp.XAverageConditional(true, xF);
Func <Vector, double> func = delegate(Vector x2)
{
Gaussian xB2 = Gaussian.FromMeanAndPrecision(x2[0], System.Math.Exp(x2[1]));
//Gaussian xF2 = GaussianOp.SampleAverageConditional_slow(xB2, meanPrior, precPrior);
Gaussian xF2 = GaussianOp_Slow.SampleAverageConditional(xB2, meanPrior, precPrior);
//Assert.True(xF2.MaxDiff(xF3) < 1e-10);
//return Math.Pow((xF*xB2).GetMean() - (xF2*xB2).GetMean(), 2) + Math.Pow((xF*xB2).GetVariance() - (xF2*xB2).GetVariance(), 2);
//return KlDiv(xF2*xB2, xF*xB2) + KlDiv(xF*xB3, xF*xB2);
//return KlDiv(xF2*xB2, xF*xB2) + Math.Pow((xF*xB3).GetMean() - (xF*xB2).GetMean(),2);
return(MeanError(xF2 * xB2, xF * xB2) + KlDiv(xF * xB3, xF * xB2));
//return xF.MaxDiff(xF2);
//Gaussian q = new Gaussian(0, 0.1);
//return Math.Pow((xF*q).GetMean() - (xF2*q).GetMean(), 2) + Math.Pow((xF*q).GetVariance() - (xF2*q).GetVariance(), 2);
};
double m = xB.GetMean();
double p = xB.Precision;
Vector x = Vector.FromArray(m, System.Math.Log(p));
Minimize2(func, x);
return(Gaussian.FromMeanAndPrecision(x[0], System.Math.Exp(x[1])));
}
public static Gaussian FindxF2(Gaussian meanPrior, Gamma precPrior, Gaussian xF)
{
Func <Vector, double> func = delegate(Vector x2)
{
Gaussian xFt = Gaussian.FromMeanAndPrecision(x2[0], System.Math.Exp(x2[1]));
Gaussian xB = IsPositiveOp.XAverageConditional(true, xFt);
Gaussian xF2 = GaussianOp_Slow.SampleAverageConditional(xB, meanPrior, precPrior);
return(xFt.MaxDiff(xF2));
};
double m = xF.GetMean();
double p = xF.Precision;
Vector x = Vector.FromArray(m, System.Math.Log(p));
Minimize2(func, x);
return(Gaussian.FromMeanAndPrecision(x[0], System.Math.Exp(x[1])));
}
public static Gaussian FindxF(Gaussian xB, Gaussian meanPrior, Gamma precPrior, Gaussian xF)
{
Gaussian xF3 = GaussianOp_Slow.SampleAverageConditional(xB, meanPrior, precPrior);
Func <Vector, double> func = delegate(Vector x2)
{
Gaussian xF2 = Gaussian.FromMeanAndPrecision(x2[0], System.Math.Exp(x2[1]));
Gaussian xB2 = IsPositiveOp.XAverageConditional(true, xF2);
//return (xF2*xB2).MaxDiff(xF2*xB) + (xF3*xB).MaxDiff(xF2*xB);
//return KlDiv(xF2*xB2, xF2*xB) + KlDiv(xF3*xB, xF2*xB);
//return KlDiv(xF3*xB, xF2*xB) + Math.Pow((xF2*xB2).GetMean() - (xF2*xB).GetMean(),2);
return(KlDiv(xF2 * xB2, xF2 * xB) + MeanError(xF3 * xB, xF2 * xB));
};
double m = xF.GetMean();
double p = xF.Precision;
Vector x = Vector.FromArray(m, System.Math.Log(p));
Minimize2(func, x);
//MinimizePowell(func, x);
return(Gaussian.FromMeanAndPrecision(x[0], System.Math.Exp(x[1])));
}
public static Gaussian FindxF0(Gaussian xB, Gaussian meanPrior, Gamma precPrior, Gaussian xF)
{
Gaussian xF3 = GaussianOp_Slow.SampleAverageConditional(xB, meanPrior, precPrior);
Func <double, double> func = delegate(double tau2)
{
Gaussian xF2 = Gaussian.FromNatural(tau2, 0);
if (tau2 >= 0)
{
return(double.PositiveInfinity);
}
Gaussian xB2 = IsPositiveOp.XAverageConditional(true, xF2);
//return (xF2*xB2).MaxDiff(xF2*xB) + (xF3*xB).MaxDiff(xF2*xB);
//return KlDiv(xF2*xB2, xF2*xB) + KlDiv(xF3*xB, xF2*xB);
//return KlDiv(xF3*xB, xF2*xB) + Math.Pow((xF2*xB2).GetMean() - (xF2*xB).GetMean(), 2);
return(KlDiv(xF2 * xB2, xF2 * xB) + MeanError(xF3 * xB, xF2 * xB));
};
double tau = xF.MeanTimesPrecision;
double fmin;
tau = Minimize(func, tau, out fmin);
//MinimizePowell(func, x);
return(Gaussian.FromNatural(tau, 0));
}
public void GaussianOpX()
{
Gaussian uniform = Gaussian.Uniform();
Gaussian X0 = Gaussian.FromMeanAndVariance(3, 0.5);
Gaussian Mean0 = Gaussian.FromMeanAndVariance(7, 1.0 / 3);
double Precision0 = 3;
Gaussian X, Mean;
Gamma Precision, to_precision;
Gaussian xActual, xExpected;
bool testImproper = false;
if (testImproper)
{
// Test the case where precisionIsBetween = false
X = Gaussian.FromNatural(1, 2);
Mean = Gaussian.FromNatural(3, -1);
Precision = Gamma.FromShapeAndRate(4, 5);
to_precision = Gamma.FromShapeAndRate(6, 7);
xActual = GaussianOp.SampleAverageConditional(X, Mean, Precision, to_precision);
}
X = Gaussian.FromNatural(-2.7793306963303595, 0.050822473645365768);
Mean = Gaussian.FromNatural(-5.9447032851878134E-09, 3.2975231004586637E-204);
Precision = Gamma.FromShapeAndRate(318.50907574398883, 9.6226982361933746E+205);
to_precision = Gamma.PointMass(0);
xActual = GaussianOp.SampleAverageConditional(X, Mean, Precision, to_precision);
X = Gaussian.FromNatural(0.1559599323109816, 8.5162535450918462);
Mean = Gaussian.PointMass(0.57957597647840942);
Precision = Gamma.FromShapeAndRate(7.8308812008325587E+30, 8.2854255911709925E+30);
to_precision = Gamma.FromShapeAndRate(1.4709139487775529, 0.14968339171493822);
xActual = GaussianOp.SampleAverageConditional(X, Mean, Precision, to_precision);
X = Gaussian.FromNatural(0.15595993233964134, 8.5162535466550349);
Mean = Gaussian.PointMass(0.57957597647840942);
Precision = Gamma.FromShapeAndRate(3.9206259406339067E+20, 4.1481991194547565E+20);
to_precision = Gamma.FromShapeAndRate(1.4709139487806249, 0.14968339171413536);
xActual = GaussianOp.SampleAverageConditional(X, Mean, Precision, to_precision);
X = Gaussian.FromNatural(0.15595993261634511, 8.5162535617468418);
Mean = Gaussian.PointMass(0.57957597647840942);
Precision = Gamma.FromShapeAndRate(1.825759224425317E+19, 1.9317356258150703E+19);
to_precision = Gamma.FromShapeAndRate(1.4709139487887679, 0.14968339176002607);
xActual = GaussianOp.SampleAverageConditional(X, Mean, Precision, to_precision);
X = Gaussian.FromNatural(0.16501264432785923, 9.01);
Mean = Gaussian.PointMass(0.57957597647840942);
Precision = Gamma.FromShapeAndRate(1.6965139612477539E+21, 1.6965139612889427E+21);
to_precision = Gamma.FromShapeAndRate(1.4695136363119978, 0.14707291154227081);
xActual = GaussianOp.SampleAverageConditional(X, Mean, Precision, to_precision);
// initialized in a bad place, gets stuck in a flat region
X = Gaussian.FromNatural(3.9112579392580757, 11.631097473681082);
Mean = Gaussian.FromNatural(10.449696977834144, 5.5617978202886995);
Precision = Gamma.FromShapeAndRate(1.0112702817305146, 0.026480506235719053);
to_precision = Gamma.FromShapeAndRate(1, 0.029622790537514355);
xActual = GaussianOp.SampleAverageConditional(X, Mean, Precision, to_precision);
X = Gaussian.FromNatural(57788.170908674481, 50207.150004827061);
Mean = Gaussian.PointMass(0);
Precision = Gamma.FromShapeAndRate(19764.051194189466, 0.97190264412377791);
xActual = GaussianOp.SampleAverageConditional_slow(X, Mean, Precision);
// integration bounds should be [-36,4]
X = Gaussian.FromNatural(1.696828485456396, 0.71980672726406147);
Mean = Gaussian.PointMass(-1);
Precision = new Gamma(1, 1);
xActual = GaussianOp.SampleAverageConditional_slow(X, Mean, Precision);
xExpected = GaussianOp_Slow.SampleAverageConditional(X, Mean, Precision);
Assert.True(xExpected.MaxDiff(xActual) < 1e-4);
X = new Gaussian(-1.565, 0.8466);
Mean = new Gaussian(0.0682, 0.3629);
Precision = new Gamma(103.2, 0.009786);
xActual = GaussianOp.SampleAverageConditional_slow(X, Mean, Precision);
xExpected = GaussianOp_Slow.SampleAverageConditional(X, Mean, Precision);
Assert.True(xExpected.MaxDiff(xActual) < 1e-4);
// Fixed precision
X = X0;
Mean = uniform;
Assert.True(GaussianOp.SampleAverageConditional(Mean, Precision0).MaxDiff(uniform) < 1e-10);
Mean = Mean0;
Assert.True(GaussianOp.SampleAverageConditional(Mean, Precision0).MaxDiff(new Gaussian(Mean.GetMean(), Mean.GetVariance() + 1 / Precision0)) < 1e-10);
Mean = Gaussian.PointMass(Mean0.GetMean());
Assert.True(GaussianOp.SampleAverageConditional(Mean, Precision0).MaxDiff(new Gaussian(Mean.GetMean(), 1 / Precision0)) < 1e-10);
// Uniform precision
// the answer should always be uniform
Precision = Gamma.Uniform();
Assert.True(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision).MaxDiff(uniform) < 1e-10);
// Unknown precision
Precision = Gamma.FromShapeAndScale(3, 3);
// Known X
X = Gaussian.PointMass(X0.GetMean());
Mean = uniform;
Assert.True(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision).MaxDiff(uniform) < 1e-10);
// Unknown X
X = X0;
Mean = uniform;
//Console.WriteLine(XAverageConditional2(result));
Assert.True(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision).MaxDiff(uniform) < 1e-10);
X = X0;
Mean = Mean0;
// converge the precision message. (only matters if KeepLastMessage is set).
//for (int i = 0; i < 10; i++) GaussianOp.PrecisionAverageConditional(X, Mean, Precision, precisionMessage);
// in matlab: test_t_msg
if (GaussianOp.ForceProper)
{
Assert.True(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision).MaxDiff(Gaussian.FromNatural(3.1495, 0)) < 1e-4);
}
else
{
Assert.True(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision).MaxDiff(new Gaussian(-9.9121, -4.5998)) < 1e-4);
}
X = X0;
Mean = Gaussian.PointMass(Mean0.GetMean());
// converge the precision message. (only matters if KeepLastMessage is set).
//for (int i = 0; i < 10; i++) GaussianOp.PrecisionAverageConditional(X, Mean, Precision, precisionMessage);
// in matlab: test_t_msg
if (GaussianOp.ForceProper)
{
Assert.True(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision).MaxDiff(Gaussian.FromNatural(2.443, 0)) < 1e-4);
}
else
{
Assert.True(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision).MaxDiff(new Gaussian(0.81394, -1.3948)) < 1e-4);
}
// Uniform X
X = uniform;
Mean = Mean0;
// converge the precision message. (only matters if KeepLastMessage is set).
//for (int i = 0; i < 10; i++) GaussianOp.PrecisionAverageConditional(X, Mean, Precision, precisionMessage);
Assert.True(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision).MaxDiff(new Gaussian(7, 0.5)) < 1e-10);
X = uniform;
Mean = Gaussian.PointMass(Mean0.GetMean());
// converge the precision message. (only matters if KeepLastMessage is set).
//for (int i = 0; i < 10; i++) GaussianOp.PrecisionAverageConditional(X, Mean, Precision, precisionMessage);
Assert.True(GaussianOp.SampleAverageConditional_slow(X, Mean, Precision).MaxDiff(new Gaussian(7, 1.0 / 6)) < 1e-10);
}
internal void StudentIsPositiveTest2()
{
GaussianOp.ForceProper = false;
double shape = 1;
double mean = -1;
Gamma precPrior = Gamma.FromShapeAndRate(shape, shape);
Gaussian meanPrior = Gaussian.PointMass(mean);
double evExpected;
Gaussian xExpected = StudentIsPositiveExact(mean, precPrior, out evExpected);
Gaussian xF2 = Gaussian.FromMeanAndVariance(-1, 1);
// the energy has a stationary point here (min in both dimensions), even though xF0 is improper
Gaussian xB0 = new Gaussian(2, 1);
xF2 = Gaussian.FromMeanAndVariance(-4.552, 6.484);
//xB0 = new Gaussian(1.832, 0.9502);
//xB0 = new Gaussian(1.792, 1.558);
//xB0 = new Gaussian(1.71, 1.558);
//xB0 = new Gaussian(1.792, 1.5);
Gaussian xF0 = GaussianOp_Slow.SampleAverageConditional(xB0, meanPrior, precPrior);
//Console.WriteLine("xB0 = {0} xF0 = {1}", xB0, xF0);
//Console.WriteLine(xF0*xB0);
//Console.WriteLine(xF2*xB0);
xF2 = new Gaussian(0.8651, 1.173);
xB0 = new Gaussian(-4, 2);
xB0 = new Gaussian(7, 7);
if (false)
{
xF2 = new Gaussian(mean, 1);
double[] xs = EpTests.linspace(0, 100, 1000);
double[] logTrue = Util.ArrayInit(xs.Length, i => GaussianOp.LogAverageFactor(xs[i], mean, precPrior));
Normalize(logTrue);
xF2 = FindxF4(xs, logTrue, xF2);
xF2 = Gaussian.FromNatural(-0.85, 0);
xB0 = IsPositiveOp.XAverageConditional(true, xF2);
Console.WriteLine("xF = {0} xB = {1}", xF2, xB0);
Console.WriteLine("x = {0} should be {1}", xF2 * xB0, xExpected);
Console.WriteLine("proj[T*xB] = {0}", GaussianOp_Slow.SampleAverageConditional(xB0, meanPrior, precPrior) * xB0);
double ev = System.Math.Exp(IsPositiveOp.LogAverageFactor(true, xF2) + GaussianOp_Slow.LogAverageFactor(xB0, meanPrior, precPrior) - xF2.GetLogAverageOf(xB0));
Console.WriteLine("evidence = {0} should be {1}", ev, evExpected);
return;
}
if (false)
{
xF2 = new Gaussian(mean, 1);
xF2 = FindxF3(xExpected, evExpected, meanPrior, precPrior, xF2);
xB0 = IsPositiveOp.XAverageConditional(true, xF2);
Console.WriteLine("xF = {0} xB = {1}", xF2, xB0);
Console.WriteLine("x = {0} should be {1}", xF2 * xB0, xExpected);
//double ev = Math.Exp(IsPositiveOp.LogAverageFactor(true, xF2) + GaussianOp.LogAverageFactor_slow(xB0, meanPrior, precPrior) - xF2.GetLogAverageOf(xB0));
//Console.WriteLine("evidence = {0} should be {1}", ev, evExpected);
return;
}
if (false)
{
xF2 = new Gaussian(-2, 10);
xF2 = FindxF2(meanPrior, precPrior, xF2);
xB0 = IsPositiveOp.XAverageConditional(true, xF2);
xF0 = GaussianOp_Slow.SampleAverageConditional(xB0, meanPrior, precPrior);
Console.WriteLine("xB = {0}", xB0);
Console.WriteLine("xF = {0} should be {1}", xF0, xF2);
return;
}
if (false)
{
xF2 = new Gaussian(-3998, 4000);
xF2 = new Gaussian(0.8651, 1.173);
xB0 = new Gaussian(-4, 2);
xB0 = new Gaussian(2000, 1e-5);
xB0 = FindxB(xB0, meanPrior, precPrior, xF2);
xF0 = GaussianOp_Slow.SampleAverageConditional(xB0, meanPrior, precPrior);
Console.WriteLine("xB = {0}", xB0);
Console.WriteLine("xF = {0} should be {1}", xF0, xF2);
return;
}
if (false)
{
//xF2 = new Gaussian(-7, 10);
//xF2 = new Gaussian(-50, 52);
xB0 = new Gaussian(-1.966, 5.506e-08);
//xF2 = new Gaussian(-3998, 4000);
xF0 = FindxF(xB0, meanPrior, precPrior, xF2);
Gaussian xB2 = IsPositiveOp.XAverageConditional(true, xF0);
Console.WriteLine("xF = {0}", xF0);
Console.WriteLine("xB = {0} should be {1}", xB2, xB0);
return;
}
if (true)
{
xF0 = new Gaussian(-3.397e+08, 5.64e+08);
xF0 = new Gaussian(-2.373e+04, 2.8e+04);
xB0 = new Gaussian(2.359, 1.392);
xF0 = Gaussian.FromNatural(-0.84, 0);
//xF0 = Gaussian.FromNatural(-0.7, 0);
for (int iter = 0; iter < 10; iter++)
{
xB0 = FindxB(xB0, meanPrior, precPrior, xF0);
Gaussian xFt = GaussianOp_Slow.SampleAverageConditional(xB0, meanPrior, precPrior);
Console.WriteLine("xB = {0}", xB0);
Console.WriteLine("xF = {0} should be {1}", xFt, xF0);
xF0 = FindxF0(xB0, meanPrior, precPrior, xF0);
Gaussian xBt = IsPositiveOp.XAverageConditional(true, xF0);
Console.WriteLine("xF = {0}", xF0);
Console.WriteLine("xB = {0} should be {1}", xBt, xB0);
}
Console.WriteLine("x = {0} should be {1}", xF0 * xB0, xExpected);
double ev = System.Math.Exp(IsPositiveOp.LogAverageFactor(true, xF0) + GaussianOp_Slow.LogAverageFactor(xB0, meanPrior, precPrior) - xF0.GetLogAverageOf(xB0));
Console.WriteLine("evidence = {0} should be {1}", ev, evExpected);
return;
}
//var precs = EpTests.linspace(1e-6, 1e-5, 200);
var precs = EpTests.linspace(xB0.Precision / 11, xB0.Precision, 100);
//var precs = EpTests.linspace(xF0.Precision/20, xF0.Precision/3, 100);
precs = EpTests.linspace(1e-9, 1e-5, 100);
//precs = new double[] { xB0.Precision };
var ms = EpTests.linspace(xB0.GetMean() - 1, xB0.GetMean() + 1, 100);
//var ms = EpTests.linspace(xF0.GetMean()-1, xF0.GetMean()+1, 100);
//precs = EpTests.linspace(1.0/10, 1.0/8, 200);
ms = EpTests.linspace(2000, 4000, 100);
//ms = new double[] { xB0.GetMean() };
Matrix result = new Matrix(precs.Length, ms.Length);
Matrix result2 = new Matrix(precs.Length, ms.Length);
//ms = new double[] { 0.7 };
for (int j = 0; j < ms.Length; j++)
{
double maxZ = double.NegativeInfinity;
double minZ = double.PositiveInfinity;
Gaussian maxxF = Gaussian.Uniform();
Gaussian minxF = Gaussian.Uniform();
Gaussian maxxB = Gaussian.Uniform();
Gaussian minxB = Gaussian.Uniform();
Vector v = Vector.Zero(3);
for (int i = 0; i < precs.Length; i++)
{
Gaussian xF = Gaussian.FromMeanAndPrecision(ms[j], precs[i]);
xF = xF2;
Gaussian xB = IsPositiveOp.XAverageConditional(true, xF);
xB = Gaussian.FromMeanAndPrecision(ms[j], precs[i]);
//xB = xB0;
v[0] = IsPositiveOp.LogAverageFactor(true, xF);
v[1] = GaussianOp.LogAverageFactor_slow(xB, meanPrior, precPrior);
//v[1] = GaussianOp_Slow.LogAverageFactor(xB, meanPrior, precPrior);
v[2] = -xF.GetLogAverageOf(xB);
double logZ = v.Sum();
double Z = logZ;
if (Z > maxZ)
{
maxZ = Z;
maxxF = xF;
maxxB = xB;
}
if (Z < minZ)
{
minZ = Z;
minxF = xF;
minxB = xB;
}
result[i, j] = Z;
result2[i, j] = IsPositiveOp.LogAverageFactor(true, xF) + xF0.GetLogAverageOf(xB) - xF.GetLogAverageOf(xB);
//Gaussian xF3 = GaussianOp.SampleAverageConditional_slower(xB, meanPrior, precPrior);
//result[i, j] = Math.Pow(xF3.Precision - xF.Precision, 2);
//result2[i, j] = Math.Pow((xF2*xB).Precision - (xF*xB).Precision, 2);
//result2[i, j] = -xF.GetLogAverageOf(xB);
//Gaussian xF2 = GaussianOp.SampleAverageConditional_slow(xB, Gaussian.PointMass(0), precPrior);
Gaussian xMarginal = xF * xB;
//Console.WriteLine("xF = {0} Z = {1} x = {2}", xF, Z.ToString("g4"), xMarginal);
}
double delta = v[1] - v[2];
//Console.WriteLine("xF = {0} xB = {1} maxZ = {2} x = {3}", maxxF, maxxB, maxZ.ToString("g4"), maxxF*maxxB);
//Console.WriteLine("xF = {0} maxZ = {1} delta = {2}", maxxF, maxZ.ToString("g4"), delta.ToString("g4"));
Console.WriteLine("xF = {0} xB = {1} minZ = {2} x = {3}", minxF, minxB, minZ.ToString("g4"), minxF * minxB);
}
//TODO: change path for cross platform using
using (var writer = new MatlabWriter(@"..\..\..\Tests\student.mat"))
{
writer.Write("z", result);
writer.Write("z2", result2);
writer.Write("precs", precs);
writer.Write("ms", ms);
}
}
