public void BernoulliFromBetaOpTest2()
{
Bernoulli sampleDist = new Bernoulli(2.0 / 3);
using (TestUtils.TemporarilyAllowBetaImproperSums)
{
for (int i = 10; i <= 10; i++)
{
double s = System.Math.Exp(i);
Beta probTrueDist = new Beta(s, 2 * s);
Beta expected = BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist, probTrueDist);
if (i == 10)
{
probTrueDist.Point = probTrueDist.GetMean();
}
Beta actual = BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist, probTrueDist);
Console.WriteLine("{0} {1}", probTrueDist, actual);
Assert.True(expected.MaxDiff(actual) < 1e-4);
}
}
for (int i = 10; i <= 10; i++)
{
Beta probTrueDist = new Beta(System.Math.Exp(i), 1);
Beta expected = BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist, probTrueDist);
if (i == 10)
{
probTrueDist.Point = 1;
}
Beta actual = BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist, probTrueDist);
Console.WriteLine("{0} {1}", probTrueDist, actual);
Assert.True(expected.MaxDiff(actual) < 1e-4);
}
}
public void DiscreteFromDirichletOpTest()
{
Dirichlet probs4 = new Dirichlet(1.0, 2, 3, 4);
Discrete sample4 = new Discrete(0.4, 0.6, 0, 0);
Dirichlet result4 = DiscreteFromDirichletOp.ProbsAverageConditional(sample4, probs4, Dirichlet.Uniform(4));
Dirichlet probs3 = new Dirichlet(1.0, 2, 7);
Discrete sample3 = new Discrete(0.4, 0.6, 0);
Dirichlet result3 = DiscreteFromDirichletOp.ProbsAverageConditional(sample3, probs3, Dirichlet.Uniform(3));
for (int i = 0; i < 3; i++)
{
Assert.True(MMath.AbsDiff(result4.PseudoCount[i], result3.PseudoCount[i], 1e-6) < 1e-10);
}
Dirichlet probs2 = new Dirichlet(1.0, 2);
Discrete sample2 = new Discrete(0.4, 0.6);
Dirichlet result2 = DiscreteFromDirichletOp.ProbsAverageConditional(sample2, probs2, Dirichlet.Uniform(2));
Beta beta = new Beta(1.0, 2);
Bernoulli bernoulli = new Bernoulli(0.4);
Beta result1 = BernoulliFromBetaOp.ProbTrueAverageConditional(bernoulli, beta);
Assert.Equal(result2.PseudoCount[0], result1.TrueCount, 1e-10);
Assert.Equal(result2.PseudoCount[1], result1.FalseCount, 1e-10);
// test handling of small alphas
Discrete sample = DiscreteFromDirichletOp.SampleAverageLogarithm(Dirichlet.Symmetric(2, 1e-8), Discrete.Uniform(2));
Assert.True(sample.IsUniform());
}
public DistributionArray <Bernoulli> SimpleAverageModel(double dummy, out double noiseEstimate)
{
noiseEstimate = double.NaN;
int K = xtrain[0].Count;
var probTrue = Variable.Beta(1, 1);
Range n = new Range(ytrain.Length);
var data = Variable.Array <bool>(n);
data[n] = Variable.Bernoulli(probTrue).ForEach(n);
data.ObservedValue = ytrain;
InferenceEngine engine = new InferenceEngine();
var probPost = engine.Infer <Beta>(probTrue);
var array = Enumerable.Range(0, xtest.Length).Select(i => BernoulliFromBetaOp.SampleAverageConditional(probPost)).ToArray();
return((DistributionArray <Bernoulli>) Distribution <bool> .Array(array));
}
/// <summary>Computations that depend on the observed value of vbool1</summary>
private void Changed_vbool1()
{
if (this.Changed_vbool1_isDone)
{
return;
}
this.vbool1_marginal = Bernoulli.Uniform();
this.vbool1_marginal = Distribution.SetPoint <Bernoulli, bool>(this.vbool1_marginal, this.Vbool1);
Beta vBeta0 = Beta.Uniform();
this.vdouble14_marginal_F = Beta.Uniform();
Beta vdouble14_use_B = default(Beta);
// Message to 'vdouble14_use' from Bernoulli factor
vdouble14_use_B = BernoulliFromBetaOp.ProbTrueAverageConditional(this.Vbool1);
// Message to 'vdouble14_marginal' from Variable factor
this.vdouble14_marginal_F = VariableOp.MarginalAverageConditional <Beta>(vdouble14_use_B, vBeta0, this.vdouble14_marginal_F);
this.Changed_vbool1_isDone = true;
}
public void BernoulliFromBetaOpTest()
{
Assert.True(System.Math.Abs(BernoulliFromBetaOp.LogEvidenceRatio(new Bernoulli(3e-5), Beta.PointMass(1)) - 0) < 1e-10);
using (TestUtils.TemporarilyAllowBetaImproperSums)
{
Beta probTrueDist = new Beta(3, 2);
Bernoulli sampleDist = new Bernoulli();
Assert.True(new Beta(1, 1).MaxDiff(BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist, probTrueDist)) < 1e-4);
sampleDist = Bernoulli.PointMass(true);
Assert.True(new Beta(2, 1).MaxDiff(BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist, probTrueDist)) < 1e-4);
sampleDist = Bernoulli.PointMass(false);
Assert.True(new Beta(1, 2).MaxDiff(BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist, probTrueDist)) < 1e-4);
sampleDist = new Bernoulli(0.9);
Assert.True(new Beta(1.724, 0.9598).MaxDiff(BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist, probTrueDist)) < 1e-3);
Assert.Throws <ImproperMessageException>(() =>
{
BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist, new Beta(1, -2));
});
}
}
public void SparseBernoulliFromBetaFactor()
{
var calcSuffix = ": calculation differs between sparse and dense";
var sparsitySuffix = ": result is not sparse as expected";
var calcErrMsg = "";
var sparsityErrMsg = "";
var tolerance = 1e-10;
Rand.Restart(12347);
int listSize = 50;
var sparseProbTrueDist = SparseBetaList.Constant(listSize, new Beta(1, 2));
sparseProbTrueDist[3] = new Beta(4, 5);
sparseProbTrueDist[6] = new Beta(7, 8);
var probTrueDist = sparseProbTrueDist.ToArray();
var sparseProbTruePoint = SparseList <double> .Constant(listSize, 0.1);
sparseProbTruePoint[3] = 0.7;
sparseProbTruePoint[6] = 0.8;
var probTruePoint = sparseProbTruePoint.ToArray();
var sparseSampleDist = SparseBernoulliList.Constant(listSize, new Bernoulli(0.1));
sparseSampleDist[3] = new Bernoulli(0.8);
sparseSampleDist[9] = new Bernoulli(0.9);
var sampleDist = sparseSampleDist.ToArray();
var sparseSamplePoint = SparseList <bool> .Constant(listSize, false);
sparseSamplePoint[3] = true;
sparseSamplePoint[9] = true;
var samplePoint = sparseSamplePoint.ToArray();
var toSparseSampleDist = SparseBernoulliList.Constant(listSize, new Bernoulli(0.1));
toSparseSampleDist[3] = new Bernoulli(0.4);
toSparseSampleDist[4] = new Bernoulli(0.8);
var toSampleDist = toSparseSampleDist.ToArray();
// ---------------------------
// Check average log factor
// ---------------------------
calcErrMsg = "Average log factor" + calcSuffix;
// Dist, dist
var sparseAvgLog = SparseBernoulliFromBetaOp.AverageLogFactor(sparseSampleDist, sparseProbTrueDist);
var avgLog = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.AverageLogFactor(sampleDist[i], probTrueDist[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Dist, point
sparseAvgLog = SparseBernoulliFromBetaOp.AverageLogFactor(sparseSampleDist, sparseProbTruePoint);
avgLog = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.AverageLogFactor(sampleDist[i], probTruePoint[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Point, dist
sparseAvgLog = SparseBernoulliFromBetaOp.AverageLogFactor(sparseSamplePoint, sparseProbTrueDist);
avgLog = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.AverageLogFactor(samplePoint[i], probTrueDist[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// Point, point
sparseAvgLog = SparseBernoulliFromBetaOp.AverageLogFactor(sparseSamplePoint, sparseProbTruePoint);
avgLog = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.AverageLogFactor(samplePoint[i], probTruePoint[i])).Sum();
TAssert.True(System.Math.Abs(avgLog - sparseAvgLog) < tolerance, calcErrMsg);
// ---------------------------
// Check log average factor
// ---------------------------
calcErrMsg = "Log average factor" + calcSuffix;
var sparseLogAvg = SparseBernoulliFromBetaOp.LogAverageFactor(sparseSampleDist, toSparseSampleDist);
var logAvg = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogAverageFactor(sampleDist[i], toSampleDist[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
sparseLogAvg = SparseBernoulliFromBetaOp.LogAverageFactor(sparseSamplePoint, sparseProbTrueDist);
logAvg = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogAverageFactor(samplePoint[i], probTrueDist[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
sparseLogAvg = SparseBernoulliFromBetaOp.LogAverageFactor(sparseSamplePoint, sparseProbTruePoint);
logAvg = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogAverageFactor(samplePoint[i], probTruePoint[i])).Sum();
TAssert.True(System.Math.Abs(logAvg - sparseLogAvg) < tolerance, calcErrMsg);
// ---------------------------
// Check log evidence ratio
// ---------------------------
calcErrMsg = "Log evidence ratio" + calcSuffix;
// Dist, dist
var sparseEvidRat = SparseBernoulliFromBetaOp.LogEvidenceRatio(sparseSampleDist, sparseProbTrueDist);
var evidRat = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogEvidenceRatio(sampleDist[i], probTrueDist[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Dist, point
sparseEvidRat = SparseBernoulliFromBetaOp.LogEvidenceRatio(sparseSampleDist, sparseProbTruePoint);
evidRat = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogEvidenceRatio(sampleDist[i], probTruePoint[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Point, dist
sparseEvidRat = SparseBernoulliFromBetaOp.LogEvidenceRatio(sparseSamplePoint, sparseProbTrueDist);
evidRat = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogEvidenceRatio(samplePoint[i], probTrueDist[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// Point, point
sparseEvidRat = SparseBernoulliFromBetaOp.LogEvidenceRatio(sparseSamplePoint, sparseProbTruePoint);
evidRat = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.LogEvidenceRatio(samplePoint[i], probTruePoint[i])).Sum();
TAssert.True(System.Math.Abs(evidRat - sparseEvidRat) < tolerance, calcErrMsg);
// ---------------------------
// Check SampleConditional
// ---------------------------
calcErrMsg = "SampleConditional" + calcSuffix;
sparsityErrMsg = "SampleConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseSampleConditional = SparseBernoulliList.Constant(listSize, new Bernoulli(0.5));
sparseSampleConditional = SparseBernoulliFromBetaOp.SampleConditional(sparseProbTruePoint, sparseSampleConditional);
var sampleConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.SampleConditional(probTruePoint[i]));
TAssert.True(2 == sparseSampleConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleConditional.MaxDiff(sampleConditional) < tolerance, calcErrMsg);
// ---------------------------
// Check SampleAverageConditional
// ---------------------------
calcErrMsg = "SampleAverageConditional" + calcSuffix;
sparsityErrMsg = "SampleAverageConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseSampleAvgConditional = SparseBernoulliList.Constant(listSize, new Bernoulli(0.5));
sparseSampleAvgConditional = SparseBernoulliFromBetaOp.SampleAverageConditional(sparseProbTrueDist, sparseSampleAvgConditional);
var sampleAvgConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.SampleAverageConditional(probTrueDist[i]));
TAssert.True(2 == sparseSampleAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgConditional.MaxDiff(sampleAvgConditional) < tolerance, calcErrMsg);
sparseSampleAvgConditional = SparseBernoulliList.Constant(listSize, new Bernoulli(0.5));
sparseSampleAvgConditional = SparseBernoulliFromBetaOp.SampleAverageConditional(sparseProbTruePoint, sparseSampleAvgConditional);
sampleAvgConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.SampleAverageConditional(probTruePoint[i]));
TAssert.True(2 == sparseSampleAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgConditional.MaxDiff(sampleAvgConditional) < tolerance, calcErrMsg);
// ---------------------------
// Check ProbTrueConditional
// ---------------------------
calcErrMsg = "ProbTrueConditional" + calcSuffix;
sparsityErrMsg = "ProbTrueConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseProbTrueConditional = SparseBetaList.Constant(listSize, new Beta(1.1, 2.2));
sparseProbTrueConditional = SparseBernoulliFromBetaOp.ProbTrueConditional(sparseSamplePoint, sparseProbTrueConditional);
var probTrueConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.ProbTrueConditional(samplePoint[i]));
TAssert.True(2 == sparseProbTrueConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseProbTrueConditional.MaxDiff(probTrueConditional) < tolerance, calcErrMsg);
// ---------------------------
// Check ProbTrueAverageConditional
// ---------------------------
calcErrMsg = "ProbTrueAverageConditional" + calcSuffix;
sparsityErrMsg = "ProbTrueAverageConditional" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseProbTrueAvgConditional = SparseBetaList.Constant(listSize, new Beta(1.1, 2.2));
sparseProbTrueAvgConditional = SparseBernoulliFromBetaOp.ProbTrueAverageConditional(sparseSampleDist, sparseProbTrueDist, sparseProbTrueAvgConditional);
var probTrueAvgConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.ProbTrueAverageConditional(sampleDist[i], probTrueDist[i]));
TAssert.True(2 == sparseProbTrueAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseProbTrueAvgConditional.MaxDiff(probTrueAvgConditional) < tolerance, calcErrMsg);
sparseProbTrueAvgConditional = SparseBetaList.Constant(listSize, new Beta(1.1, 2.2));
sparseProbTrueAvgConditional = SparseBernoulliFromBetaOp.ProbTrueAverageConditional(sparseSamplePoint, sparseProbTrueAvgConditional);
probTrueAvgConditional = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.ProbTrueAverageConditional(samplePoint[i]));
TAssert.True(2 == sparseProbTrueAvgConditional.SparseCount, sparsityErrMsg);
TAssert.True(sparseProbTrueAvgConditional.MaxDiff(probTrueAvgConditional) < tolerance, calcErrMsg);
// ---------------------------
// Check SampleAverageLogarithm
// ---------------------------
calcErrMsg = "SampleAverageLogarithm" + calcSuffix;
sparsityErrMsg = "SampleAverageLogarithm" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseSampleAvgLogarithm = SparseBernoulliList.Constant(listSize, new Bernoulli(0.5));
sparseSampleAvgLogarithm = SparseBernoulliFromBetaOp.SampleAverageLogarithm(sparseProbTrueDist, sparseSampleAvgLogarithm);
var sampleAvgLogarithm = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.SampleAverageLogarithm(probTrueDist[i]));
TAssert.True(2 == sparseSampleAvgLogarithm.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgLogarithm.MaxDiff(sampleAvgLogarithm) < tolerance, calcErrMsg);
sparseSampleAvgLogarithm = SparseBernoulliList.Constant(listSize, new Bernoulli(0.5));
sparseSampleAvgLogarithm = SparseBernoulliFromBetaOp.SampleAverageLogarithm(sparseProbTruePoint, sparseSampleAvgLogarithm);
sampleAvgLogarithm = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.SampleAverageLogarithm(probTruePoint[i]));
TAssert.True(2 == sparseSampleAvgLogarithm.SparseCount, sparsityErrMsg);
TAssert.True(sparseSampleAvgLogarithm.MaxDiff(sampleAvgLogarithm) < tolerance, calcErrMsg);
// ---------------------------
// Check ProbTrueAverageLogarithm
// ---------------------------
calcErrMsg = "ProbTrueAverageLogarithm" + calcSuffix;
sparsityErrMsg = "ProbTrueAverageLogarithm" + sparsitySuffix;
// Use different common value to ensure this gets properly set
var sparseProbTrueAvgLogarithm = SparseBetaList.Constant(listSize, new Beta(1.1, 2.2));
sparseProbTrueAvgLogarithm = SparseBernoulliFromBetaOp.ProbTrueAverageLogarithm(sparseSampleDist, sparseProbTrueAvgLogarithm);
var probTrueAvgLogarithm = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.ProbTrueAverageLogarithm(sampleDist[i]));
TAssert.True(2 == sparseProbTrueAvgLogarithm.SparseCount, sparsityErrMsg);
TAssert.True(sparseProbTrueAvgLogarithm.MaxDiff(probTrueAvgLogarithm) < tolerance, calcErrMsg);
sparseProbTrueAvgLogarithm = SparseBetaList.Constant(listSize, new Beta(1.1, 2.2));
sparseProbTrueAvgLogarithm = SparseBernoulliFromBetaOp.ProbTrueAverageLogarithm(sparseSamplePoint, sparseProbTrueAvgLogarithm);
probTrueAvgLogarithm = Util.ArrayInit(listSize, i => BernoulliFromBetaOp.ProbTrueAverageLogarithm(samplePoint[i]));
TAssert.True(2 == sparseProbTrueAvgLogarithm.SparseCount, sparsityErrMsg);
TAssert.True(sparseProbTrueAvgLogarithm.MaxDiff(probTrueAvgLogarithm) < tolerance, calcErrMsg);
}
