private void RandBinomial(double p, int n)
{
double meanExpected = p * n;
double varianceExpected = p * (1 - p) * n;
double sum = 0;
double sum2 = 0;
for (int i = 0; i < nsamples; i++)
{
double x = Rand.Binomial(n, p);
sum += x;
sum2 += x * x;
}
double mean = sum / nsamples;
double variance = sum2 / nsamples - mean * mean;
double error = MMath.AbsDiff(meanExpected, mean, 1e-6);
if (error > System.Math.Sqrt(varianceExpected) * 5)
{
Assert.True(false, string.Format("Binomial({0},{1}) mean = {2} should be {3}, error = {4}", p, n, mean, meanExpected, error));
}
error = MMath.AbsDiff(varianceExpected, variance, 1e-6);
if (error > System.Math.Sqrt(varianceExpected) * 5)
{
Assert.True(false, string.Format("Binomial({0},{1}) variance = {2} should be {3}, error = {4}", p, n, variance, varianceExpected, error));
}
}
public int Sample()
{
if (A != 1 || B != 1)
{
throw new NotImplementedException();
}
return(Rand.Binomial(TrialCount, ProbSuccess));
}
public void PoissonExpTest2()
{
Variable <bool> evidence = Variable.Bernoulli(0.5).Named("evidence");
IfBlock block = Variable.If(evidence);
Variable <double> x = Variable.GaussianFromMeanAndVariance(1.2, 3.4).Named("x");
Rand.Restart(12347);
int n = 10;
int N = 10;
int[] data = new int[N];
for (int i = 0; i < N; i++)
{
data[i] = Rand.Binomial(n, 1.0 / (double)n);
}
data = new int[] { 5, 6, 7 };
Range item = new Range(data.Length).Named("item");
VariableArray <double> ex = Variable.Array <double>(item).Named("ex");
ex[item] = Variable.Exp(x).ForEach(item);
VariableArray <int> y = Variable.Array <int>(item).Named("y");
y[item] = Variable.Poisson(ex[item]);
block.CloseBlock();
y.ObservedValue = data;
InferenceEngine ie = new InferenceEngine(new VariationalMessagePassing());
var ca = ie.GetCompiledInferenceAlgorithm(evidence, x);
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) < 1e-10)
{
break;
}
oldLogEvidence = logEvidence1;
}
Gaussian xExpected = new Gaussian(1.755071011884509, 0.055154577283323);
Gaussian xActual = ca.Marginal <Gaussian>(x.NameInGeneratedCode);
Console.WriteLine("x = {0} should be {1}", xActual, xExpected);
Assert.True(xExpected.MaxDiff(xActual) < 1e-3);
}
public static int Binomial(int trialCount, double probSuccess)
{
return(Rand.Binomial(trialCount, probSuccess));
}
public void IndexOfMaximumDiverges()
{
int numberOfFeatures = 10;
int numberOfFolders = 3;
int numEmails = 1000;
var featuresCountsObs = Enumerable.Range(0, numEmails).Select(o => Rand.Binomial(numberOfFeatures, 5.0 / numberOfFeatures)).ToArray();
var featureIndicesObs = featuresCountsObs.Select(o => Rand.Perm(numberOfFeatures).ToList().GetRange(0, o).ToArray()).ToArray();
//var trueFeatureWeights = Enumerable.Range(0, numberOfFolders).Select(p=>Enumerable.Range(0, numberOfFeatures).Select(o => Rand.Normal()).ToArray()).ToArray();
//var folders = featureIndicesObs.Select(fi=>fi.Select(p=>trueFeatureWeights.Select(q=>q[p]).Sum()
// random data for now!
var folders = Enumerable.Range(0, numEmails).Select(o => Rand.Int(numberOfFolders)).ToArray();
Range numberOfFeaturesRange = new Range(numberOfFeatures).Named("NumberOfFeaturesRange");
numberOfFeaturesRange.AddAttribute(new Sequential()); // This requires new build of Infer.NET
// creat a range for the number of classes
Range numberOfClases = new Range(numberOfFolders).Named("NumberOfClassesRange");
// Model the total number of items
var numberOfItems = Variable.New <int>().Named("numberOfItems");
numberOfItems.ObservedValue = numEmails;
Range numberOfItemsRange = new Range(numberOfItems).Named("numberOfItemsRange");
numberOfItemsRange.AddAttribute(new Sequential());
// Model the number features present in each item in each class
var featureCounts = Variable.Array <int>(numberOfItemsRange).Named("featureCounts");
Range featureCountItemRange = new Range(featureCounts[numberOfItemsRange]).Named("featureItemCountRange");
featureCounts.ObservedValue = featuresCountsObs;
// Model the features we observe
var featureIndicies = Variable.Array(Variable.Array <int>(featureCountItemRange), numberOfItemsRange).Named("featureIndicies");
featureIndicies.ObservedValue = featureIndicesObs;
// Setup the priors
var FeatureWeights = Variable.Array(Variable.Array <double>(numberOfFeaturesRange), numberOfClases).Named("FeatureWeights");
FeatureWeights[numberOfClases][numberOfFeaturesRange] = Variable.GaussianFromMeanAndPrecision(0, 1).ForEach(numberOfClases, numberOfFeaturesRange);
// Setup the label value (Folder)
var folderValue = Variable.Array <int>(numberOfItemsRange).Named("folderValue");
folderValue.ObservedValue = folders;
var sparseWeightVector =
Variable.Array(Variable.Array(Variable.Array <double>(featureCountItemRange), numberOfClases), numberOfItemsRange).Named("sparseWeightVector");
;
sparseWeightVector[numberOfItemsRange][numberOfClases] = Variable.Subarray <double>(FeatureWeights[numberOfClases], featureIndicies[numberOfItemsRange]);
var scoresWithNoise = Variable.Array(Variable.Array <double>(numberOfClases), numberOfItemsRange).Named("scoresWithNoise");
scoresWithNoise[numberOfItemsRange][numberOfClases] = Variable.GaussianFromMeanAndVariance(Variable.Sum(sparseWeightVector[numberOfItemsRange][numberOfClases]), 1);
folderValue[numberOfItemsRange] = Variable <int> .Factor(MMath.IndexOfMaximumDouble, scoresWithNoise[numberOfItemsRange]);
folderValue.AddAttribute(new MarginalPrototype(Discrete.Uniform(numberOfClases.SizeAsInt)));
var ie = new InferenceEngine();
Console.WriteLine(ie.Infer(FeatureWeights));
}
