public void IndexOfMaximumFastTest()
{
int n = 5;
Range item = new Range(n).Named("item");
var priors = Variable <Gaussian> .Array(item);
priors.ObservedValue = Util.ArrayInit(n, i => Gaussian.FromMeanAndVariance(i * 0.5, i));
var x = Variable.Array <double>(item).Named("x");
x[item] = Variable <double> .Random(priors[item]);
var y = Variable <int> .Factor(MMath.IndexOfMaximumDouble, x);
InferenceEngine engine = new InferenceEngine();
engine.ShowProgress = false;
string format = "f4";
var yActual = engine.Infer <Discrete>(y);
Console.WriteLine("Quadratic: {0}", yActual.ToString(format));
// Monte Carlo estimate
Rand.Restart(0);
DiscreteEstimator est = new DiscreteEstimator(n);
for (int iter = 0; iter < 100000; iter++)
{
double[] samples = Util.ArrayInit(n, i => priors.ObservedValue[i].Sample());
int argmax = MMath.IndexOfMaximumDouble(samples);
est.Add(argmax);
}
var yExpected = est.GetDistribution(Discrete.Uniform(n));
Console.WriteLine("Sampling: {0}", yExpected.ToString(format));
Assert.True(yExpected.MaxDiff(yActual) < 1e-2);
engine.Compiler.GivePriorityTo(typeof(IndexOfMaximumOp_Fast));
yActual = engine.Infer <Discrete>(y);
Console.WriteLine("Linear: {0}", yActual.ToString(format));
Assert.True(yExpected.MaxDiff(yActual) < 1e-2);
bool compareApproximation = false;
if (compareApproximation)
{
var yPost2 = IndexOfMaximumOp_Fast.IndexOfMaximumDoubleAverageConditional(priors.ObservedValue, Discrete.Uniform(n));
Console.WriteLine(yPost2);
var yPost3 = IndexOfMaximumOp_Fast.IndexOfMaximumDoubleAverageConditional2(priors.ObservedValue, Discrete.Uniform(n));
Console.WriteLine(yPost3);
}
}
/// <summary>
/// Estimates rating distribution in the given set of observations.
/// </summary>
/// <param name="observations">The set of observations for which the rating distribution should be estimated.</param>
/// <returns>The estimated rating distribution.</returns>
/// <remarks>The support of the resulting distribution is shifted by -MinRating to handle negative rating values.</remarks>
private Discrete EstimateRatingDistribution(IEnumerable <RatedUserItem> observations)
{
// TODO: this code should be rewritten properly (without offseting ratings) as soon as Discrete supports negative values
var estimator = new DiscreteEstimator(this.starRatingInfo.MaxStarRating - this.starRatingInfo.MinStarRating + 1);
foreach (RatedUserItem observation in observations)
{
estimator.Add(observation.Rating - this.starRatingInfo.MinStarRating);
}
var result = Discrete.Uniform(this.starRatingInfo.MaxStarRating - this.starRatingInfo.MinStarRating + 1);
return(estimator.GetDistribution(result));
}
// Quote an estimator expression
public static IExpression QuoteEstimator(object value)
{
// todo: remove and use Construction attributes
IExpression expr = null;
if (value is BernoulliEstimator)
{
BernoulliEstimator g = (BernoulliEstimator)value;
expr = Builder.NewObject(value.GetType());
}
else if (value is DirichletEstimator)
{
DirichletEstimator g = (DirichletEstimator)value;
expr = Builder.NewObject(value.GetType(), Quote((g.Dimension)));
}
else if (value is DiscreteEstimator)
{
DiscreteEstimator g = (DiscreteEstimator)value;
expr = Builder.NewObject(value.GetType(), Quote((g.Dimension)));
}
else if (value is GammaEstimator)
{
GammaEstimator g = (GammaEstimator)value;
expr = Builder.NewObject(value.GetType());
}
else if (value is GaussianEstimator)
{
GaussianEstimator g = (GaussianEstimator)value;
expr = Builder.NewObject(value.GetType());
}
else if (value is VectorGaussianEstimator)
{
VectorGaussianEstimator g = (VectorGaussianEstimator)value;
expr = Builder.NewObject(value.GetType(), Quote(g.Dimension));
}
else if (value is WishartEstimator)
{
WishartEstimator g = (WishartEstimator)value;
expr = Builder.NewObject(value.GetType(), Quote(g.Dimension));
}
return(expr);
}