/// <summary>
/// Train the specified dataSet and priors for the specified number of iterations.
/// </summary>
/// <param name="dataSet">Data set.</param>
/// <param name="priors">Priors.</param>
/// <param name="numberOfIterations">Number of iterations.</param>
public Marginals Train(DataSet dataSet, Marginals priors, int numberOfIterations = 10)
{
SetObservedVariables(
dataSet.Features,
DistributionArrayHelpers.Copy(priors.WeightMeans),
DistributionArrayHelpers.Copy(priors.WeightPrecisions),
dataSet.Labels);
#if !USE_PRECOMPILED_ALGORITHM
engine.Algorithm.DefaultNumberOfIterations = numberOfIterations;
var posteriorWeights = engine.Infer <Gaussian[][]>(weights);
var posteriorWeightMeans = engine.Infer <Gaussian[]>(weightMeans);
var posteriorWeightPrecisions = engine.Infer <Gamma[]>(weightPrecisions);
#else
algorithm.Execute(numberOfIterations);
var posteriorWeights = algorithm.Marginal <Gaussian[][]>(weights.Name);
var posteriorWeightMeans = algorithm.Marginal <Gaussian[]>(weightMeans.Name);
var posteriorWeightPrecisions = algorithm.Marginal <Gamma[]>(weightPrecisions.Name);
#endif
return(new Marginals {
Weights = posteriorWeights, WeightMeans = posteriorWeightMeans, WeightPrecisions = posteriorWeightPrecisions
});
}
/// <summary>
/// Infers the marginal distribution for the specified variable.
/// </summary>
/// <typeparam name="TReturn">Desired return type which may be a distribution type or an array type if the argument is a VariableArray</typeparam>
/// <param name="var">The variable whose marginal is to be inferred</param>
/// <returns>The marginal distribution (or an approximation to it)</returns>
public TReturn Infer <TReturn>(IVariable var)
{
IGeneratedAlgorithm ca = InferAll(false, var);
return(ca.Marginal <TReturn>(var.Name));
}
/// <summary>
/// Infers the marginal distribution for the specified variable.
/// </summary>
/// <param name="var">The variable whose marginal is to be inferred</param>
/// <returns>The marginal distribution (or an approximation to it)</returns>
public object Infer(IVariable var)
{
IGeneratedAlgorithm ca = InferAll(false, var);
return(ca.Marginal(var.Name));
}