/// <summary>
/// Creates a new IndependencyCalculator
/// </summary>
/// <param name="probCalculator">Provider for probability distributions</param>
/// <param name="tolerance">Tolerance level for double calculations</param>
/// <param name="config">BayesianLearningConfiguration object for optional settings</param>
public IndependencyCalculator(IProbabilityDistributionCalculator probCalculator, double tolerance, BayesianLearningConfiguration?config = null)
{
_config = config ?? BayesianLearningConfiguration.Default;
_probCalculator = probCalculator;
_tolerance = tolerance;
}
/// <summary>
/// Creates a Bayesian Network from a given DagPattern which has to be a DAG and calculates the conditional distributions according to the DAG structure.
/// </summary>
/// <exception cref="ArgumentException">Thrown when the given DagPattern is no DAG</exception>
public static BayesianNetwork FromDagPattern(DagPattern <RandomVariable> dag, IProbabilityDistributionCalculator calc)
{
if (!dag.IsDag())
{
throw new ArgumentException("BayesianNetworks need a DAG, but the given DAG was actually a DAG pattern!");
}
var network = new BayesianNetwork(dag);
foreach (var rvar in dag.Nodes)
{
var parents = dag.GetParents(rvar).ToList();
var distribution = calc.CalculateConditionalProbabilityDistribution(rvar, parents);
network.Distributions[rvar] = new ProbabilityDistribution(rvar, parents, distribution);
}
return(network);
}
