AIMA.Probability.Bayes.Approx.PriorSample.priorSample C# (CSharp) Méthode

        public Map<RandomVariable, Object> priorSample(BayesianNetwork bn)
        {
            // x <- an event with n elements
            Map<RandomVariable, Object> x = new LinkedHashMap<RandomVariable, Object>();
            // foreach variable X<sub>i</sub> in X<sub>1</sub>,...,X<sub>n</sub> do
            foreach (RandomVariable Xi in bn.getVariablesInTopologicalOrder())
            {
                // x[i] <- a random sample from
                // <b>P</b>(X<sub>i</sub> | parents(X<sub>i</sub>))
                x.Add(Xi, ProbUtil.randomSample(bn.getNode(Xi), x, randomizer));
            }
            // return x
            return x;
        }
    }

        // function REJECTION-SAMPLING(X, e, bn, N) returns an estimate of
        // <b>P</b>(X|e)

        /**
         * The REJECTION-SAMPLING algorithm in Figure 14.14. For answering queries
         * given evidence in a Bayesian Network.
         *
         * @param X
         *            the query variables
         * @param e
         *            observed values for variables E
         * @param bn
         *            a Bayesian network
         * @param Nsamples
         *            the total number of samples to be generated
         * @return an estimate of <b>P</b>(X|e)
         */

        public CategoricalDistribution rejectionSampling(RandomVariable[] X,
                                                         AssignmentProposition[] e, BayesianNetwork bn, int Nsamples)
        {
            // local variables: <b>N</b>, a vector of counts for each value of X,
            // initially zero
            double[] N = new double[ProbUtil
                                    .expectedSizeOfCategoricalDistribution(X)];

            // for j = 1 to N do
            for (int j = 0; j < Nsamples; j++)
            {
                // <b>x</b> <- PRIOR-SAMPLE(bn)
                Map <RandomVariable, Object> x = ps.priorSample(bn);
                // if <b>x</b> is consistent with e then
                if (isConsistent(x, e))
                {
                    // <b>N</b>[x] <- <b>N</b>[x] + 1
                    // where x is the value of X in <b>x</b>
                    N[ProbUtil.indexOf(X, x)] += 1.0;
                }
            }
            // return NORMALIZE(<b>N</b>)
            return(new ProbabilityTable(N, X).normalize());
        }