numl.Supervised.NaiveBayes.NaiveBayesGenerator.Generate C# (CSharp) Méthode

        public override IModel Generate(Matrix X, Vector y)
        {
            if (Descriptor == null)
                throw new InvalidOperationException("Cannot build naive bayes model without type knowledge!");

            // create answer probabilities
            if (!Descriptor.Label.Discrete)
                throw new InvalidOperationException("Need to use regression for non-discrete labels!");

            this.Preprocess(X);

            // compute Y probabilities
            Statistic[] statistics = GetLabelStats(y);

            Measure root = new Measure
            {
                Discrete = true,
                Label = Descriptor.Label.Name,
                Probabilities = statistics
            };

            // collect feature ranges
            Measure[] features = GetBaseConditionals(X);

            // compute conditional counts
            for (int i = 0; i < y.Length; i++)
            {
                var stat = statistics.Where(s => s.X.Min == y[i]).First();
                if (stat.Conditionals == null)
                    stat.Conditionals = CloneMeasure(features);

                for (int j = 0; j < X.Cols; j++)
                {
                    var s = stat.Conditionals[j];
                    s.Increment(X[i, j]);
                }
            }

            // normalize into probabilities
            for (int i = 0; i < statistics.Length; i++)
            {
                var cond = statistics[i];
                for (int j = 0; j < cond.Conditionals.Length; j++)
                    cond.Conditionals[j].Normalize();
            }

            // label ids
            LabelIds(root);

            return new NaiveBayesModel
            {
                Descriptor = Descriptor,
                NormalizeFeatures = base.NormalizeFeatures,
                FeatureNormalizer = base.FeatureNormalizer,
                FeatureProperties = base.FeatureProperties,
                Root = root
            };
        }

        public void Tennis_Naive_Bayes_Save_And_Load_Test()
        {
            var data = Tennis.GetData();
            var description = Descriptor.Create<Tennis>();
            var generator = new NaiveBayesGenerator(2);
            var model = generator.Generate(description, data);
            Serialize(model);

            var lmodel = Deserialize<NaiveBayesModel>();
        }