numl.Learner.GenerateModel C# (CSharp) Method

        private static LearningModel GenerateModel(IGenerator generator, Matrix x, Vector y, IEnumerable<object> examples, double trainingPct, int total)
        {
            var descriptor = generator.Descriptor;
            //var total = examples.Count();
            var trainingCount = (int)System.Math.Floor(total * trainingPct);

            // 100 - trainingPercentage for testing
            var testingSlice = GetTestPoints(total - trainingCount, total).ToArray();

            // trainingPercentage for training
            var trainingSlice = GetTrainingPoints(testingSlice, total).ToArray();

            // training
            var x_t = x.Slice(trainingSlice);
            var y_t = y.Slice(trainingSlice);

            // generate model
            var model = generator.Generate(x_t, y_t);
            model.Descriptor = descriptor;

            Score score = new Score();

            if (testingSlice.Count() > 0)
            {
                // testing
                object[] test = GetTestExamples(testingSlice, examples);
                Vector y_pred = new Vector(test.Length);
                Vector y_test = descriptor.ToExamples(test).Item2;

                bool isBinary = y_test.IsBinary();
                if (isBinary)
                    y_test = y_test.ToBinary(f => f == 1d, 1.0, 0.0);

                for (int j = 0; j < test.Length; j++)
                {
                    // items under test
                    object o = test[j];

                    // make prediction
                    var features = descriptor.Convert(o, false).ToVector();
                    // --- temp changes ---
                    double val = model.Predict(features);
                    var pred = descriptor.Label.Convert(val);

                    var truth = Ject.Get(o, descriptor.Label.Name);

                    if (truth.Equals(pred))
                        y_pred[j] = y_test[j];
                    else
                        y_pred[j] = (isBinary ? (y_test[j] >= 1d ? 0d : 1d) : val);
                }

                // score predictions
                score = Score.ScorePredictions(y_pred, y_test);
            }

            return new LearningModel { Generator = generator, Model = model, Score = score };
        }