Snippets.txt

1 Create Pipeline
            var pipeline = new LearningPipeline();

2 Load Data
            pipeline.Add(new TextLoader<NotePredictionInput>(
                dataPath, 
                useHeader: true, 
                separator: ","));  

3 Calculate Label
            pipeline.Add(new ColumnCopier(("NoteNumber", "Label")));
            pipeline.Add(new Dictionarizer("Label"));

4 Calculate Features
            pipeline.Add(new ColumnConcatenator("Features",
                "KeySignature",
                "Note0_Present",
                "Note1_Present",
                "Note2_Present",
                "Note3_Present",
                "Note4_Present",
                "Note5_Present",
                "Note6_Present",
                "Note7_Present",
                "Note8_Present",
                "Note9_Present",
                "Note10_Present",
                "Note11_Present"));

5 Add Trainer
            pipeline.Add(new StochasticDualCoordinateAscentClassifier());

6 Prediction Output
            pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
            {
                PredictedLabelColumn = "PredictedLabel"
            });

7 Create Model
            var model = pipeline.Train<NotePredictionInput, PredictedNote>();
            await model.WriteAsync(modelPath);

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1 Load Model

            var model = await PredictionModel
                .ReadAsync<NotePredictionInput, PredictedNote>(modelPath);

2 Test
            var result = model.Predict(input);
            Console.WriteLine($"Predicted {result.NoteNumber}");

----------------------------------------

1 Load Model
            var model = await PredictionModel
                .ReadAsync<NotePredictionInput, PredictedNote>(_modelPath);

2 Predict
                    var feature = BuildFeature(knownNotes);
                    var result = model.Predict(feature);

3 Update Note
                    var newNote = AdjustToMeasureOctave(result.NoteNumber, knownNotes);
                    note.Note = newNote;