ML.NET 教學課程 1 2 3

ASUS X450J @ Windows 10 x64 + Visual Studio 2017
https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet/get-started/windows
1. Hello ML.NET
1-1 cmd
1-2 dotnet new console -o myApp
1-3 cd myApp
1-4 dotnet add package Microsoft.ML --version 0.3.0
1-6 Program.cs
using System; using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Runtime.Api; using Microsoft.ML.Trainers; using Microsoft.ML.Transforms; namespace mlex1 { class Program { // STEP 1: Define your data structures // IrisData is used to provide training data, and as // input for prediction operations // - First 4 properties are inputs/features used to predict the label // - Label is what you are predicting, and is only set when training public class IrisData { [Column("0")] public float SepalLength; [Column("1")] public float SepalWidth; [Column("2")] public float PetalLength; [Column("3")] public float PetalWidth; [Column("4")] [ColumnName("Label")] public string Label; } // IrisPrediction is the result returned from prediction operations public class IrisPrediction { [ColumnName("PredictedLabel")] public string PredictedLabels; } static void Main(string[] args) { // STEP 2: Create a pipeline and load your data var pipeline = new LearningPipeline(); // If working in Visual Studio, make sure the 'Copy to Output Directory' // property of iris-data.txt is set to 'Copy always' string dataPath = "iris-data.txt"; pipeline.Add(new TextLoader(dataPath).CreateFrom<IrisData>(separator: ',')); // STEP 3: Transform your data // Assign numeric values to text in the "Label" column, because only // numbers can be processed during model training pipeline.Add(new Dictionarizer("Label")); // Puts all features into a vector pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")); // STEP 4: Add learner // Add a learning algorithm to the pipeline. // This is a classification scenario (What type of iris is this?) pipeline.Add(new StochasticDualCoordinateAscentClassifier()); // Convert the Label back into original text (after converting to number in step 3) pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" }); // STEP 5: Train your model based on the data set var model = pipeline.Train<IrisData, IrisPrediction>(); // STEP 6: Use your model to make a prediction // You can change these numbers to test different predictions var prediction = model.Predict(new IrisData() { SepalLength = 3.3f, SepalWidth = 1.6f, PetalLength = 0.2f, PetalWidth = 5.1f, }); Console.WriteLine($"Predicted flower type is: {prediction.PredictedLabels}"); } } }

1-7 Download iris-data.txt

1-8

Ref:https://docs.microsoft.com/zh-tw/dotnet/machine-learning/tutorials/sentiment-analysis

2. 情感分析二元分類案例

https://github.com/dotnet/machinelearning-samples

2-1 download zip
2-2 unzip
2-3 copy datasets folder into project
2-4 run

3. Clustering_iris
3-1 Download (as previous case)
3-2 copy copy datasets folder into project
3-3 run