MyFirstMLApplication

This is my first Machine Learning application with Microsoft.ML

Compiling and run

dotnet build
dotnet run

Machine Learning Codes

using Microsoft.ML;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;
using System;

namespace myApp
{
  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<IrisData>(dataPath, 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}");
      }
  }
}
  ```