static void Main(string[] args)
{
TextLoader loader = TextLoader.Create(typeof(IrisData));
loader.Separator = ",";
IDataView trainingData = loader.Read("iris-data.txt");
var estimator = new ValueToKeyMappingEstimator("Label")
.Append(new ColumnConcatenatingEstimator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
.Append(new SdcaMultiClassTrainer())
.Append(new KeyToValueMappingEstimator("PredictedLabel"));
var model = estimator.Train <IrisData, IrisPrediction>(trainingData);
IrisData newInput = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
IrisPrediction prediction = model.Predict(newInput);
Console.WriteLine($"Predicted flower type is: {prediction.PredictedLabel}");
}
static void Main(string[] args)
{
TextLoader loader = TextLoader.Create(typeof(IrisData), separator: ",");
IDataView trainingData = loader.Read("iris-data.txt");
var pipeline = new EstimatorChain();
pipeline.Add(new ValueToKeyMappingEstimator(nameof(IrisData.Label)));
pipeline.Add(new ColumnConcatenatingEstimator(
inputColumns: new[]
{
nameof(IrisData.SepalLength),
nameof(IrisData.SepalWidth),
nameof(IrisData.PetalLength),
nameof(IrisData.PetalWidth)
},
outputColumn: DefaultColumnNames.Features));
pipeline.Add(new SdcaMultiClassTrainer(
featureColumn: DefaultColumnNames.Features,
labelColumn: nameof(IrisData.Label),
predictedLabelColumn: nameof(IrisPrediction.PredictedLabel)));
pipeline.Add(new KeyToValueMappingEstimator(nameof(IrisPrediction.PredictedLabel)));
var model = pipeline.Fit(trainingData);
IrisData newInput = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
IrisPrediction prediction = model
.MakePredictionFunction <IrisData, IrisPrediction>()
.Predict(newInput);
Console.WriteLine($"Predicted flower type is: {prediction.PredictedLabel}");
}
static void Main(string[] args)
{
// STEP 2: Create a ML.NET environment
var mlContext = new MLContext();
Random rnd = new Random();
Stopwatch sw = new Stopwatch();
Write("Loading dataset... ");
sw.Start();
// If working in Visual Studio, make sure the 'Copy to Output Directory'
// property of iris-data.txt is set to 'Copy always'
var reader = mlContext.Data.CreateTextLoader <IrisData>(separatorChar: ',', hasHeader: false);
//https://en.m.wikipedia.org/wiki/Iris_flower_data_set
IDataView trainingDataView = reader.Load("iris-data.txt");
// STEP 3: Transform your data and add a learner
// Assign numeric values to text in the "Label" column, because only
// numbers can be processed during model training.
// Add a learning algorithm to the pipeline. e.g.(What type of iris is this?)
// Convert the Label back into original text (after converting to number in step 3)
var pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
.Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
.Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumnName: "Label", featureColumnName: "Features"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
sw.Stop();
WriteLine($"{sw.Elapsed.Seconds*1000 + sw.Elapsed.Milliseconds} ms");
// STEP 4: Train your model based on the data set
Write("Training the model... ");
sw.Restart();
var model = pipeline.Fit(trainingDataView);
sw.Stop();
WriteLine($"{sw.Elapsed.Seconds*1000 + sw.Elapsed.Milliseconds} ms\n");
do
{
// STEP 5: Use your model to make a prediction
// You can change these numbers to test different predictions
var iris = new IrisData()
{
// SepalLength = 2.3f,
// SepalWidth = 1.6f,
// PetalLength = 0.2f,
// PetalWidth = 5.1f,
SepalLength = (float)rnd.NextDouble() * 3.6f + 4.3f,
SepalWidth = (float)rnd.NextDouble() * 2.4f + 2.0f,
PetalLength = (float)rnd.NextDouble() * 5.9f + 1.0f,
PetalWidth = (float)rnd.NextDouble() * 2.4f,
};
WriteLine($"Sepal length: {iris.SepalLength:F1} cm");
WriteLine($"Sepal width: {iris.SepalWidth:F1} cm");
WriteLine($"Petal length: {iris.PetalLength:F1} cm");
WriteLine($"Petal width: {iris.PetalWidth:F1} cm");
var prediction = model.CreatePredictionEngine <IrisData, IrisPrediction>(mlContext).Predict(iris);
WriteLine($"Predicted flower type is: {prediction.PredictedLabels}");
WriteLine("Esc to quit...\n");
} while (ReadKey(true).Key != ConsoleKey.Escape);
}