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}");
}
