public ActionResult <IEnumerable <float> > GetPrediction([FromQuery, Required] float?sepalLength,
[FromQuery, Required] float?sepalWidth,
[FromQuery, Required] float?petalLength,
[FromQuery, Required] float?petalWidth)
{
var observation = new IrisObservation
{
SepalLength = sepalLength.Value,
SepalWidth = sepalWidth.Value,
PetalLength = petalLength.Value,
PetalWidth = petalWidth.Value
};
var prediction = this.model.Predict(observation);
return(prediction.Distances);
}
public void TestEP_Q_KMeansEntryPointAPI_06()
{
var iris = FileHelper.GetTestFile("iris.txt");
using (var env = new ConsoleEnvironment())
{
var reader = new TextLoader(env,
new TextLoader.Arguments()
{
Separator = "\t",
HasHeader = true,
Column = new[] {
new TextLoader.Column("Label", DataKind.R4, 0),
new TextLoader.Column("Sepal_length", DataKind.R4, 1),
new TextLoader.Column("Sepal_width", DataKind.R4, 2),
new TextLoader.Column("Petal_length", DataKind.R4, 3),
new TextLoader.Column("Petal_width", DataKind.R4, 4),
}
});
var pipeline = new ColumnConcatenatingEstimator(env, "Features", "Sepal_length", "Sepal_width", "Petal_length", "Petal_width")
.Append(new KMeansPlusPlusTrainer(env, "Features", clustersCount: 3));
IDataView trainingDataView = reader.Read(new MultiFileSource(iris));
var model = pipeline.Fit(trainingDataView);
var obs = new IrisObservation()
{
Sepal_length = 3.3f,
Sepal_width = 1.6f,
Petal_length = 0.2f,
Petal_width = 5.1f,
};
var prediction = model.MakePredictionFunction <IrisObservation, IrisPrediction>(env).Predict(obs);
Assert.IsTrue(prediction.PredictedLabel != 0);
var df = Scikit.ML.DataManipulation.DataFrameIO.ReadCsv(iris, sep: '\t', dtypes: new ColumnType[] { NumberType.R4 });
var prediction2 = model.MakePredictionFunctionDataFrame(env, df.Schema);
var df2 = Scikit.ML.DataManipulation.DataFrameIO.ReadCsv(iris, sep: '\t', dtypes: new ColumnType[] { NumberType.R4 });
var df3 = prediction2.Predict(df2);
Assert.AreEqual(df.Shape[0], df3.Shape[0]);
}
}
public void TestEP_Q_KMeansEntryPointAPI_04()
{
var iris = FileHelper.GetTestFile("iris.txt");
var pipeline = new Legacy.LearningPipeline();
pipeline.Add(new Legacy.Data.TextLoader(iris).CreateFrom <IrisObservation>(separator: '\t', useHeader: true));
pipeline.Add(new Legacy.Transforms.ColumnConcatenator("Features", "Sepal_length", "Sepal_width"));
pipeline.Add(new Legacy.Trainers.KMeansPlusPlusClusterer());
var model = pipeline.Train <IrisObservation, IrisPrediction>();
var obs = new IrisObservation()
{
Sepal_length = 3.3f,
Sepal_width = 1.6f,
Petal_length = 0.2f,
Petal_width = 5.1f,
};
var predictions = model.Predict(obs);
Assert.IsTrue(predictions.PredictedLabel != 0);
}
