public void TestEP_NearestNeighborsLPBc()
{
using (var env = EnvHelper.NewTestEnvironment(conc: 1))
{
var iris = FileHelper.GetTestFile("iris_binary.txt");
var df = DataFrameIO.ReadCsv(iris, sep: '\t', dtypes: new ColumnType[] { NumberType.R4 });
var importData = df.EPTextLoader(iris, sep: '\t', header: true);
var learningPipeline = new GenericLearningPipeline(conc: 1);
learningPipeline.Add(importData);
learningPipeline.Add(new Legacy.Transforms.ColumnConcatenator("Feat", "Sepal_length", "Sepal_width"));
var node = new Scikit.ML.EntryPoints.NearestNeighborsBinary("Feat", "Label", null);
learningPipeline.Add(node);
var predictor = learningPipeline.Train();
var predictions = predictor.Predict(df);
var dfout = DataFrameIO.ReadView(predictions);
Assert.AreEqual(dfout.Shape, new Tuple <int, int>(150, 9));
}
}
public static void Add(this Microsoft.ML.Runtime.Experiment exp, NearestNeighborsBinary input, NearestNeighborsBinary.Output output)
{
exp.AddEntryPoint(NearestNeighborsBinary.Name, input, output);
}
public static NearestNeighborsBinary.Output Add(this Microsoft.ML.Runtime.Experiment exp, NearestNeighborsBinary input)
{
var output = new NearestNeighborsBinary.Output();
exp.Add(input, output);
return(output);
}
