public IrisPrediction Predict(double sl, double sw, double pl, double pw)
{
var obs = new IrisObservation()
{
Sepal_length = (float)sl,
Sepal_width = (float)sw,
Petal_length = (float)pl,
Petal_width = (float)pw,
};
return(_fct.Predict(obs));
}
public void Train(string dest)
{
using (var env = new ConsoleEnvironment(verbose: false))
{
var args = new TextLoader.Arguments()
{
Separator = ",",
HasHeader = true,
Column = new TextLoader.Column[] {
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 reader = new TextLoader(env, args);
var concat = new ColumnConcatenatingEstimator(env,
"Features", "Sepal_length",
"Sepal_width", "Petal_length", "Petal_width");
var km = new MulticlassLogisticRegression(env, "Label", "Features");
var pipeline = concat.Append(km);
IDataView trainingDataView = reader.Read(new MultiFileSource(_dataset));
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,
};
_fct = model.MakePredictionFunction <IrisObservation, IrisPrediction>(env);
using (var stdest = File.OpenWrite(dest))
model.SaveTo(env, stdest);
}
}