public float[] PredictRaw(float[][] data)
{
var test = new DMatrix(data);
return(booster.Predict(test));
}
//public XGBClassifier(IDictionary<string, object> p_parameters)
//{
// parameters = p_parameters;
//}
/// <summary>
/// Fit the gradient boosting model
/// </summary>
/// <param name="data">
/// Feature matrix
/// </param>
/// <param name="labels">
/// Labels
/// </param>
public void Fit(float[][] data, float[] labels)
{
var train = new DMatrix(data, labels);
booster = Train(parameters, train, ((int)parameters["n_estimators"]));
}
/// <summary>
/// Predict using the gradient boosted model
/// </summary>
/// <param name="data">
/// Feature matrix to do predicitons on
/// </param>
/// <returns>
/// Predictions
/// </returns>
public float[] Predict(float[][] data)
{
var test = new DMatrix(data);
return(booster.Predict(test).Select(v => v > 0.5f ? 1f : 0f).ToArray());
}
public void Init(float[][] data, float[] labels)
{
XGBTrain = new DMatrix(data, labels);
booster = new Booster(XGBTrain);
booster.SetParametersGeneric(parameters);
}