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); }