def fit(self, features, labels): """Set the data into the model object Parameters ---------- features : {`numpy.ndarray`, `scipy.sparse.csr_matrix`}, shape=(n_samples, n_features) The features matrix, either dense or sparse labels : `numpy.ndarray`, shape=(n_samples,) The labels vector Returns ------- output : `ModelHuber` The current instance with given data """ ModelFirstOrder.fit(self, features, labels) ModelGeneralizedLinear.fit(self, features, labels) ModelLipschitz.fit(self, features, labels) self._set("_model", _ModelHuber(self.features, self.labels, self.fit_intercept, self.threshold, self.n_threads)) return self
def fit(self, features, labels): """Set the data into the model object Parameters ---------- features : {`numpy.ndarray`, `scipy.sparse.csr_matrix`}, shape=(n_samples, n_features) The features matrix, either dense or sparse labels : `numpy.ndarray`, shape=(n_samples,) The labels vector Returns ------- output : `ModelPoisReg` The current instance with given data """ ModelFirstOrder.fit(self, features, labels) ModelGeneralizedLinear.fit(self, features, labels) self._set("_model", self._build_cpp_model(features.dtype)) return self