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