def fit(self, features, labels, censoring=None):
"""Set the data into the model object.
Parameters
----------
features : List[{2d array, csr matrix containing float64
of shape (n_intervals, n_features)}]
The features matrix
labels : List[{1d array, csr matrix of shape (n_intervals,)]
The labels vector
censoring : 1d array of shape (n_cases,)
The censoring vector
Returns
-------
output : `ModelSCCS`
The current instance with given data
"""
ModelFirstOrder.fit(self, features, labels, censoring)
ModelLipschitz.fit(self, features, labels)
self._set(
"_model",
_ModelSCCS(features=self.features,
labels=self.labels,
censoring=self.censoring,
n_lags=self.n_lags))
self.dtype = features[0].dtype
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 : `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
def fit(self, features, labels):
"""Set the data into the model object
Parameters
----------
features : `numpy.ndarray`, shape=(n_samples, n_features)
The features matrix
labels : `numpy.ndarray`, shape=(n_samples,)
The labels vector
Returns
-------
output : `ModelLinRegWithIntercepts`
The current instance with given data
"""
ModelFirstOrder.fit(self, features, labels)
ModelGeneralizedLinearWithIntercepts.fit(self, features, labels)
ModelLipschitz.fit(self, features, labels)
self._set("_model", _ModelLinRegWithIntercepts(self.features,
self.labels,
self.fit_intercept,
self.n_threads))
return self