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
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 : `ModelQuadraticHinge`
The current instance with given data
"""
ModelFirstOrder.fit(self, features, labels)
ModelGeneralizedLinear.fit(self, features, labels)
ModelLipschitz.fit(self, features, labels)
self._set(
"_model",
_ModelModelQuadraticHinge(self.features, self.labels,
self.fit_intercept, self.n_threads))
return self
def __init__(self, fit_intercept: bool = True, n_threads: int = 1):
ModelFirstOrder.__init__(self)
ModelGeneralizedLinearWithIntercepts.__init__(self,
fit_intercept)
ModelLipschitz.__init__(self)
self.n_threads = n_threads