def __init__(self, fit_intercept: bool = True, threshold: float = 1,
n_threads: int = 1):
ModelFirstOrder.__init__(self)
ModelGeneralizedLinear.__init__(self, fit_intercept)
ModelLipschitz.__init__(self)
self.n_threads = n_threads
self.threshold = threshold
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, 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 __init__(self, fit_intercept: bool = True, smoothness: float = 1.,
n_threads: int = 1):
ModelFirstOrder.__init__(self)
ModelGeneralizedLinear.__init__(self, fit_intercept)
ModelLipschitz.__init__(self)
self.n_threads = n_threads
self.smoothness = smoothness
def __init__(self):
ModelFirstOrder.__init__(self)
self.features = None
self.times = None
self.censoring = None
self.n_samples = None
self.n_features = None
self.n_failures = None
self.censoring_rate = None
self._model = None
def __init__(self, n_intervals: int, n_lags: np.array):
ModelFirstOrder.__init__(self)
ModelLipschitz.__init__(self)
self.n_intervals = n_intervals
self.n_features = len(n_lags)
self.n_lags = n_lags
for n_l in n_lags:
if n_l >= n_intervals:
raise ValueError("n_lags should be < n_intervals")
self.labels = None
self.features = None
self.censoring = None
self.n_cases = None
def __init__(self, n_intervals: int, n_lags: int):
if n_lags >= n_intervals:
raise ValueError("n_lags should be < n_intervals")
ModelFirstOrder.__init__(self)
ModelLipschitz.__init__(self)
self.n_lags = n_lags
self.n_intervals = n_intervals
self.labels = None
self.features = None
self.censoring = None
self.n_features = None
self.n_samples = None
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
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
def _as_dict(self):
dd = ModelFirstOrder._as_dict(self)
del dd["features"]
del dd["times"]
del dd["censoring"]
return dd
def __init__(self, fit_intercept: bool = True, n_threads: int = 1):
ModelFirstOrder.__init__(self)
ModelGeneralizedLinear.__init__(self, fit_intercept)
self.n_threads = n_threads
