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 __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 __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 __init__(self, fit_intercept: bool = True, n_threads: int = 1):
ModelFirstOrder.__init__(self)
ModelGeneralizedLinear.__init__(self, fit_intercept)
self.n_threads = n_threads