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