Default device for the machine (typically a single cpu core)

r""" Adaptive Best-Subset Selection(ABESS) algorithm for linear regression. Parameters ---------- splicing_type: {0, 1}, optional, de

---------- float ----------

---------- float ----------

r""" Adaptive Best-Subset Selection (ABESS) algorithm for logistic regression. Parameters ---------- splicing_type: {0, 1}, optional,

r""" Generate a dataset with single response. Parameters ---------- n: int The number of observations. p: int The

---------- float ----------

---------- double ----------

---------- double ----------

---------- float ----------

\ingroup Operators Placeholder for the B-operator when \f$B = I\f$.

r""" Adaptive Best-Subset Selection(ABESS) algorithm for principal component analysis. Parameters ---------- support_size : array

Sparse Ising model estimation via SLIDE (Sparse pairwise Logistic regression for Ising model via best-subset sElection). Fits one Logist

Callable step function. .. math:: f(z) = a * y_i + b, x_i \\leq z < x_{i + 1} Parameters ---------- x : ndarray, sh

r""" Adaptive Best-Subset Selection (ABESS) algorithm for Cox proportional hazards model. Parameters ---------- splicing_type: {0

r""" Adaptive Best-Subset Selection(ABESS) algorithm for Gamma regression. Parameters ---------- splicing_type: {0, 1}, optional, def

r""" Adaptive Best-Subset Selection(ABESS) algorithm for multitasklearning. Parameters ---------- splicing_type: {0, 1}, optional, de

r""" Adaptive Best-Subset Selection(ABESS) algorithm for Poisson regression. Parameters ---------- splicing_type: {0, 1}, optional, d

r""" Adaptive Best-Subset Selection(ABESS) algorithm for robust principal component analysis. Parameters ---------- support_size

Make our own __half definition that is similar to CUDA's.

///////////////////////////////////////////////////////////////////// Exceptions

r""" Generate a dataset with multi-responses. Parameters ---------- n: int, optional, default=100 The number of observations.

r"""Breslow's estimator of the cumulative hazard function. Attributes ---------- cum_baseline_hazard_ : :class:`sksurv.functions.StepFunct

The type used to identify a dense storage. */

r""" Adaptive Best-Subset Selection(ABESS) algorithm for multiclassification problem. Parameters ---------- splicing_type: {0, 1}

The type used to identify a general sparse storage. */

\internal * \brief Template functor to compute the and of two booleans * * \sa class CwiseBinaryOp, ArrayBase::operator&& */

\internal * \brief Template functor to compute the or of two booleans * * \sa class CwiseBinaryOp, ArrayBase::operator|| */

\internal * \brief Template functor to compute the xor of two booleans * * \sa class CwiseBinaryOp, ArrayBase::operator^ */

\ingroup enums * Used as template parameter in DenseCoeffBase and MapBase to indicate * which accessors should be provided. */

\internal \ingroup enums * Enum used in experimental parallel implementation. */

\ingroup enums * Enum for indicating whether a buffer is aligned or not. */

\internal \ingroup enums * Used in AmbiVector. */