r""" Calculate the inputs that will be used by the optimization method when we select the input model='Classic'. Parameters

* Compute the symbolic and numeric factorization of the input sparse matrix. * The input matrix should be in column-major storage. */

template<typename _Scalar>

\returns the dimension in which the box holds */

Replace symbolic last/end "keywords" by their true runtime value

Finds and replaces a set of substrings in a string.

Given an \a index of a node, on exit, \a outVBegin and \a outVEnd range over the indices of the volume children of the node * and \a outOBegin and

\returns the index of the root of the hierarchy */

\returns the image of the matrix, also called its column-space. The columns of the returned matrix * will form a basis of the image (column-spac

\returns true if the matrix is negative (semidefinite) */

\returns true if the matrix is positive (semidefinite) */

\returns the kernel of the matrix, also called its null-space. The columns of the returned matrix * will form a basis of the kernel. *

\returns an expression of the matrix Q as products of sparse Householder reflectors. * The common usage of this function is to apply it to a dense

\returns an expression of the matrix U, * The only operation available with this expression is the triangular solve * \code * y = b;

r""" Calculate the expected returns vector using the selected method. Parameters ---------- X : DataFrame of shape (n_samples, n_asse

\returns the size of the storage major dimension, * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */

\returns the outer-stride of the underlying dense matrix * \sa DenseCoeffsBase::outerStride() */

WARNING: This function is synchronous and will block the calling thread. Synchronous parallelFor executes f with [0, n) arguments in parallel and wai

\returns an estimate of the reciprocal condition number of the matrix of * which \c *this is the Cholesky decomposition. */

returns a reverse iterator to the row (resp. column) before the first row (resp. column) of the nested expression * \sa begin(), cend() */

r""" This function help us to round values to values close or away from zero. Parameters ---------- data : np.ndarray, pd.Series or p

\returns the rotation angle in [-pi,pi] */